davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
mrachilles/ntu60SkeletonPoint	--- license: mit ---
Nadav/pixel_glue_qqp	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 4725063877.25 num_examples: 363846 - name: validation num_bytes: 525056314.25 num_examples: 40430 download_size: 5039025536 dataset_size: 5250120191.5 --- # Dataset Card for "pixel_glue_qqp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
RobbeD/csgo-texture-patterns-1024	--- dataset_info: features: - name: description dtype: string - name: finish_style dtype: string - name: weapon dtype: string - name: skin dtype: string - name: finish_catalog dtype: int64 - name: flavor_text dtype: string - name: mask_image dtype: image - name: ao_image dtype: image - name: conditioning_image dtype: image - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1220768222.0 num_examples: 556 download_size: 629049265 dataset_size: 1220768222.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "csgo-texture-patterns-1024" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/ukm_2000_girlsfrontline	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ukm_2000/UKM-2000/UKM-2000 (Girls' Frontline) This is the dataset of ukm_2000/UKM-2000/UKM-2000 (Girls' Frontline), containing 23 images and their tags. The core tags of this character are `long_hair, pink_hair, breasts, bangs, red_eyes, hat, pink_eyes, very_long_hair, 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 \| 23 \| 31.57 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 23 \| 17.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 51 \| 34.27 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 23 \| 27.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 51 \| 50.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_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/ukm_2000_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, solo, looking_at_viewer, open_jacket, long_sleeves, black_gloves, blush, white_jacket, animal_hood, gun, sitting, smile, white_background, black_leotard, boots, closed_mouth, fingerless_gloves, hood_up, hooded_jacket, sidelocks, simple_background, skindentation, thigh_strap, thighhighs \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| looking_at_viewer, 1girl, black_bikini, solo, hair_bun, navel, pink_headwear, thigh_strap, baseball_cap, black_gloves, fingerless_gloves, see-through, single_thighhigh, stomach, white_shirt, bare_shoulders, collarbone, crop_top, fishnet_thighhighs, simple_background, standing, thighs, white_background, black_choker, cowboy_shot, large_breasts, off-shoulder_shirt, short_sleeves, smile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| open_jacket \| long_sleeves \| black_gloves \| blush \| white_jacket \| animal_hood \| gun \| sitting \| smile \| white_background \| black_leotard \| boots \| closed_mouth \| fingerless_gloves \| hood_up \| hooded_jacket \| sidelocks \| simple_background \| skindentation \| thigh_strap \| thighhighs \| black_bikini \| hair_bun \| navel \| pink_headwear \| baseball_cap \| see-through \| single_thighhigh \| stomach \| white_shirt \| bare_shoulders \| collarbone \| crop_top \| fishnet_thighhighs \| standing \| thighs \| black_choker \| cowboy_shot \| large_breasts \| off-shoulder_shirt \| short_sleeves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------------\|:---------------\|:---------------\|:--------\|:---------------\|:--------------\|:------\|:----------\|:--------\|:-------------------\|:----------------\|:--------\|:---------------\|:--------------------\|:----------\|:----------------\|:------------\|:--------------------\|:----------------\|:--------------\|:-------------\|:---------------\|:-----------\|:--------\|:----------------\|:---------------\|:--------------\|:-------------------\|:----------\|:--------------\|:-----------------\|:-------------\|:-----------\|:---------------------\|:-----------\|:---------\|:---------------\|:--------------\|:----------------\|:---------------------\|:----------------\| \| 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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| X \| \| \| \| \| \| X \| X \| \| \| \| X \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
ElTucuGardella/PaolaX	--- license: unknown ---
GBaker/MedQA-USMLE-4-options-hf	--- license: cc-by-sa-4.0 --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }
tyzhu/squad_qa_wrong_rare_v5_full_no_permute	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 7639879.229800348 num_examples: 4778 - name: validation num_bytes: 349767 num_examples: 300 download_size: 1200683 dataset_size: 7989646.229800348 --- # Dataset Card for "squad_qa_wrong_rare_v5_full_no_permute" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
m-rousseau/oqa-v1	--- license: apache-2.0 ---
Sourabh2/Daily_Conversation_Hinglish	--- dataset_info: features: - name: seed dtype: string - name: question dtype: string - name: question_raw dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1271518 num_examples: 1786 download_size: 258915 dataset_size: 1271518 configs: - config_name: default data_files: - split: train path: data/train-* ---
AdapterOcean/chemistry_dataset_standardized_cluster_1_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: 6207644 num_examples: 5502 download_size: 2530400 dataset_size: 6207644 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "chemistry_dataset_standardized_cluster_1_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
elements-dev/hq_portrait_sdxl_subset	--- dataset_info: features: - name: image dtype: image - name: blip2 dtype: string - name: foreground_canny_edge_image dtype: image splits: - name: train num_bytes: 26844364.0 num_examples: 100 download_size: 25919375 dataset_size: 26844364.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
huggingartists/rex-orange-county	--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/rex-orange-county" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - Homepage: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Repository: [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - Paper: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Size of the generated dataset: 0.116278 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/348ad82a8d34eaff777b6743ca0f2d70.400x400x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/rex-orange-county"> <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">Rex Orange County</div> <a href="https://genius.com/artists/rex-orange-county"> <div style="text-align: center; font-size: 14px;">@rex-orange-county</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/rex-orange-county). ### 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/rex-orange-county") ``` ## 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\| \|------:\|---------:\|---:\| \|66\| -\| -\| '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/rex-orange-county") 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)
irds/lotte_pooled_dev_forum	--- pretty_name: '`lotte/pooled/dev/forum`' viewer: false source_datasets: ['irds/lotte_pooled_dev'] task_categories: - text-retrieval --- # Dataset Card for `lotte/pooled/dev/forum` The `lotte/pooled/dev/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/pooled/dev/forum). # Data This dataset provides: - `queries` (i.e., topics); count=10,097 - `qrels`: (relevance assessments); count=68,685 - For `docs`, use [`irds/lotte_pooled_dev`](https://huggingface.co/datasets/irds/lotte_pooled_dev) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/lotte_pooled_dev_forum', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/lotte_pooled_dev_forum', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```
AarushSah/scibowl-synthetic	--- license: apache-2.0 language: - en tags: - chemistry - biology - earth science - claude - claude 3 opus - synthetic - astronomy - general science - physics pretty_name: S size_categories: - 1K<n<10K --- # Scibowl-synthetic (v0.1) ## Dataset Description Scibowl-synthetic is a dataset of science bowl questions that have been answered by the Claude 3 Opus language model. The dataset is designed to provide a high-quality resource for teaching science concepts to language models. ## Dataset Summary - Repository: https://huggingface.co/datasets/AarushSah/scibowl-synthetic/ - Point of Contact: Aarush Sah ## Dataset Composition The dataset consists of 5,046 text-based examples. Each example includes a science bowl question, the expected answer, the answer provided by Claude, the thought process of Claude (wrapped in `<thinking>` tags), and the final response (wrapped in `<answer>` tags). ## Data Collection Process The science bowl questions were collected by downloading all sample questions from the High School (HS) level. The questions were then parsed using Meta Nougat, cleaned, and processed through the Claude 3 Opus language model. The following system prompt was used to generate the answers: ``` You will answer the question posed by the user step by step, with detailed reasoning explaining how you arrived at that answer. Think before you answer, and explain your thought process. Make sure you are factual and evidence based. Double check your responses. Wrap your thought process in <thinking> tags. Wrap your final response in <answer> tags. Within the answer tags, only put the answer, no reasoning. ``` ## Dataset Structure Each example in the dataset is represented as a JSON object with the following fields: - `question`: The science bowl question. - `expected`: The expected answer to the question. - `answer`: The answer provided by Claude. - `thinking`: Claude's thought process, wrapped in `<thinking>` tags. - `response`: The complete response from Claude, including the thought process and final answer. - `correct`: A boolean value indicating whether Claude's answer matches the expected answer. Note that this value is not fully reliable and may contain false negatives. ## Dataset Splits Currently, there are no predefined splits for the dataset. Users can create their own splits as needed. ## Dataset Use The Scibowl-synthetic dataset can be used for various purposes, such as: - Fine-tuning language models on science-related tasks. - Evaluating the performance of language models on answering science questions. - Studying the reasoning and thought processes of language models. ## Potential Issues and Biases As the answers in the dataset are generated by the Claude 3 Opus language model, they may be subject to the biases and limitations inherent in the model. Users should be aware of these potential biases when using the dataset. Additionally, the `correct` boolean value in the dataset is not fully reliable and may contain false negatives. Users should exercise caution when using this value for evaluation purposes. ## Licensing Information The Scibowl-synthetic dataset is released under the Apache 2.0 license. ## Citation Information If you use this dataset in your research or projects, please cite it as follows: ```bibtex @misc{Scibowl-synthetic, title = {Scibowl-synthetic: A Dataset of Science Bowl Questions Answered by Claude 3 Opus}, author = {Aarush Sah}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/AarushSah/scibowl-synthetic/} } ``` ## Contributions If you have any suggestions, improvements, or additional data to contribute, please open an issue or submit a pull request in the dataset repository.
burglarhobbit/temp-cultura-x	--- license: apache-2.0 dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string - name: source dtype: string - name: text_en_tr dtype: string - name: text_gu_rm dtype: string splits: - name: train num_bytes: 29478 num_examples: 3 download_size: 54682 dataset_size: 29478 configs: - config_name: default data_files: - split: train path: data/train-* ---
davidgaofc/pairwise_setup	--- license: mit dataset_info: features: - name: Question dtype: string - name: SFT dtype: string - name: Base_PPO dtype: string - name: Prima_PPO dtype: string splits: - name: train num_bytes: 914772 num_examples: 1640 download_size: 264717 dataset_size: 914772 configs: - config_name: default data_files: - split: train path: data/train-* ---
zZWipeoutZz/rogue_style	--- license: creativeml-openrail-m --- <h4> Usage </h4> To use this embedding you have to download the file and put it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt add <em style="font-weight:600">art by rogue_style </em> add <b>[ ]</b> around it to reduce its weight. <h4> Included Files </h4> <ul> <li>500 steps <em>Usage: art by rogue_style-500</em></li> <li>3500 steps <em>Usage: art by rogue_style-3500</em></li> <li>6500 steps <em>Usage: art by rogue_style</em> </li> </ul> cheers<br> Wipeout <h4> Example Pictures </h4> <table> <tbody> <tr> <td><img height="100%/" width="100%" src="https://i.imgur.com/JefZ3cA.png"></td> <td><img height="100%/" width="100%" src="https://i.imgur.com/YBJzVIi.png"></td> <td><img height="100%/" width="100%" src="https://i.imgur.com/96iutfu.png"></td> <td><img height="100%/" width="100%" src="https://i.imgur.com/SBKfnc4.png"></td> </tr> </tbody> </table> <h4> prompt comparison </h4> <em> click the image to enlarge</em> <a href="https://i.imgur.com/a6te4zG.png" target="_blank"><img height="50%" width="50%" src="https://i.imgur.com/a6te4zG.png"></a>
davanstrien/ner-test	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: string - name: ner_tags sequence: string - name: tokens sequence: string splits: - name: train num_bytes: 1548186 num_examples: 5216 - name: valid num_bytes: 392764 num_examples: 1304 download_size: 0 dataset_size: 1940950 --- # Dataset Card for "ner-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
edbeeching/gia-dataset-tokenized-debug2	--- dataset_info: config_name: atari-alien features: - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 442153668 num_examples: 335 download_size: 35972017 dataset_size: 442153668 configs: - config_name: atari-alien data_files: - split: test path: atari-alien/test-* --- # Dataset Card for "gia-dataset-tokenized-debug2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
HuggingFaceM4/gradio_dope_data_points_test	Invalid username or password.
CyberHarem/kashin_koji_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kashin Koji/果心居士 (Fate/Grand Order) This is the dataset of Kashin Koji/果心居士 (Fate/Grand Order), containing 34 images and their tags. The core tags of this character are `heterochromia, long_hair, multicolored_hair, red_eyes, white_hair, black_hair, bangs, grey_hair, two-tone_hair, hair_ornament, green_eyes, twintails, very_long_hair, blue_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 34 \| 72.37 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 34 \| 33.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 83 \| 73.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 34 \| 58.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 83 \| 115.31 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kashin_koji_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------\| \| 0 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, looking_at_viewer, parted_lips, split-color_hair, red_gloves \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| parted_lips \| split-color_hair \| red_gloves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------------\|:-------------------\|:-------------\| \| 0 \| 25 \| ![](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 \|
eunbinni/ola_llama2_13B_t1_data	--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 691281335 num_examples: 580812 download_size: 399933748 dataset_size: 691281335 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ola_llama2_13B_t1_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/weser_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of weser/ヴェーザー/威悉 (Azur Lane) This is the dataset of weser/ヴェーザー/威悉 (Azur Lane), containing 28 images and their tags. The core tags of this character are `breasts, red_hair, short_hair, red_eyes, bangs, large_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 \| 28 \| 32.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 28 \| 21.55 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 60 \| 39.18 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 28 \| 30.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 60 \| 51.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/weser_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, looking_at_viewer, solo, bare_shoulders, cleavage, official_alternate_costume, smile, thigh_strap, black_dress, covered_navel \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, jacket, long_sleeves, looking_at_viewer, thigh_boots, necktie, black_footwear, black_thighhighs, choker, dress, high_heel_boots, sitting, smile \| \| 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, bare_shoulders, crop_top, looking_at_viewer, midriff, navel, solo, collarbone, off-shoulder_shirt, open_mouth, simple_background, stomach, white_background, :o, blush, cowboy_shot, jeans, long_sleeves, standing, alternate_costume, belt, black_shirt, blue_pants, cleavage, hair_between_eyes, hand_up, holding, parted_lips \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| bare_shoulders \| cleavage \| official_alternate_costume \| smile \| thigh_strap \| black_dress \| covered_navel \| jacket \| long_sleeves \| thigh_boots \| necktie \| black_footwear \| black_thighhighs \| choker \| dress \| high_heel_boots \| sitting \| crop_top \| midriff \| navel \| collarbone \| off-shoulder_shirt \| open_mouth \| simple_background \| stomach \| white_background \| :o \| blush \| cowboy_shot \| jeans \| standing \| alternate_costume \| belt \| black_shirt \| blue_pants \| hair_between_eyes \| hand_up \| holding \| parted_lips \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-----------------\|:-----------\|:-----------------------------\|:--------\|:--------------\|:--------------\|:----------------\|:---------\|:---------------\|:--------------\|:----------\|:-----------------\|:-------------------\|:---------\|:--------\|:------------------\|:----------\|:-----------\|:----------\|:--------\|:-------------\|:---------------------\|:-------------\|:--------------------\|:----------\|:-------------------\|:-----\|:--------\|:--------------\|:--------\|:-----------\|:--------------------\|:-------\|:--------------\|:-------------\|:--------------------\|:----------\|:----------\|:--------------\| \| 0 \| 12 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \|
carslab/life_coach_athletes	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 1234000 num_examples: 5000 download_size: 15528 dataset_size: 1234000 configs: - config_name: default data_files: - split: train path: data/train-* ---
felipesampaio/gumballjoaovg	--- license: openrail ---
JovialValley/phoneme_totaldataset_4	--- dataset_info: features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: label dtype: string - name: emotion dtype: string - name: emotion_str dtype: string splits: - name: train num_bytes: 164035246.0 num_examples: 390 - name: test num_bytes: 40309237.0 num_examples: 97 download_size: 137553091 dataset_size: 204344483.0 --- # Dataset Card for "phoneme_totaldataset_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/bbf77e22	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1332 dataset_size: 182 --- # Dataset Card for "bbf77e22" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
GalaktischeGurke/invoices_instruct_vf	--- dataset_info: features: - name: ground_truth dtype: string splits: - name: train num_bytes: 911760 num_examples: 501 download_size: 364209 dataset_size: 911760 --- # Dataset Card for "invoices_instruct_vf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
heliosprime/twitter_dataset_1713187618	--- 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: 16759 num_examples: 45 download_size: 17172 dataset_size: 16759 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713187618" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bgspaditya/byt-mal-minpro	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: url dtype: string - name: type dtype: string - name: type_code dtype: int64 splits: - name: train num_bytes: 43302335.10276401 num_examples: 520952 - name: val num_bytes: 5412791.887845501 num_examples: 65119 - name: test num_bytes: 5412875.009390486 num_examples: 65120 download_size: 32733332 dataset_size: 54128002.0 --- # Dataset Card for "byt-mal-minpro" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BangumiBase/akibameidosensou	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Akiba Meido Sensou This is the image base of bangumi Akiba Meido Sensou, we detected 48 characters, 2198 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 87 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 185 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 39 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 70 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 169 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 314 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 29 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 16 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 28 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 24 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 37 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 21 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 60 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 31 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 35 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 158 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 16 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 13 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 9 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 12 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 33 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 85 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 34 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 9 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 10 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 8 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 10 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 23 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 21 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 38 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| 30 \| 6 \| [Download](30/dataset.zip) \| ![preview 1](30/preview_1.png) \| ![preview 2](30/preview_2.png) \| ![preview 3](30/preview_3.png) \| ![preview 4](30/preview_4.png) \| ![preview 5](30/preview_5.png) \| ![preview 6](30/preview_6.png) \| N/A \| N/A \| \| 31 \| 7 \| [Download](31/dataset.zip) \| ![preview 1](31/preview_1.png) \| ![preview 2](31/preview_2.png) \| ![preview 3](31/preview_3.png) \| ![preview 4](31/preview_4.png) \| ![preview 5](31/preview_5.png) \| ![preview 6](31/preview_6.png) \| ![preview 7](31/preview_7.png) \| N/A \| \| 32 \| 9 \| [Download](32/dataset.zip) \| ![preview 1](32/preview_1.png) \| ![preview 2](32/preview_2.png) \| ![preview 3](32/preview_3.png) \| ![preview 4](32/preview_4.png) \| ![preview 5](32/preview_5.png) \| ![preview 6](32/preview_6.png) \| ![preview 7](32/preview_7.png) \| ![preview 8](32/preview_8.png) \| \| 33 \| 16 \| [Download](33/dataset.zip) \| ![preview 1](33/preview_1.png) \| ![preview 2](33/preview_2.png) \| ![preview 3](33/preview_3.png) \| ![preview 4](33/preview_4.png) \| ![preview 5](33/preview_5.png) \| ![preview 6](33/preview_6.png) \| ![preview 7](33/preview_7.png) \| ![preview 8](33/preview_8.png) \| \| 34 \| 11 \| [Download](34/dataset.zip) \| ![preview 1](34/preview_1.png) \| ![preview 2](34/preview_2.png) \| ![preview 3](34/preview_3.png) \| ![preview 4](34/preview_4.png) \| ![preview 5](34/preview_5.png) \| ![preview 6](34/preview_6.png) \| ![preview 7](34/preview_7.png) \| ![preview 8](34/preview_8.png) \| \| 35 \| 15 \| [Download](35/dataset.zip) \| ![preview 1](35/preview_1.png) \| ![preview 2](35/preview_2.png) \| ![preview 3](35/preview_3.png) \| ![preview 4](35/preview_4.png) \| ![preview 5](35/preview_5.png) \| ![preview 6](35/preview_6.png) \| ![preview 7](35/preview_7.png) \| ![preview 8](35/preview_8.png) \| \| 36 \| 7 \| [Download](36/dataset.zip) \| ![preview 1](36/preview_1.png) \| ![preview 2](36/preview_2.png) \| ![preview 3](36/preview_3.png) \| ![preview 4](36/preview_4.png) \| ![preview 5](36/preview_5.png) \| ![preview 6](36/preview_6.png) \| ![preview 7](36/preview_7.png) \| N/A \| \| 37 \| 28 \| [Download](37/dataset.zip) \| ![preview 1](37/preview_1.png) \| ![preview 2](37/preview_2.png) \| ![preview 3](37/preview_3.png) \| ![preview 4](37/preview_4.png) \| ![preview 5](37/preview_5.png) \| ![preview 6](37/preview_6.png) \| ![preview 7](37/preview_7.png) \| ![preview 8](37/preview_8.png) \| \| 38 \| 136 \| [Download](38/dataset.zip) \| ![preview 1](38/preview_1.png) \| ![preview 2](38/preview_2.png) \| ![preview 3](38/preview_3.png) \| ![preview 4](38/preview_4.png) \| ![preview 5](38/preview_5.png) \| ![preview 6](38/preview_6.png) \| ![preview 7](38/preview_7.png) \| ![preview 8](38/preview_8.png) \| \| 39 \| 14 \| [Download](39/dataset.zip) \| ![preview 1](39/preview_1.png) \| ![preview 2](39/preview_2.png) \| ![preview 3](39/preview_3.png) \| ![preview 4](39/preview_4.png) \| ![preview 5](39/preview_5.png) \| ![preview 6](39/preview_6.png) \| ![preview 7](39/preview_7.png) \| ![preview 8](39/preview_8.png) \| \| 40 \| 9 \| [Download](40/dataset.zip) \| ![preview 1](40/preview_1.png) \| ![preview 2](40/preview_2.png) \| ![preview 3](40/preview_3.png) \| ![preview 4](40/preview_4.png) \| ![preview 5](40/preview_5.png) \| ![preview 6](40/preview_6.png) \| ![preview 7](40/preview_7.png) \| ![preview 8](40/preview_8.png) \| \| 41 \| 9 \| [Download](41/dataset.zip) \| ![preview 1](41/preview_1.png) \| ![preview 2](41/preview_2.png) \| ![preview 3](41/preview_3.png) \| ![preview 4](41/preview_4.png) \| ![preview 5](41/preview_5.png) \| ![preview 6](41/preview_6.png) \| ![preview 7](41/preview_7.png) \| ![preview 8](41/preview_8.png) \| \| 42 \| 22 \| [Download](42/dataset.zip) \| ![preview 1](42/preview_1.png) \| ![preview 2](42/preview_2.png) \| ![preview 3](42/preview_3.png) \| ![preview 4](42/preview_4.png) \| ![preview 5](42/preview_5.png) \| ![preview 6](42/preview_6.png) \| ![preview 7](42/preview_7.png) \| ![preview 8](42/preview_8.png) \| \| 43 \| 10 \| [Download](43/dataset.zip) \| ![preview 1](43/preview_1.png) \| ![preview 2](43/preview_2.png) \| ![preview 3](43/preview_3.png) \| ![preview 4](43/preview_4.png) \| ![preview 5](43/preview_5.png) \| ![preview 6](43/preview_6.png) \| ![preview 7](43/preview_7.png) \| ![preview 8](43/preview_8.png) \| \| 44 \| 11 \| [Download](44/dataset.zip) \| ![preview 1](44/preview_1.png) \| ![preview 2](44/preview_2.png) \| ![preview 3](44/preview_3.png) \| ![preview 4](44/preview_4.png) \| ![preview 5](44/preview_5.png) \| ![preview 6](44/preview_6.png) \| ![preview 7](44/preview_7.png) \| ![preview 8](44/preview_8.png) \| \| 45 \| 5 \| [Download](45/dataset.zip) \| ![preview 1](45/preview_1.png) \| ![preview 2](45/preview_2.png) \| ![preview 3](45/preview_3.png) \| ![preview 4](45/preview_4.png) \| ![preview 5](45/preview_5.png) \| N/A \| N/A \| N/A \| \| 46 \| 7 \| [Download](46/dataset.zip) \| ![preview 1](46/preview_1.png) \| ![preview 2](46/preview_2.png) \| ![preview 3](46/preview_3.png) \| ![preview 4](46/preview_4.png) \| ![preview 5](46/preview_5.png) \| ![preview 6](46/preview_6.png) \| ![preview 7](46/preview_7.png) \| N/A \| \| noise \| 252 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|
autoevaluate/autoeval-eval-tweet_eval-emotion-dbaa98-66233145581	--- type: predictions tags: - autotrain - evaluation datasets: - tweet_eval eval_info: task: multi_class_classification model: FelixHonikker/bert-emotion metrics: [] dataset_name: tweet_eval dataset_config: emotion dataset_split: train col_mapping: text: text 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: Multi-class Text Classification * Model: FelixHonikker/bert-emotion * Dataset: tweet_eval * Config: emotion * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Ayushkm2799](https://huggingface.co/Ayushkm2799) for evaluating this model.
deepapaikar/Katzbot_SC_pairs_2col	--- license: apache-2.0 ---
matthewlqin/cleaned	--- dataset_info: features: - name: image dtype: image - name: text sequence: string splits: - name: train num_bytes: 789717609.75 num_examples: 3322 download_size: 395091356 dataset_size: 789717609.75 --- # Dataset Card for "cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Harsh-7300/english_to_french	--- license: mit dataset_card: H@rsh7300 language: - en - fr task_categories: - translation pretty_name: dataset3 size_categories: - 1K<n<10K tags: - legal --- # Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- 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]
ewhfef/pubmed-10k	--- task_categories: - text-generation ---
NathanRoll/TalkBank_CA_Bergmann	--- dataset_info: features: - name: audio sequence: float32 - name: __index_level_0__ dtype: string splits: - name: train num_bytes: 170179623 num_examples: 83 download_size: 169288052 dataset_size: 170179623 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "TalkBank_CA_Bergmann" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/saeki_sayaka_yagatekimininaru	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Saeki Sayaka This is the dataset of Saeki Sayaka, containing 129 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 \| 129 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 304 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| raw-stage3-eyes \| 349 \| [Download](dataset-raw-stage3-eyes.zip) \| 3-stage cropped (with eye-focus) raw data with meta information. \| \| 384x512 \| 129 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x704 \| 129 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x880 \| 129 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 304 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 304 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p512-640 \| 248 \| [Download](dataset-stage3-p512-640.zip) \| 3-stage cropped dataset with the area not less than 512x512 pixels. \| \| stage3-eyes-640 \| 349 \| [Download](dataset-stage3-eyes-640.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. \| \| stage3-eyes-800 \| 349 \| [Download](dataset-stage3-eyes-800.zip) \| 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. \|
XAC-Moon/MOON	--- license: apache-2.0 ---
LangChainDatasets/openapi-chain-klarna-products-get	--- license: mit ---
WillHeld/phl_accent_cv	--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accents dtype: string - name: variant dtype: 'null' - name: locale dtype: string - name: segment dtype: 'null' - name: label dtype: int64 - name: embed sequence: float64 - name: audio dtype: audio splits: - name: train num_bytes: 762096970.116 num_examples: 4287 download_size: 724530999 dataset_size: 762096970.116 configs: - config_name: default data_files: - split: train path: data/train-* ---
Garydesu/RStest	--- dataset_info: features: - name: image sequence: sequence: sequence: uint8 - name: label sequence: sequence: uint8 splits: - name: train num_bytes: 1393870128 num_examples: 166 download_size: 438686388 dataset_size: 1393870128 ---
Eitanli/rewrite_instructions_bu	--- dataset_info: features: - name: id dtype: int64 - name: recipe dtype: string - name: instructions dtype: string splits: - name: train num_bytes: 160548334 num_examples: 74401 download_size: 81393986 dataset_size: 160548334 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rewrite_instructions_bu" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
wbensvage/clothes_desc	--- license: apache-2.0 annotations_creators: - human generated by using detail_desc and color language: - en language_creators: - other multilinguality: - monolingual pretty_name: 'H&M Clothes captions' size_categories: - n=1K source_datasets: - www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations tags: [] task_categories: - text-to-image task_ids: [] --- # Dataset Card for H&M Clothes captions _Dataset used to train/finetune [Clothes text to image model] Captions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data?select=images) For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided. ---
Nikutka/L1_poleval_korpus_wzorcowy	--- dataset_info: features: - name: content dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 20564 num_examples: 253 - name: test num_bytes: 1963 num_examples: 25 download_size: 18165 dataset_size: 22527 --- # Dataset Card for "L1_poleval_korpus_wzorcowy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_jeff31415__TinyLlama-1.1B-1T-OpenOrca	--- pretty_name: Evaluation run of jeff31415/TinyLlama-1.1B-1T-OpenOrca dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jeff31415/TinyLlama-1.1B-1T-OpenOrca](https://huggingface.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca)\ \ 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_jeff31415__TinyLlama-1.1B-1T-OpenOrca\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-09T19:50:28.018627](https://huggingface.co/datasets/open-llm-leaderboard/details_jeff31415__TinyLlama-1.1B-1T-OpenOrca/blob/main/results_2024-03-09T19-50-28.018627.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\": 0.25775935350308826,\n\ \ \"acc_stderr\": 0.030862569052274226,\n \"acc_norm\": 0.2586688775762298,\n\ \ \"acc_norm_stderr\": 0.03162013433693599,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871114,\n \"mc2\": 0.38577014111881996,\n\ \ \"mc2_stderr\": 0.014141938216925623\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.2909556313993174,\n \"acc_stderr\": 0.013273077865907576,\n\ \ \"acc_norm\": 0.31313993174061433,\n \"acc_norm_stderr\": 0.013552671543623497\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.4082852021509659,\n\ \ \"acc_stderr\": 0.004905119039849457,\n \"acc_norm\": 0.523401712806214,\n\ \ \"acc_norm_stderr\": 0.004984313205791438\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.3037037037037037,\n\ \ \"acc_stderr\": 0.03972552884785136,\n \"acc_norm\": 0.3037037037037037,\n\ \ \"acc_norm_stderr\": 0.03972552884785136\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.03317672787533157,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.03317672787533157\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.23018867924528302,\n \"acc_stderr\": 0.025907897122408173,\n\ \ \"acc_norm\": 0.23018867924528302,\n \"acc_norm_stderr\": 0.025907897122408173\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2152777777777778,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.2152777777777778,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n\ \ \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.21965317919075145,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n\ \ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n\ \ },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\"\ : {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.028659179374292323,\n\ \ \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.028659179374292323\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135303,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135303\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.20634920634920634,\n\ \ \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n\ \ \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.24516129032258063,\n\ \ \"acc_stderr\": 0.02447224384089553,\n \"acc_norm\": 0.24516129032258063,\n\ \ \"acc_norm_stderr\": 0.02447224384089553\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.03144712581678242,\n\ \ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.03144712581678242\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\"\ : 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.029620227874790465,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.029620227874790465\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.20725388601036268,\n \"acc_stderr\": 0.02925282329180363,\n\ \ \"acc_norm\": 0.20725388601036268,\n \"acc_norm_stderr\": 0.02925282329180363\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.2153846153846154,\n \"acc_stderr\": 0.020843034557462878,\n\ \ \"acc_norm\": 0.2153846153846154,\n \"acc_norm_stderr\": 0.020843034557462878\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2777777777777778,\n \"acc_stderr\": 0.027309140588230182,\n \ \ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230182\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.23949579831932774,\n \"acc_stderr\": 0.027722065493361276,\n\ \ \"acc_norm\": 0.23949579831932774,\n \"acc_norm_stderr\": 0.027722065493361276\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"\ acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.20917431192660552,\n \"acc_stderr\": 0.017437937173343233,\n \"\ acc_norm\": 0.20917431192660552,\n \"acc_norm_stderr\": 0.017437937173343233\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.37037037037037035,\n \"acc_stderr\": 0.03293377139415191,\n \"\ acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.03293377139415191\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.20588235294117646,\n \"acc_stderr\": 0.028379449451588674,\n \"\ acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.028379449451588674\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \ \ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.35874439461883406,\n\ \ \"acc_stderr\": 0.03219079200419996,\n \"acc_norm\": 0.35874439461883406,\n\ \ \"acc_norm_stderr\": 0.03219079200419996\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.2824427480916031,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.2824427480916031,\n \"acc_norm_stderr\": 0.03948406125768361\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.038968789850704164,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.038968789850704164\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.04236511258094634,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.04236511258094634\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04109974682633932,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04109974682633932\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.24358974358974358,\n\ \ \"acc_stderr\": 0.028120966503914404,\n \"acc_norm\": 0.24358974358974358,\n\ \ \"acc_norm_stderr\": 0.028120966503914404\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.2720306513409962,\n\ \ \"acc_stderr\": 0.01591336744750052,\n \"acc_norm\": 0.2720306513409962,\n\ \ \"acc_norm_stderr\": 0.01591336744750052\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.023176298203992016,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.023176298203992016\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.26256983240223464,\n\ \ \"acc_stderr\": 0.014716824273017761,\n \"acc_norm\": 0.26256983240223464,\n\ \ \"acc_norm_stderr\": 0.014716824273017761\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.238562091503268,\n \"acc_stderr\": 0.024404394928087866,\n\ \ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.024404394928087866\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2347266881028939,\n\ \ \"acc_stderr\": 0.024071805887677045,\n \"acc_norm\": 0.2347266881028939,\n\ \ \"acc_norm_stderr\": 0.024071805887677045\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.2932098765432099,\n \"acc_stderr\": 0.025329888171900915,\n\ \ \"acc_norm\": 0.2932098765432099,\n \"acc_norm_stderr\": 0.025329888171900915\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180844,\n \ \ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180844\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.23663624511082137,\n\ \ \"acc_stderr\": 0.010855137351572742,\n \"acc_norm\": 0.23663624511082137,\n\ \ \"acc_norm_stderr\": 0.010855137351572742\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.1801470588235294,\n \"acc_stderr\": 0.02334516361654486,\n\ \ \"acc_norm\": 0.1801470588235294,\n \"acc_norm_stderr\": 0.02334516361654486\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.2581699346405229,\n \"acc_stderr\": 0.017704531653250068,\n \ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.017704531653250068\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.2545454545454545,\n\ \ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.2545454545454545,\n\ \ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.24897959183673468,\n \"acc_stderr\": 0.02768297952296023,\n\ \ \"acc_norm\": 0.24897959183673468,\n \"acc_norm_stderr\": 0.02768297952296023\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553027,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553027\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.30409356725146197,\n \"acc_stderr\": 0.03528211258245232,\n\ \ \"acc_norm\": 0.30409356725146197,\n \"acc_norm_stderr\": 0.03528211258245232\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871114,\n \"mc2\": 0.38577014111881996,\n\ \ \"mc2_stderr\": 0.014141938216925623\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.5824782951854776,\n \"acc_stderr\": 0.013859978264440251\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.016679302501895376,\n \ \ \"acc_stderr\": 0.0035275958887224465\n }\n}\n```" repo_url: https://huggingface.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|arc:challenge\|25_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-09T19-50-28.018627.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|gsm8k\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hellaswag\|10_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T19-50-28.018627.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-09T19-50-28.018627.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '/details_harness\|winogrande\|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-09T19-50-28.018627.parquet' - config_name: results data_files: - split: 2024_03_09T19_50_28.018627 path: - results_2024-03-09T19-50-28.018627.parquet - split: latest path: - results_2024-03-09T19-50-28.018627.parquet --- # Dataset Card for Evaluation run of jeff31415/TinyLlama-1.1B-1T-OpenOrca <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [jeff31415/TinyLlama-1.1B-1T-OpenOrca](https://huggingface.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca) 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_jeff31415__TinyLlama-1.1B-1T-OpenOrca", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-09T19:50:28.018627](https://huggingface.co/datasets/open-llm-leaderboard/details_jeff31415__TinyLlama-1.1B-1T-OpenOrca/blob/main/results_2024-03-09T19-50-28.018627.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": 0.25775935350308826, "acc_stderr": 0.030862569052274226, "acc_norm": 0.2586688775762298, "acc_norm_stderr": 0.03162013433693599, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871114, "mc2": 0.38577014111881996, "mc2_stderr": 0.014141938216925623 }, "harness\|arc:challenge\|25": { "acc": 0.2909556313993174, "acc_stderr": 0.013273077865907576, "acc_norm": 0.31313993174061433, "acc_norm_stderr": 0.013552671543623497 }, "harness\|hellaswag\|10": { "acc": 0.4082852021509659, "acc_stderr": 0.004905119039849457, "acc_norm": 0.523401712806214, "acc_norm_stderr": 0.004984313205791438 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.3037037037037037, "acc_stderr": 0.03972552884785136, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.03972552884785136 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03317672787533157, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03317672787533157 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.23018867924528302, "acc_stderr": 0.025907897122408173, "acc_norm": 0.23018867924528302, "acc_norm_stderr": 0.025907897122408173 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2152777777777778, "acc_stderr": 0.03437079344106135, "acc_norm": 0.2152777777777778, "acc_norm_stderr": 0.03437079344106135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.16, "acc_stderr": 0.03684529491774709, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774709 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.21965317919075145, "acc_stderr": 0.031568093627031744, "acc_norm": 0.21965317919075145, "acc_norm_stderr": 0.031568093627031744 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.25957446808510637, "acc_stderr": 0.028659179374292323, "acc_norm": 0.25957446808510637, "acc_norm_stderr": 0.028659179374292323 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135303, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135303 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.21, "acc_stderr": 0.04093601807403326, "acc_norm": 0.21, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.24516129032258063, "acc_stderr": 0.02447224384089553, "acc_norm": 0.24516129032258063, "acc_norm_stderr": 0.02447224384089553 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.27586206896551724, "acc_stderr": 0.03144712581678242, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.03144712581678242 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2545454545454545, "acc_stderr": 0.03401506715249039, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.03401506715249039 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.2222222222222222, "acc_stderr": 0.029620227874790465, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.029620227874790465 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.20725388601036268, "acc_stderr": 0.02925282329180363, "acc_norm": 0.20725388601036268, "acc_norm_stderr": 0.02925282329180363 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2153846153846154, "acc_stderr": 0.020843034557462878, "acc_norm": 0.2153846153846154, "acc_norm_stderr": 0.020843034557462878 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.027309140588230182, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.027309140588230182 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.23949579831932774, "acc_stderr": 0.027722065493361276, "acc_norm": 0.23949579831932774, "acc_norm_stderr": 0.027722065493361276 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2185430463576159, "acc_stderr": 0.03374235550425694, "acc_norm": 0.2185430463576159, "acc_norm_stderr": 0.03374235550425694 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.20917431192660552, "acc_stderr": 0.017437937173343233, "acc_norm": 0.20917431192660552, "acc_norm_stderr": 0.017437937173343233 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.03293377139415191, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.03293377139415191 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.20588235294117646, "acc_stderr": 0.028379449451588674, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.028379449451588674 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.25738396624472576, "acc_stderr": 0.028458820991460302, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.028458820991460302 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.35874439461883406, "acc_stderr": 0.03219079200419996, "acc_norm": 0.35874439461883406, "acc_norm_stderr": 0.03219079200419996 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2824427480916031, "acc_stderr": 0.03948406125768361, "acc_norm": 0.2824427480916031, "acc_norm_stderr": 0.03948406125768361 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2396694214876033, "acc_stderr": 0.038968789850704164, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.038968789850704164 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.04236511258094634, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.04236511258094634 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.26993865030674846, "acc_stderr": 0.034878251684978906, "acc_norm": 0.26993865030674846, "acc_norm_stderr": 0.034878251684978906 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.25, "acc_stderr": 0.04109974682633932, "acc_norm": 0.25, "acc_norm_stderr": 0.04109974682633932 }, "harness\|hendrycksTest-management\|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.24358974358974358, "acc_stderr": 0.028120966503914404, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.028120966503914404 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2720306513409962, "acc_stderr": 0.01591336744750052, "acc_norm": 0.2720306513409962, "acc_norm_stderr": 0.01591336744750052 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24566473988439305, "acc_stderr": 0.023176298203992016, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.023176298203992016 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.26256983240223464, "acc_stderr": 0.014716824273017761, "acc_norm": 0.26256983240223464, "acc_norm_stderr": 0.014716824273017761 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.238562091503268, "acc_stderr": 0.024404394928087866, "acc_norm": 0.238562091503268, "acc_norm_stderr": 0.024404394928087866 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2347266881028939, "acc_stderr": 0.024071805887677045, "acc_norm": 0.2347266881028939, "acc_norm_stderr": 0.024071805887677045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.2932098765432099, "acc_stderr": 0.025329888171900915, "acc_norm": 0.2932098765432099, "acc_norm_stderr": 0.025329888171900915 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.25886524822695034, "acc_stderr": 0.026129572527180844, "acc_norm": 0.25886524822695034, "acc_norm_stderr": 0.026129572527180844 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.23663624511082137, "acc_stderr": 0.010855137351572742, "acc_norm": 0.23663624511082137, "acc_norm_stderr": 0.010855137351572742 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.1801470588235294, "acc_stderr": 0.02334516361654486, "acc_norm": 0.1801470588235294, "acc_norm_stderr": 0.02334516361654486 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.2581699346405229, "acc_stderr": 0.017704531653250068, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.017704531653250068 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2545454545454545, "acc_stderr": 0.041723430387053825, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.041723430387053825 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.24897959183673468, "acc_stderr": 0.02768297952296023, "acc_norm": 0.24897959183673468, "acc_norm_stderr": 0.02768297952296023 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553027, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553027 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.30409356725146197, "acc_stderr": 0.03528211258245232, "acc_norm": 0.30409356725146197, "acc_norm_stderr": 0.03528211258245232 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871114, "mc2": 0.38577014111881996, "mc2_stderr": 0.014141938216925623 }, "harness\|winogrande\|5": { "acc": 0.5824782951854776, "acc_stderr": 0.013859978264440251 }, "harness\|gsm8k\|5": { "acc": 0.016679302501895376, "acc_stderr": 0.0035275958887224465 } } ``` ## 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]
noahzhy/lpr_data	--- license: mit language: - ko pretty_name: l size_categories: - 100K<n<1M ---
yuri-no/openbookqa-ITA	--- dataset_info: features: - name: id dtype: string - name: question_stem dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answerKey dtype: string - name: fact1 dtype: string - name: humanScore dtype: float64 - name: clarity dtype: float64 splits: - name: test num_bytes: 142808 num_examples: 500 download_size: 78813 dataset_size: 142808 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - question-answering language: - it size_categories: - n<1K --- ## Dataset Details This is an Italian translated version of the test only dataset [allenai/openbookqa](https://huggingface.co/datasets/allenai/openbookqa). The dataset was translated using the Palm 2 Google API.
liuyanchen1015/MULTI_VALUE_qqp_reflex_number	--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 65737 num_examples: 383 - name: test num_bytes: 685215 num_examples: 4019 - name: train num_bytes: 609260 num_examples: 3544 download_size: 722259 dataset_size: 1360212 --- # Dataset Card for "MULTI_VALUE_qqp_reflex_number" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Joe02/Belm_style_refs	--- license: other ---
joey234/medmcqa-rule-neg	--- dataset_info: features: - name: id dtype: string - name: opa dtype: string - name: opb dtype: string - name: opc dtype: string - name: opd dtype: string - name: cop dtype: class_label: names: '0': a '1': b '2': c '3': d - name: choice_type dtype: string - name: exp dtype: string - name: subject_name dtype: string - name: topic_name dtype: string - name: question dtype: string splits: - name: test num_bytes: 1417364 num_examples: 6150 - name: validation num_bytes: 2233369 num_examples: 4183 download_size: 2422050 dataset_size: 3650733 --- # Dataset Card for "medmcqa-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Ulangi/Gtzan	--- license: apache-2.0 ---
AlienKevin/LIHKG	--- license: mit language: - yue pretty_name: 連登 size_categories: - 1M<n<10M --- Scraped conversations of the LIHKG forum. Content scraped by Ayaka: https://github.com/ayaka14732/lihkg-scraper
hardikch05/Text-to-sql-v1-custom-1000	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 422792 num_examples: 1000 download_size: 158487 dataset_size: 422792 configs: - config_name: default data_files: - split: train path: data/train-* ---
vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138793	--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138793" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0	--- pretty_name: Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)\ \ 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_upstage__SOLAR-10.7B-Instruct-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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\": 0.6657586984797939,\n\ \ \"acc_stderr\": 0.03165995758526614,\n \"acc_norm\": 0.6666511531376961,\n\ \ \"acc_norm_stderr\": 0.0323050384069596,\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.013621696119173307,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.01325001257939344\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7070304720175263,\n\ \ \"acc_stderr\": 0.004541944342035901,\n \"acc_norm\": 0.8815972913762199,\n\ \ \"acc_norm_stderr\": 0.003224240722351317\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361072,\n\ \ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361072\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.02872750295788027,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.02872750295788027\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.44,\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\"\ : 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.05021167315686779\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\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236785,\n\ \ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236785\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\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130726,\n \"\ acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130726\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8032258064516129,\n \"acc_stderr\": 0.022616409420742025,\n \"\ acc_norm\": 0.8032258064516129,\n \"acc_norm_stderr\": 0.022616409420742025\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8737373737373737,\n \"acc_stderr\": 0.02366435940288023,\n \"\ acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.02366435940288023\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3814814814814815,\n \"acc_stderr\": 0.029616718927497593,\n \ \ \"acc_norm\": 0.3814814814814815,\n \"acc_norm_stderr\": 0.029616718927497593\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \ \ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\ acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\ \ \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\":\ \ 0.8565400843881856,\n \"acc_stderr\": 0.022818291821017012,\n \"\ acc_norm\": 0.8565400843881856,\n \"acc_norm_stderr\": 0.022818291821017012\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.02280138253459753\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8033205619412516,\n\ \ \"acc_stderr\": 0.014214138556913917,\n \"acc_norm\": 0.8033205619412516,\n\ \ \"acc_norm_stderr\": 0.014214138556913917\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123567,\n\ \ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123567\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.39329608938547483,\n\ \ \"acc_stderr\": 0.016337268694270112,\n \"acc_norm\": 0.39329608938547483,\n\ \ \"acc_norm_stderr\": 0.016337268694270112\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7901234567901234,\n \"acc_stderr\": 0.02265834408598137,\n\ \ \"acc_norm\": 0.7901234567901234,\n \"acc_norm_stderr\": 0.02265834408598137\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.4934810951760104,\n\ \ \"acc_stderr\": 0.012769150688867503,\n \"acc_norm\": 0.4934810951760104,\n\ \ \"acc_norm_stderr\": 0.012769150688867503\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103135,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103135\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6911764705882353,\n \"acc_stderr\": 0.018690850273595294,\n \ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.018690850273595294\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.01041084977522279\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6474601971190296,\n \ \ \"acc_stderr\": 0.013159909755930337\n }\n}\n```" repo_url: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0 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_13T21_02_33.929144 path: - '/details_harness\|arc:challenge\|25_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-13T21-02-33.929144.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|gsm8k\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hellaswag\|10_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-13T21-02-33.929144.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '/details_harness\|winogrande\|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-13T21-02-33.929144.parquet' - config_name: results data_files: - split: 2023_12_13T21_02_33.929144 path: - results_2023-12-13T21-02-33.929144.parquet - split: latest path: - results_2023-12-13T21-02-33.929144.parquet --- # Dataset Card for Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 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_upstage__SOLAR-10.7B-Instruct-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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": 0.6657586984797939, "acc_stderr": 0.03165995758526614, "acc_norm": 0.6666511531376961, "acc_norm_stderr": 0.0323050384069596, "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness\|arc:challenge\|25": { "acc": 0.6808873720136519, "acc_stderr": 0.013621696119173307, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.01325001257939344 }, "harness\|hellaswag\|10": { "acc": 0.7070304720175263, "acc_stderr": 0.004541944342035901, "acc_norm": 0.8815972913762199, "acc_norm_stderr": 0.003224240722351317 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7368421052631579, "acc_stderr": 0.03583496176361072, "acc_norm": 0.7368421052631579, "acc_norm_stderr": 0.03583496176361072 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6792452830188679, "acc_stderr": 0.02872750295788027, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.02872750295788027 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.05021167315686779, "acc_norm": 0.52, "acc_norm_stderr": 0.05021167315686779 }, "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": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6297872340425532, "acc_stderr": 0.03156564682236785, "acc_norm": 0.6297872340425532, "acc_norm_stderr": 0.03156564682236785 }, "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": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.47883597883597884, "acc_stderr": 0.025728230952130726, "acc_norm": 0.47883597883597884, "acc_norm_stderr": 0.025728230952130726 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8032258064516129, "acc_stderr": 0.022616409420742025, "acc_norm": 0.8032258064516129, "acc_norm_stderr": 0.022616409420742025 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.72, "acc_stderr": 0.04512608598542128, "acc_norm": 0.72, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8, "acc_stderr": 0.031234752377721175, "acc_norm": 0.8, "acc_norm_stderr": 0.031234752377721175 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8737373737373737, "acc_stderr": 0.02366435940288023, "acc_norm": 0.8737373737373737, "acc_norm_stderr": 0.02366435940288023 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.02098685459328973, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.02098685459328973 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6615384615384615, "acc_stderr": 0.023991500500313036, "acc_norm": 0.6615384615384615, "acc_norm_stderr": 0.023991500500313036 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3814814814814815, "acc_stderr": 0.029616718927497593, "acc_norm": 0.3814814814814815, "acc_norm_stderr": 0.029616718927497593 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7184873949579832, "acc_stderr": 0.02921354941437217, "acc_norm": 0.7184873949579832, "acc_norm_stderr": 0.02921354941437217 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8477064220183487, "acc_stderr": 0.015405084393157074, "acc_norm": 0.8477064220183487, "acc_norm_stderr": 0.015405084393157074 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5555555555555556, "acc_stderr": 0.03388857118502325, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.03388857118502325 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.0251956584289318, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.0251956584289318 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8565400843881856, "acc_stderr": 0.022818291821017012, "acc_norm": 0.8565400843881856, "acc_norm_stderr": 0.022818291821017012 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6816143497757847, "acc_stderr": 0.03126580522513713, "acc_norm": 0.6816143497757847, "acc_norm_stderr": 0.03126580522513713 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7480916030534351, "acc_stderr": 0.03807387116306086, "acc_norm": 0.7480916030534351, "acc_norm_stderr": 0.03807387116306086 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7768595041322314, "acc_stderr": 0.03800754475228733, "acc_norm": 0.7768595041322314, "acc_norm_stderr": 0.03800754475228733 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8055555555555556, "acc_stderr": 0.038260763248848646, "acc_norm": 0.8055555555555556, "acc_norm_stderr": 0.038260763248848646 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.754601226993865, "acc_stderr": 0.03380939813943354, "acc_norm": 0.754601226993865, "acc_norm_stderr": 0.03380939813943354 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.44642857142857145, "acc_stderr": 0.047184714852195886, "acc_norm": 0.44642857142857145, "acc_norm_stderr": 0.047184714852195886 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459753, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459753 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8033205619412516, "acc_stderr": 0.014214138556913917, "acc_norm": 0.8033205619412516, "acc_norm_stderr": 0.014214138556913917 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7601156069364162, "acc_stderr": 0.022989592543123567, "acc_norm": 0.7601156069364162, "acc_norm_stderr": 0.022989592543123567 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.39329608938547483, "acc_stderr": 0.016337268694270112, "acc_norm": 0.39329608938547483, "acc_norm_stderr": 0.016337268694270112 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7581699346405228, "acc_stderr": 0.024518195641879334, "acc_norm": 0.7581699346405228, "acc_norm_stderr": 0.024518195641879334 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.729903536977492, "acc_stderr": 0.02521804037341062, "acc_norm": 0.729903536977492, "acc_norm_stderr": 0.02521804037341062 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7901234567901234, "acc_stderr": 0.02265834408598137, "acc_norm": 0.7901234567901234, "acc_norm_stderr": 0.02265834408598137 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.4934810951760104, "acc_stderr": 0.012769150688867503, "acc_norm": 0.4934810951760104, "acc_norm_stderr": 0.012769150688867503 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7389705882352942, "acc_stderr": 0.026679252270103135, "acc_norm": 0.7389705882352942, "acc_norm_stderr": 0.026679252270103135 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6911764705882353, "acc_stderr": 0.018690850273595294, "acc_norm": 0.6911764705882353, "acc_norm_stderr": 0.018690850273595294 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6909090909090909, "acc_stderr": 0.044262946482000985, "acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7346938775510204, "acc_stderr": 0.0282638899437846, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.0282638899437846 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598052, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598052 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03126781714663179, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness\|winogrande\|5": { "acc": 0.8358326756116812, "acc_stderr": 0.01041084977522279 }, "harness\|gsm8k\|5": { "acc": 0.6474601971190296, "acc_stderr": 0.013159909755930337 } } ``` ## 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]
theoxo/proofwriter-deduction-balanced	--- license: cc-by-4.0 --- A processed subset of the OWA section of the [ProofWriter dataset](https://allenai.org/data/proofwriter). Each train/test split contains 300 entries, each of which has a unique set of theories and a single question for those theories. Both splits are balanced so that the depth of the proof required to answer the question varies evenly between 0-5 (50 entries each), and the labels are balanced (100 each). 'Unknown' labels have been replaced by 'Uncertain' to match other datasets.
jieunnie/ColorLand2	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1048630.0 num_examples: 1 download_size: 68080 dataset_size: 1048630.0 --- # Dataset Card for "ColorLand2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
IlyaGusev/ru_turbo_saiga	--- dataset_info: features: - name: messages sequence: - name: role dtype: string - name: content dtype: string - name: seed dtype: string - name: source dtype: string - name: model_name dtype: string splits: - name: train num_bytes: 87316730 num_examples: 37731 download_size: 21742388 dataset_size: 87316730 license: cc-by-4.0 task_categories: - text-generation - text2text-generation language: - ru tags: - chat size_categories: - 10K<n<100K --- # Saiga Dataset of ChatGPT-generated chats in Russian. <img src="https://cdn.midjourney.com/0db33d04-9d39-45f3-acb2-e5c789852e23/0_3.png" > Based on the [Baize](https://github.com/project-baize/baize-chatbot) paper. Code: [link](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chat.py). Prompt: ``` Идёт диалог между пользователем и ИИ ассистентом. Пользователь и ассистент общаются на тему: {{seed}} Реплики человека начинаются с [Пользователь], реплики ассистента начинаются с [Ассистент]. Пользователь задаёт вопросы на основе темы и предыдущих сообщений. Пользователь обрывает беседу, когда у него не остается вопросов. Ассистент даёт максимально полные, информативные, точные и творческие ответы. Ассистент старается не задавать вопросов, за исключением уточняющих. Ассистент может отвечать несколькими абзацами. Ассистент может использовать Markdown. Закончи диалог точно в таком же формате. [Пользователь] Привет! [Ассистент] Привет! Чем я могу помочь? ``` ## Legal disclaimer Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit for us developing models that compete with OpenAI. Not for you.
fathyshalab/reklamation24_full	--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: label_name dtype: string - name: __index_level_0__ dtype: int64 - name: domain dtype: string splits: - name: train num_bytes: 3300008 num_examples: 6199 - name: test num_bytes: 831948 num_examples: 1559 download_size: 2038299 dataset_size: 4131956 --- # Dataset Card for "reklamation24_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ruslanasenov/llm-tolkien	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2196528.0 num_examples: 268 - name: test num_bytes: 245880.0 num_examples: 30 download_size: 1124977 dataset_size: 2442408.0 --- # Dataset Card for "llm-tolkien" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ahmedtremo/guanaco-llama2-1k	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1655208 num_examples: 1000 download_size: 966969 dataset_size: 1655208 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Deivid457/Nobru	--- license: openrail ---
ro-h/regulatory_comments	--- language: - en tags: - government - api - policy pretty_name: Regulation.gov Public Comments size_categories: - n<1K task_categories: - text-classification --- # Dataset Card for Regulatory Comments (Predownloaded; No API Call) United States governmental agencies often make proposed regulations open to the public for comment. Proposed regulations are organized into "dockets". This dataset will use Regulation.gov public API to aggregate and clean public comments for dockets that mention substance use. Each example will consist of one docket, and include metadata such as docket id, docket title, etc. Each docket entry will also include information about the top 10 comments, including comment metadata and comment text. In this version, the data has been preloaded and saved to the repository. Raw data can be found in docket_comments_all.json. The code used to call the api can be found in api_call.py. If the user wants to call from the API directly, reference [https://huggingface.co/datasets/ro-h/regulatory_comments_api]. For an example of how to use this dataset, reference [https://colab.research.google.com/drive/1AiFznbHaDVszcmXYS3Ht5QLov2bvfQFX?usp=sharing]. ## Dataset Details ### Dataset Description and Structure This dataset will contain approximately 100 dockets. The number of dockets included are rate-limited by the government API. If a larger set of dockets are required, consider requesting a rate-unlimited API key and directly calling from the API using [https://huggingface.co/datasets/ro-h/regulatory_comments_api]. Each docket will be associated with at least one comment. The maximum number of comments per docket is 10. Comments will be retrieved in relevance order according to Regulation.gov. The following information is included in this dataset: Docket Metadata id (int): A unique numerical identifier assigned to each regulatory docket. agency (str): The abbreviation for the agency posting the regulatory docket (e.g., "FDA") title (str): The official title or name of the regulatory docket. This title typically summarizes the main issue or area of regulation covered by the docket. update_date (str): The date when the docket was last modified on Regulations.gov. update_time (str): The time when the docket was last modified on Regulations.gov. purpose (str): Whether the docket was rulemaking, non-rulemaking, or other. keywords (list): A string of keywords, as determined by Regulations.gov. Comment Metadata Note that huggingface converts lists of dictionaries to dictionaries of lists. comment_id (int): A unique numerical identifier for each public comment submitted on the docket. comment_url (str): A URL or web link to the specific comment or docket on Regulations.gov. This allows direct access to the original document or page for replicability purposes. comment_date (str): The date when the comment was posted on Regulations.gov. This is important for understanding the timeline of public engagement. comment_time (str): The time when the comment was posted on Regulations.gov. commenter_fname (str): The first name of the individual or entity that submitted the comment. This could be a person, organization, business, or government entity. commenter_lname (str): The last name of the individual or entity that submitted the comment. comment_length (int): The length of the comment in terms of the number of characters (spaces included) Comment Content text (str): The actual text of the comment submitted. This is the primary content for analysis, containing the commenter's views, arguments, and feedback on the regulatory matter. ### Dataset Limitations Commenter name features were phased in later in the system, so some dockets will have no first name/last name entries. Further, some comments were uploaded solely via attachment, and are stored in the system as null since the API has no access to comment attachments. However, many large companies will upload their comments via attachments, making any sentiment analysis biased towards individual commenters. - Curated by: Ro Huang ### Dataset Sources - Repository: [https://huggingface.co/datasets/ro-h/regulatory_comments_api] - Original Website: [https://www.regulations.gov/] - API Website: [https://open.gsa.gov/api/regulationsgov/] ## Uses This dataset may be used by researchers or policy-stakeholders curious about the influence of public comments on regulation development. For example, sentiment analysis may be run on comment text; alternatively, simple descriptive analysis on the comment length and agency regulation may prove interesting. ## Dataset Creation ### Curation Rationale After a law is passed, it may require specific details or guidelines to be practically enforceable or operable. Federal agencies and the Executive branch engage in rulemaking, which specify the practical ways that legislation can get turned into reality. Then, they will open a Public Comment period in which they will receive comments, suggestions, and questions on the regulations they proposed. After taking in the feedback, the agency will modify their regulation and post a final rule. As an example, imagine that the legislative branch of the government passes a bill to increase the number of hospitals nationwide. While the Congressman drafting the bill may have provided some general guidelines (e.g., there should be at least one hospital in a zip code), there is oftentimes ambiguity on how the bill’s goals should be achieved. The Department of Health and Human Services is tasked with implementing this new law, given its relevance to national healthcare infrastructure. The agency would draft and publish a set of proposed rules, which might include criteria for where new hospitals can be built, standards for hospital facilities, and the process for applying for federal funding. During the Public Comment period, healthcare providers, local governments, and the public can provide feedback or express concerns about the proposed rules. The agency will then read through these public comments, and modify their regulation accordingly. While this is a vital part of the United States regulatory process, there is little understanding of how agencies approach public comments and modify their proposed regulations. Further, the data extracted from the API is often unclean and difficult to navigate. This dataset seeks to offer some clarity through aggregating comments related to substance use, an issue that a diversity of stakeholders have investment in. #### Data Collection and Processing Filtering Methods: For each docket, we retrieve relevant metadata such as docket ID, title, context, purpose, and keywords. Additionally, the top 10 comments for each docket are collected, including their metadata (comment ID, URL, date, title, commenter's first and last name) and the comment text itself. The process focuses on the first page of 25 comments for each docket, and the top 10 comments are selected based on their order of appearance in the API response. Dockets with no comments are filtered out. Data Normalization: The collected data is normalized into a structured format. Each docket and its associated comments are organized into a nested dictionary structure. This structure includes key information about the docket and a list of comments, each with its detailed metadata. Data Cleaning: HTML text tags are removed from comment text. However, the content of the comment remains unedited, meaning any typos or grammatical errors in the original comment are preserved. Tools and Libraries Used: Requests Library: Used for making API calls to the Regulations.gov API to fetch dockets and comments data. Datasets Library from HuggingFace: Employed for defining and managing the dataset's structure and generation process. Python: The entire data collection and processing script is written in Python. Error Handling: In the event of a failed API request (indicated by a non-200 HTTP response status), the data collection process for the current docket is halted, and the process moves to the next docket.
CyberHarem/mutsuki_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mutsuki/睦月/睦月 (Azur Lane) This is the dataset of mutsuki/睦月/睦月 (Azur Lane), containing 139 images and their tags. The core tags of this character are `animal_ears, brown_hair, cat_ears, green_eyes, hat, twintails, school_hat, short_hair, yellow_headwear, tail, ribbon, cat_tail, animal_ear_fluff, fang, short_twintails, bangs, low_twintails, bow, cat_girl, ears_through_headwear`, 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 \| 139 \| 137.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 139 \| 87.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 325 \| 188.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 139 \| 125.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 325 \| 252.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/mutsuki_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 34 \| ![](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) \| kindergarten_uniform, blue_shirt, long_sleeves, looking_at_viewer, open_mouth, 1girl, blush, solo, yellow_skirt, yellow_neckerchief, pleated_skirt, :d, holding_lollipop, blunt_bangs, sailor_collar, jingle_bell, shoes, white_socks, white_background, hair_bow \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, full_body, kindergarten_uniform, looking_at_viewer, open_mouth, pleated_skirt, red_skirt, smile, solo, standing, tail_bow, white_pantyhose, ;d, lifebuoy, lollipop, one_eye_closed, arm_up, candy_wrapper, chibi, jingle_bell, simple_background, white_background, white_shirt, black_footwear, blunt_bangs, brown_footwear, holding_candy, legs_apart, mary_janes, outstretched_arm, paw_print, pigeon-toed, pink_bowtie, puffy_long_sleeves, rigging, torpedo_tubes, turret \| \| 2 \| 6 \| ![](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, blush, hetero, loli, open_mouth, sex, solo_focus, vaginal, 1boy, navel, penis, spread_legs, bar_censor, nude, tears, cum_in_pussy, nipples, sweat \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bell, blush, christmas, green_bow, open_mouth, red_bow, santa_hat, solo, candy_cane, fur-trimmed_headwear, looking_at_viewer, red_headwear, white_dress, wrist_cuffs, brown_footwear, red_capelet, striped_bow, :d, ;d, animal, blunt_bangs, chick, fur-trimmed_boots, fur-trimmed_capelet, holding_food, one_eye_closed, sack, snowflakes, white_background, white_thighhighs \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| kindergarten_uniform \| blue_shirt \| long_sleeves \| looking_at_viewer \| open_mouth \| 1girl \| blush \| solo \| yellow_skirt \| yellow_neckerchief \| pleated_skirt \| :d \| holding_lollipop \| blunt_bangs \| sailor_collar \| jingle_bell \| shoes \| white_socks \| white_background \| hair_bow \| full_body \| red_skirt \| smile \| standing \| tail_bow \| white_pantyhose \| ;d \| lifebuoy \| lollipop \| one_eye_closed \| arm_up \| candy_wrapper \| chibi \| simple_background \| white_shirt \| black_footwear \| brown_footwear \| holding_candy \| legs_apart \| mary_janes \| outstretched_arm \| paw_print \| pigeon-toed \| pink_bowtie \| puffy_long_sleeves \| rigging \| torpedo_tubes \| turret \| hetero \| loli \| sex \| solo_focus \| vaginal \| 1boy \| navel \| penis \| spread_legs \| bar_censor \| nude \| tears \| cum_in_pussy \| nipples \| sweat \| bell \| christmas \| green_bow \| red_bow \| santa_hat \| candy_cane \| fur-trimmed_headwear \| red_headwear \| white_dress \| wrist_cuffs \| red_capelet \| striped_bow \| animal \| chick \| fur-trimmed_boots \| fur-trimmed_capelet \| holding_food \| sack \| snowflakes \| white_thighhighs \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------\|:-------------\|:---------------\|:--------------------\|:-------------\|:--------\|:--------\|:-------\|:---------------\|:---------------------\|:----------------\|:-----\|:-------------------\|:--------------\|:----------------\|:--------------\|:--------\|:--------------\|:-------------------\|:-----------\|:------------\|:------------\|:--------\|:-----------\|:-----------\|:------------------\|:-----\|:-----------\|:-----------\|:-----------------\|:---------\|:----------------\|:--------\|:--------------------\|:--------------\|:-----------------\|:-----------------\|:----------------\|:-------------\|:-------------\|:-------------------\|:------------\|:--------------\|:--------------\|:---------------------\|:----------\|:----------------\|:---------\|:---------\|:-------\|:------\|:-------------\|:----------\|:-------\|:--------\|:--------\|:--------------\|:-------------\|:-------\|:--------\|:---------------\|:----------\|:--------\|:-------\|:------------\|:------------\|:----------\|:------------\|:-------------\|:-----------------------\|:---------------\|:--------------\|:--------------\|:--------------\|:--------------\|:---------\|:--------\|:--------------------\|:----------------------\|:---------------\|:-------\|:-------------\|:-------------------\| \| 0 \| 34 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| \| X \| X \| X \| X \| X \| \| \| X \| \| \| X \| \| X \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| \| \| \| X \| X \| X \| X \| X \| \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
hippocrates/PmcPatient_test	--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string splits: - name: train num_bytes: 388938338 num_examples: 126454 - name: valid num_bytes: 388938338 num_examples: 126454 - name: test num_bytes: 388938338 num_examples: 126454 download_size: 622007712 dataset_size: 1166815014 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---
eunbinni/ola_polyglot_3.8B_t2_data	--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 46498843 num_examples: 139107 download_size: 28667291 dataset_size: 46498843 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ola_polyglot_3.8B_t2_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
DipakBundheliya/Shipping-label-NER	--- license: afl-3.0 ---
james-burton/fake_job_postings2_ord	--- dataset_info: features: - name: title dtype: string - name: salary_range dtype: string - name: description dtype: string - name: required_experience dtype: float64 - name: required_education dtype: float64 - name: fraudulent dtype: int64 splits: - name: train num_bytes: 14528605 num_examples: 10816 - name: validation num_bytes: 2469547 num_examples: 1909 - name: test num_bytes: 4328842 num_examples: 3182 download_size: 0 dataset_size: 21326994 --- # Dataset Card for "fake_job_postings2_ord" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rbeauchamp/blip_50k_val	--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: seed dtype: uint32 - name: step dtype: uint16 - name: cfg dtype: float32 - name: sampler dtype: string - name: width dtype: uint16 - name: height dtype: uint16 - name: user_name dtype: string - name: timestamp dtype: timestamp[us, tz=UTC] - name: image_nsfw dtype: float32 - name: prompt_nsfw dtype: float32 splits: - name: train num_bytes: 4614195691.6 num_examples: 10000 download_size: 4624195058 dataset_size: 4614195691.6 --- # Dataset Card for "blip_50k_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_allbyai__ToRoLaMa-7b-v1.0	--- pretty_name: Evaluation run of allbyai/ToRoLaMa-7b-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allbyai/ToRoLaMa-7b-v1.0](https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0) 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_allbyai__ToRoLaMa-7b-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T09:43:59.013115](https://huggingface.co/datasets/open-llm-leaderboard/details_allbyai__ToRoLaMa-7b-v1.0/blob/main/results_2024-01-05T09-43-59.013115.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\": 0.45235843004419446,\n\ \ \"acc_stderr\": 0.03423154255354607,\n \"acc_norm\": 0.45929415154501946,\n\ \ \"acc_norm_stderr\": 0.035110482824261206,\n \"mc1\": 0.30354957160342716,\n\ \ \"mc1_stderr\": 0.016095884155386844,\n \"mc2\": 0.44894454656581184,\n\ \ \"mc2_stderr\": 0.015890874190577126\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.47013651877133106,\n \"acc_stderr\": 0.014585305840007104,\n\ \ \"acc_norm\": 0.5170648464163823,\n \"acc_norm_stderr\": 0.0146028783885366\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.566122286397132,\n\ \ \"acc_stderr\": 0.004945956744943814,\n \"acc_norm\": 0.7381995618402709,\n\ \ \"acc_norm_stderr\": 0.00438716120308797\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\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.45394736842105265,\n \"acc_stderr\": 0.04051646342874143,\n\ \ \"acc_norm\": 0.45394736842105265,\n \"acc_norm_stderr\": 0.04051646342874143\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\": 0.46037735849056605,\n \"acc_stderr\": 0.030676096599389188,\n\ \ \"acc_norm\": 0.46037735849056605,\n \"acc_norm_stderr\": 0.030676096599389188\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.041553199555931467,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.041553199555931467\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n\ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.3699421965317919,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.3699421965317919,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4127659574468085,\n \"acc_stderr\": 0.03218471141400351,\n\ \ \"acc_norm\": 0.4127659574468085,\n \"acc_norm_stderr\": 0.03218471141400351\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3157894736842105,\n\ \ \"acc_stderr\": 0.04372748290278006,\n \"acc_norm\": 0.3157894736842105,\n\ \ \"acc_norm_stderr\": 0.04372748290278006\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.29894179894179895,\n \"acc_stderr\": 0.023577604791655826,\n \"\ acc_norm\": 0.29894179894179895,\n \"acc_norm_stderr\": 0.023577604791655826\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.04006168083848877,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.04006168083848877\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5258064516129032,\n\ \ \"acc_stderr\": 0.028406095057653326,\n \"acc_norm\": 0.5258064516129032,\n\ \ \"acc_norm_stderr\": 0.028406095057653326\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n\ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.0381549430868893,\n\ \ \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.0381549430868893\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.5757575757575758,\n \"acc_stderr\": 0.035212249088415845,\n \"\ acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.035212249088415845\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.6476683937823834,\n \"acc_stderr\": 0.03447478286414358,\n\ \ \"acc_norm\": 0.6476683937823834,\n \"acc_norm_stderr\": 0.03447478286414358\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.3769230769230769,\n \"acc_stderr\": 0.024570975364225995,\n\ \ \"acc_norm\": 0.3769230769230769,\n \"acc_norm_stderr\": 0.024570975364225995\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085622,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085622\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.42436974789915966,\n \"acc_stderr\": 0.03210479051015776,\n\ \ \"acc_norm\": 0.42436974789915966,\n \"acc_norm_stderr\": 0.03210479051015776\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.26490066225165565,\n \"acc_stderr\": 0.03603038545360384,\n \"\ acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.03603038545360384\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.5486238532110091,\n \"acc_stderr\": 0.021335714711268786,\n \"\ acc_norm\": 0.5486238532110091,\n \"acc_norm_stderr\": 0.021335714711268786\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.2638888888888889,\n \"acc_stderr\": 0.030058202704309846,\n \"\ acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.030058202704309846\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239172,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239172\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5907172995780591,\n \"acc_stderr\": 0.032007041833595914,\n \ \ \"acc_norm\": 0.5907172995780591,\n \"acc_norm_stderr\": 0.032007041833595914\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.547085201793722,\n\ \ \"acc_stderr\": 0.033408675019233246,\n \"acc_norm\": 0.547085201793722,\n\ \ \"acc_norm_stderr\": 0.033408675019233246\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.5190839694656488,\n \"acc_stderr\": 0.04382094705550988,\n\ \ \"acc_norm\": 0.5190839694656488,\n \"acc_norm_stderr\": 0.04382094705550988\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.6446280991735537,\n \"acc_stderr\": 0.0436923632657398,\n \"acc_norm\"\ : 0.6446280991735537,\n \"acc_norm_stderr\": 0.0436923632657398\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5462962962962963,\n\ \ \"acc_stderr\": 0.04812917324536824,\n \"acc_norm\": 0.5462962962962963,\n\ \ \"acc_norm_stderr\": 0.04812917324536824\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.49079754601226994,\n \"acc_stderr\": 0.03927705600787443,\n\ \ \"acc_norm\": 0.49079754601226994,\n \"acc_norm_stderr\": 0.03927705600787443\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.375,\n\ \ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \ \ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.6407766990291263,\n \"acc_stderr\": 0.04750458399041695,\n\ \ \"acc_norm\": 0.6407766990291263,\n \"acc_norm_stderr\": 0.04750458399041695\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.6923076923076923,\n\ \ \"acc_stderr\": 0.03023638994217309,\n \"acc_norm\": 0.6923076923076923,\n\ \ \"acc_norm_stderr\": 0.03023638994217309\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562427,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562427\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6079182630906769,\n\ \ \"acc_stderr\": 0.017458524050147636,\n \"acc_norm\": 0.6079182630906769,\n\ \ \"acc_norm_stderr\": 0.017458524050147636\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.026919095102908273,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.026919095102908273\n \ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.28938547486033517,\n\ \ \"acc_stderr\": 0.01516654455049031,\n \"acc_norm\": 0.28938547486033517,\n\ \ \"acc_norm_stderr\": 0.01516654455049031\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5032679738562091,\n \"acc_stderr\": 0.028629305194003543,\n\ \ \"acc_norm\": 0.5032679738562091,\n \"acc_norm_stderr\": 0.028629305194003543\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5369774919614148,\n\ \ \"acc_stderr\": 0.02832032583010591,\n \"acc_norm\": 0.5369774919614148,\n\ \ \"acc_norm_stderr\": 0.02832032583010591\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.4783950617283951,\n \"acc_stderr\": 0.02779476010500874,\n\ \ \"acc_norm\": 0.4783950617283951,\n \"acc_norm_stderr\": 0.02779476010500874\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611324,\n \ \ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611324\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.32985658409387225,\n\ \ \"acc_stderr\": 0.012008129938540469,\n \"acc_norm\": 0.32985658409387225,\n\ \ \"acc_norm_stderr\": 0.012008129938540469\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.029520095697687758,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.029520095697687758\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4411764705882353,\n \"acc_stderr\": 0.02008736207670286,\n \ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.02008736207670286\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.44545454545454544,\n\ \ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.44545454545454544,\n\ \ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.5102040816326531,\n \"acc_stderr\": 0.03200255347893782,\n\ \ \"acc_norm\": 0.5102040816326531,\n \"acc_norm_stderr\": 0.03200255347893782\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6368159203980099,\n\ \ \"acc_stderr\": 0.03400598505599014,\n \"acc_norm\": 0.6368159203980099,\n\ \ \"acc_norm_stderr\": 0.03400598505599014\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.40963855421686746,\n\ \ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\ \ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.6432748538011696,\n \"acc_stderr\": 0.03674013002860954,\n\ \ \"acc_norm\": 0.6432748538011696,\n \"acc_norm_stderr\": 0.03674013002860954\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.30354957160342716,\n\ \ \"mc1_stderr\": 0.016095884155386844,\n \"mc2\": 0.44894454656581184,\n\ \ \"mc2_stderr\": 0.015890874190577126\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7008681925808997,\n \"acc_stderr\": 0.012868639066091533\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.013646702047005308,\n \ \ \"acc_stderr\": 0.003195747075480772\n }\n}\n```" repo_url: https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0 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_05T09_43_59.013115 path: - '/details_harness\|arc:challenge\|25_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-05T09-43-59.013115.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|gsm8k\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hellaswag\|10_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T09-43-59.013115.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-05T09-43-59.013115.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '/details_harness\|winogrande\|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-05T09-43-59.013115.parquet' - config_name: results data_files: - split: 2024_01_05T09_43_59.013115 path: - results_2024-01-05T09-43-59.013115.parquet - split: latest path: - results_2024-01-05T09-43-59.013115.parquet --- # Dataset Card for Evaluation run of allbyai/ToRoLaMa-7b-v1.0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [allbyai/ToRoLaMa-7b-v1.0](https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0) 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_allbyai__ToRoLaMa-7b-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T09:43:59.013115](https://huggingface.co/datasets/open-llm-leaderboard/details_allbyai__ToRoLaMa-7b-v1.0/blob/main/results_2024-01-05T09-43-59.013115.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": 0.45235843004419446, "acc_stderr": 0.03423154255354607, "acc_norm": 0.45929415154501946, "acc_norm_stderr": 0.035110482824261206, "mc1": 0.30354957160342716, "mc1_stderr": 0.016095884155386844, "mc2": 0.44894454656581184, "mc2_stderr": 0.015890874190577126 }, "harness\|arc:challenge\|25": { "acc": 0.47013651877133106, "acc_stderr": 0.014585305840007104, "acc_norm": 0.5170648464163823, "acc_norm_stderr": 0.0146028783885366 }, "harness\|hellaswag\|10": { "acc": 0.566122286397132, "acc_stderr": 0.004945956744943814, "acc_norm": 0.7381995618402709, "acc_norm_stderr": 0.00438716120308797 }, "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": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.45394736842105265, "acc_stderr": 0.04051646342874143, "acc_norm": 0.45394736842105265, "acc_norm_stderr": 0.04051646342874143 }, "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": 0.46037735849056605, "acc_stderr": 0.030676096599389188, "acc_norm": 0.46037735849056605, "acc_norm_stderr": 0.030676096599389188 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.041553199555931467, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.041553199555931467 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.3699421965317919, "acc_stderr": 0.0368122963339432, "acc_norm": 0.3699421965317919, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4127659574468085, "acc_stderr": 0.03218471141400351, "acc_norm": 0.4127659574468085, "acc_norm_stderr": 0.03218471141400351 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278006, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278006 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.29894179894179895, "acc_stderr": 0.023577604791655826, "acc_norm": 0.29894179894179895, "acc_norm_stderr": 0.023577604791655826 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.04006168083848877, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.04006168083848877 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5258064516129032, "acc_stderr": 0.028406095057653326, "acc_norm": 0.5258064516129032, "acc_norm_stderr": 0.028406095057653326 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0317852971064275, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0317852971064275 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6060606060606061, "acc_stderr": 0.0381549430868893, "acc_norm": 0.6060606060606061, "acc_norm_stderr": 0.0381549430868893 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5757575757575758, "acc_stderr": 0.035212249088415845, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.035212249088415845 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6476683937823834, "acc_stderr": 0.03447478286414358, "acc_norm": 0.6476683937823834, "acc_norm_stderr": 0.03447478286414358 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.3769230769230769, "acc_stderr": 0.024570975364225995, "acc_norm": 0.3769230769230769, "acc_norm_stderr": 0.024570975364225995 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085622, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085622 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.42436974789915966, "acc_stderr": 0.03210479051015776, "acc_norm": 0.42436974789915966, "acc_norm_stderr": 0.03210479051015776 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.26490066225165565, "acc_stderr": 0.03603038545360384, "acc_norm": 0.26490066225165565, "acc_norm_stderr": 0.03603038545360384 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.5486238532110091, "acc_stderr": 0.021335714711268786, "acc_norm": 0.5486238532110091, "acc_norm_stderr": 0.021335714711268786 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.2638888888888889, "acc_stderr": 0.030058202704309846, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.030058202704309846 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239172, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239172 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5907172995780591, "acc_stderr": 0.032007041833595914, "acc_norm": 0.5907172995780591, "acc_norm_stderr": 0.032007041833595914 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.547085201793722, "acc_stderr": 0.033408675019233246, "acc_norm": 0.547085201793722, "acc_norm_stderr": 0.033408675019233246 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.5190839694656488, "acc_stderr": 0.04382094705550988, "acc_norm": 0.5190839694656488, "acc_norm_stderr": 0.04382094705550988 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.6446280991735537, "acc_stderr": 0.0436923632657398, "acc_norm": 0.6446280991735537, "acc_norm_stderr": 0.0436923632657398 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5462962962962963, "acc_stderr": 0.04812917324536824, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.04812917324536824 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.49079754601226994, "acc_stderr": 0.03927705600787443, "acc_norm": 0.49079754601226994, "acc_norm_stderr": 0.03927705600787443 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness\|hendrycksTest-management\|5": { "acc": 0.6407766990291263, "acc_stderr": 0.04750458399041695, "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.04750458399041695 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.6923076923076923, "acc_stderr": 0.03023638994217309, "acc_norm": 0.6923076923076923, "acc_norm_stderr": 0.03023638994217309 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.43, "acc_stderr": 0.04975698519562427, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562427 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6079182630906769, "acc_stderr": 0.017458524050147636, "acc_norm": 0.6079182630906769, "acc_norm_stderr": 0.017458524050147636 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5, "acc_stderr": 0.026919095102908273, "acc_norm": 0.5, "acc_norm_stderr": 0.026919095102908273 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.28938547486033517, "acc_stderr": 0.01516654455049031, "acc_norm": 0.28938547486033517, "acc_norm_stderr": 0.01516654455049031 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5032679738562091, "acc_stderr": 0.028629305194003543, "acc_norm": 0.5032679738562091, "acc_norm_stderr": 0.028629305194003543 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5369774919614148, "acc_stderr": 0.02832032583010591, "acc_norm": 0.5369774919614148, "acc_norm_stderr": 0.02832032583010591 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.4783950617283951, "acc_stderr": 0.02779476010500874, "acc_norm": 0.4783950617283951, "acc_norm_stderr": 0.02779476010500874 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.34397163120567376, "acc_stderr": 0.028338017428611324, "acc_norm": 0.34397163120567376, "acc_norm_stderr": 0.028338017428611324 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.32985658409387225, "acc_stderr": 0.012008129938540469, "acc_norm": 0.32985658409387225, "acc_norm_stderr": 0.012008129938540469 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.38235294117647056, "acc_stderr": 0.029520095697687758, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.029520095697687758 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.02008736207670286, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.02008736207670286 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.44545454545454544, "acc_stderr": 0.047605488214603246, "acc_norm": 0.44545454545454544, "acc_norm_stderr": 0.047605488214603246 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5102040816326531, "acc_stderr": 0.03200255347893782, "acc_norm": 0.5102040816326531, "acc_norm_stderr": 0.03200255347893782 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6368159203980099, "acc_stderr": 0.03400598505599014, "acc_norm": 0.6368159203980099, "acc_norm_stderr": 0.03400598505599014 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.40963855421686746, "acc_stderr": 0.03828401115079022, "acc_norm": 0.40963855421686746, "acc_norm_stderr": 0.03828401115079022 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6432748538011696, "acc_stderr": 0.03674013002860954, "acc_norm": 0.6432748538011696, "acc_norm_stderr": 0.03674013002860954 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.30354957160342716, "mc1_stderr": 0.016095884155386844, "mc2": 0.44894454656581184, "mc2_stderr": 0.015890874190577126 }, "harness\|winogrande\|5": { "acc": 0.7008681925808997, "acc_stderr": 0.012868639066091533 }, "harness\|gsm8k\|5": { "acc": 0.013646702047005308, "acc_stderr": 0.003195747075480772 } } ``` ## 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 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Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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AiresPucrs/CelebA-Smiles	--- license: other dataset_info: features: - name: image dtype: image - name: 5_o_Clock_Shadow dtype: int64 - name: Arched_Eyebrows dtype: int64 - name: Attractive dtype: int64 - name: Bags_Under_Eyes dtype: int64 - name: Bald dtype: int64 - name: Bangs dtype: int64 - name: Big_Lips dtype: int64 - name: Big_Nose dtype: int64 - name: Black_Hair dtype: int64 - name: Blond_Hair dtype: int64 - name: Blurry dtype: int64 - name: Brown_Hair dtype: int64 - name: Bushy_Eyebrows dtype: int64 - name: Chubby dtype: int64 - name: Double_Chin dtype: int64 - name: Eyeglasses dtype: int64 - name: Goatee dtype: int64 - name: Gray_Hair dtype: int64 - name: Heavy_Makeup dtype: int64 - name: High_Cheekbones dtype: int64 - name: Male dtype: int64 - name: Mouth_Slightly_Open dtype: int64 - name: Mustache dtype: int64 - name: Narrow_Eyes dtype: int64 - name: No_Beard dtype: int64 - name: Oval_Face dtype: int64 - name: Pale_Skin dtype: int64 - name: Pointy_Nose dtype: int64 - name: Receding_Hairline dtype: int64 - name: Rosy_Cheeks dtype: int64 - name: Sideburns dtype: int64 - name: Smiling dtype: int64 - name: Straight_Hair dtype: int64 - name: Wavy_Hair dtype: int64 - name: Wearing_Earrings dtype: int64 - name: Wearing_Hat dtype: int64 - name: Wearing_Lipstick dtype: int64 - name: Wearing_Necklace dtype: int64 - name: Wearing_Necktie dtype: int64 - name: Young dtype: int64 splits: - name: train num_bytes: 365293550 num_examples: 50000 download_size: 349853371 dataset_size: 365293550 configs: - config_name: default data_files: - split: train path: data/train-* pretty_name: CelebA-Smiles size_categories: - 10M<n<100M --- # CelebA-Smiles ## Overview This dataset is a subset of the [CelebFaces Attributes Dataset](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset). The dataset can be employed as the training and test sets for computer vision tasks like smile detection. ## Dataset Details The CelebA-Smiles dataset is a smaller version of the original dataset. This data originally came from [CelebFaces Attributes Dataset (CelebA)](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset) The original dataset contains : [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including - 10,177 number of identities - 202,599 face images - 5 landmark locations - 40 binary attribute annotations per image. The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face recognition, face detection, landmark (or facial part) localization, and face editing & synthesis. ```latex @inproceedings{liu2015faceattributes, title = {Deep Learning Face Attributes in the Wild}, author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou}, booktitle = {Proceedings of International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` - Dataset Name: CelebA-Smiles - Language: English - Total Size: 50,000 demonstrations ## Contents The subset dataset consists of images of celebrity people with 40 attributes. The CelebA-Smile dataset is balanced with 50% people smiling and 50% people not smiling, it also contains the other 39 attributes like "5_o_Clock_Shadow", "Arched_Eyebrows", "Attractive", "Bags_Under_Eyes", "bald", etc. ## How to use ```python from datasets import load_dataset dataset = load_dataset("AiresPucrs/CelebA-Smiles", split='train') ``` ## License The dataset is licensed under the [Other](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).
afmck/common_voice_13_0_hi_pseudo_labelled	--- dataset_info: config_name: zh-TW features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 160498655.447 num_examples: 6799 - name: validation num_bytes: 122880246.375 num_examples: 4825 - name: test num_bytes: 142152848.375 num_examples: 4825 download_size: 398340449 dataset_size: 425531750.197 configs: - config_name: zh-TW data_files: - split: train path: zh-TW/train-* - split: validation path: zh-TW/validation-* - split: test path: zh-TW/test-* ---
ai4bharat/IndicQA-Translated	--- dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: itv2 hi question dtype: string - name: itv2 hi context dtype: string - name: itv2 hi answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: test num_bytes: 15063303 num_examples: 1547 download_size: 1378045 dataset_size: 15063303 configs: - config_name: default data_files: - split: test path: data/test-* ---
Mxode/Baike-Astronomy-ZH	--- license: apache-2.0 task_categories: - text-generation language: - zh tags: - astronomy size_categories: - n<1K --- 天文学百科，包含 8 个子目录，约 1000 条词条、110,0000 个字符。数据包含一级目录、二级目录、标题、内容。其中内容已经处理为单行，且文本普遍较长。一个样例如下： ```json { "top_category": "天文学", "sub_category": "天体力学", "title": "万有引力定律", "content": "万有引力定律（汉语拼音：wàn yǒu yǐn lì zhī dìng lǜ），（universal gravitation，law of），自然界中任何两个质点都相互吸引，这个力同两个质点的质量的乘积成正比，同它们之间的距离的二次方成反比。如用m1、m2表示两质点的质量，r表示两质点间的距离，F表示作用力的值，则F＝Gm1m2／r2，式中的G是比例常量，称万有引力常量或牛顿引力常量，数值因不同单位制而异，在国际单位制中G为6.672×1011牛顿·米2／千克2。这个定律由牛顿于1687年在《原理》上首次发表，它和牛顿运动定律一起，构成了牛顿力学特别是天体力学的基础。\n　　在牛顿公布该定律之前，胡克、惠更斯都曾根据开普勒定律推测行星和太阳间存在和距离二次方成反比的引力，但未能提出数学证明，为此胡克还和牛顿通过信，因此对定律的首创权有过争议。牛顿还曾对晚年的忘年交斯多克雷说过，1666年他在家乡避瘟疫时，曾因见苹果从树上落地而想到地球对苹果的引力是否可延伸到月球。此说传布很广，许多科学家深信不疑，并对牛顿为何推迟20年才发表有种种推测。但也有人根据牛顿晚年的精神状态，认为他对斯多克雷所说的并非真情。\n　　一般物体之间的引力，在物体尺度远小于质心距离时，可视为质点；尺度和间距相近时，须视为质点系，用积分法求引力。但牛顿已算出一个密度均匀的圆球对附近质点的引力同把圆球的质量集中于球心时完全一致。对万有引力的起因，牛顿未作解释，把它视为超距力或以太的作用，系后人所为。爱因斯坦在广义相对论中将引力归之于时空曲率的变化。" } ```
Nart/parallel-ab-ru	--- language_creators: - expert-generated language: - ab - ru license: - cc0-1.0 multilinguality: - translation - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - translation task_ids: [] pretty_name: Abkhazian Russian parallel corpus --- ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) ## Dataset Description - Point of Contact: [Nart Tlisha](mailto:daniel.abzakh@gmail.com) - Size of the generated dataset: 33.5 MB ### Dataset Summary The Abkhaz Russian parallel corpus dataset is a collection of 205,665 sentences/words extracted from different sources; e-books, web scrapping. ## Dataset Creation ### Source Data Here is a link to the source on [github](https://github.com/danielinux7/Multilingual-Parallel-Corpus/blob/master/references.md) ## Considerations for Using the Data ### Other Known Limitations The accuracy of the dataset is around 95% (gramatical, arthographical errors)
open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b	--- pretty_name: Evaluation run of Azazelle/Tippy-Toppy-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Azazelle/Tippy-Toppy-7b](https://huggingface.co/Azazelle/Tippy-Toppy-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-06T01:20:11.911337](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b/blob/main/results_2024-01-06T01-20-11.911337.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\": 0.6570837201709685,\n\ \ \"acc_stderr\": 0.031992607878974816,\n \"acc_norm\": 0.658599829847844,\n\ \ \"acc_norm_stderr\": 0.03263443134197047,\n \"mc1\": 0.390452876376989,\n\ \ \"mc1_stderr\": 0.017078230743431455,\n \"mc2\": 0.5570225708371419,\n\ \ \"mc2_stderr\": 0.015617917882145785\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6382252559726962,\n \"acc_stderr\": 0.014041957945038075,\n\ \ \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.013752062419817834\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6790479984066919,\n\ \ \"acc_stderr\": 0.004658882929099517,\n \"acc_norm\": 0.8587930691097391,\n\ \ \"acc_norm_stderr\": 0.003475231889452832\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.034765901043041336\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5263157894736842,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.023287665127268542,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.023287665127268542\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919436,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919436\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465066,\n \ \ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465066\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n\ \ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700472,\n \"\ acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700472\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240647,\n \"\ acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240647\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.02574490253229092,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.02574490253229092\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608303,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608303\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.02418242749657761,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.02418242749657761\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3664804469273743,\n\ \ \"acc_stderr\": 0.01611523550486547,\n \"acc_norm\": 0.3664804469273743,\n\ \ \"acc_norm_stderr\": 0.01611523550486547\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7623456790123457,\n \"acc_stderr\": 0.02368359183700856,\n\ \ \"acc_norm\": 0.7623456790123457,\n \"acc_norm_stderr\": 0.02368359183700856\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.029736592526424438,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.029736592526424438\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7242647058823529,\n \"acc_stderr\": 0.027146271936625162,\n\ \ \"acc_norm\": 0.7242647058823529,\n \"acc_norm_stderr\": 0.027146271936625162\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000325,\n \ \ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000325\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578334,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578334\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.390452876376989,\n\ \ \"mc1_stderr\": 0.017078230743431455,\n \"mc2\": 0.5570225708371419,\n\ \ \"mc2_stderr\": 0.015617917882145785\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7884767166535123,\n \"acc_stderr\": 0.01147774768422318\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6467020470053071,\n \ \ \"acc_stderr\": 0.013166337192115686\n }\n}\n```" repo_url: https://huggingface.co/Azazelle/Tippy-Toppy-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|arc:challenge\|25_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|arc:challenge\|25_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-06T01-20-11.911337.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|gsm8k\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|gsm8k\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hellaswag\|10_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hellaswag\|10_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T00-38-33.020065.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-20-11.911337.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-06T01-20-11.911337.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '/details_harness\|winogrande\|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '/details_harness\|winogrande\|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-06T01-20-11.911337.parquet' - config_name: results data_files: - split: 2024_01_06T00_38_33.020065 path: - results_2024-01-06T00-38-33.020065.parquet - split: 2024_01_06T01_20_11.911337 path: - results_2024-01-06T01-20-11.911337.parquet - split: latest path: - results_2024-01-06T01-20-11.911337.parquet --- # Dataset Card for Evaluation run of Azazelle/Tippy-Toppy-7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Azazelle/Tippy-Toppy-7b](https://huggingface.co/Azazelle/Tippy-Toppy-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-06T01:20:11.911337](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b/blob/main/results_2024-01-06T01-20-11.911337.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": 0.6570837201709685, "acc_stderr": 0.031992607878974816, "acc_norm": 0.658599829847844, "acc_norm_stderr": 0.03263443134197047, "mc1": 0.390452876376989, "mc1_stderr": 0.017078230743431455, "mc2": 0.5570225708371419, "mc2_stderr": 0.015617917882145785 }, "harness\|arc:challenge\|25": { "acc": 0.6382252559726962, "acc_stderr": 0.014041957945038075, "acc_norm": 0.6689419795221843, "acc_norm_stderr": 0.013752062419817834 }, "harness\|hellaswag\|10": { "acc": 0.6790479984066919, "acc_stderr": 0.004658882929099517, "acc_norm": 0.8587930691097391, "acc_norm_stderr": 0.003475231889452832 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.034765901043041336, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.034765901043041336 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268542, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268542 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175008, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175008 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919436, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919436 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.36666666666666664, "acc_stderr": 0.029381620726465066, "acc_norm": 0.36666666666666664, "acc_norm_stderr": 0.029381620726465066 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6974789915966386, "acc_stderr": 0.029837962388291932, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.029837962388291932 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8550458715596331, "acc_stderr": 0.015094215699700472, "acc_norm": 0.8550458715596331, "acc_norm_stderr": 0.015094215699700472 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5462962962962963, "acc_stderr": 0.033953227263757976, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.033953227263757976 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240647, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240647 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8059071729957806, "acc_stderr": 0.02574490253229092, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.02574490253229092 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6995515695067265, "acc_stderr": 0.03076935200822914, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.03076935200822914 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608303, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608303 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7196531791907514, "acc_stderr": 0.02418242749657761, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.02418242749657761 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3664804469273743, "acc_stderr": 0.01611523550486547, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.01611523550486547 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7483660130718954, "acc_stderr": 0.0248480182638752, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.0248480182638752 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7623456790123457, "acc_stderr": 0.02368359183700856, "acc_norm": 0.7623456790123457, "acc_norm_stderr": 0.02368359183700856 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.46099290780141844, "acc_stderr": 0.029736592526424438, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.029736592526424438 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303957, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303957 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7242647058823529, "acc_stderr": 0.027146271936625162, "acc_norm": 0.7242647058823529, "acc_norm_stderr": 0.027146271936625162 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000325, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000325 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578334, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578334 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.390452876376989, "mc1_stderr": 0.017078230743431455, "mc2": 0.5570225708371419, "mc2_stderr": 0.015617917882145785 }, "harness\|winogrande\|5": { "acc": 0.7884767166535123, "acc_stderr": 0.01147774768422318 }, "harness\|gsm8k\|5": { "acc": 0.6467020470053071, "acc_stderr": 0.013166337192115686 } } ``` ## 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]
suthawadee/receipt_th_4	--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 50296489.0 num_examples: 320 - name: validation num_bytes: 6665987.0 num_examples: 40 - name: test num_bytes: 6009751.0 num_examples: 40 download_size: 62106872 dataset_size: 62972227.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
flagship/rice-thermal-new_demo	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': RiceLeafs_BrownSpot '1': RiceLeafs_Healthy '2': RiceLeafs_Hispa '3': RiceLeafs_LeafBlast splits: - name: train num_bytes: 2607108.0 num_examples: 354 - name: test num_bytes: 944624.0 num_examples: 129 download_size: 3511150 dataset_size: 3551732.0 --- # Dataset Card for "rice-thermal-new_demo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
316usman/thematic5e-pw-embed-part4	--- dataset_info: features: - name: text dtype: string - name: document_url dtype: string - name: source_url dtype: string - name: country dtype: string splits: - name: train num_bytes: 265771303 num_examples: 405601 download_size: 102982484 dataset_size: 265771303 configs: - config_name: default data_files: - split: train path: data/train-* ---
arieg/cluster01_large_150	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 004097 '1': '005264' '2': '006674' '3': 009560 '4': '011764' '5': '016334' '6': 019707 '7': '025055' '8': '025601' '9': 026681 '10': 030488 '11': '032756' '12': 036388 '13': 036990 '14': '045516' '15': 047894 '16': '054152' '17': '054156' '18': 058543 '19': 059448 '20': 064093 '21': 064248 '22': '064520' '23': 064992 '24': 065683 '25': 068897 '26': 069781 '27': '071240' '28': '073171' '29': 074945 '30': '075314' '31': '076131' '32': 078841 '33': 081365 '34': 081565 '35': 084139 '36': 084141 '37': 085486 '38': 085492 '39': 087158 '40': 087187 '41': 087966 '42': 088960 '43': 089857 '44': 091900 '45': 093942 '46': 095452 '47': 096694 '48': 098550 '49': 098551 '50': 098552 '51': '101118' '52': '101868' '53': '107181' '54': '107851' '55': '108014' '56': '108303' '57': '108969' '58': '110171' '59': '111372' '60': '111398' '61': '111399' '62': '120178' '63': '121314' '64': '121415' '65': '121738' '66': '125188' '67': '126404' '68': '126489' '69': '126491' '70': '127204' '71': '129185' '72': '129372' '73': '130218' '74': '130950' '75': '130951' '76': '130954' '77': '131792' '78': '132434' '79': '137211' '80': '137900' '81': '141735' '82': '142082' '83': '144545' '84': '146685' '85': '148186' '86': '148211' '87': '148235' '88': '148532' '89': '149369' splits: - name: train num_bytes: 706895816.0 num_examples: 13500 download_size: 706947444 dataset_size: 706895816.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
alex-miller/oecd-dac-crs	--- language: - en - fr - es license: cc size_categories: - 1M<n<10M task_categories: - mask-generation pretty_name: OECD DAC CRS Project titles and descriptions dataset_info: features: - name: text dtype: string - name: Year dtype: int64 - name: ProjectNumber dtype: string - name: RecipientName dtype: string - name: RecipientCode dtype: int64 - name: DonorName dtype: string - name: DonorCode dtype: int64 - name: ProjectTitle dtype: string - name: SectorName dtype: string - name: PurposeName dtype: string - name: FlowName dtype: string - name: ShortDescription dtype: string - name: LongDescription dtype: string splits: - name: train num_bytes: 1643110932 num_examples: 1870757 download_size: 697495115 dataset_size: 1643110932 configs: - config_name: default data_files: - split: train path: data/train-* tags: - finance --- # OECD DAC CRS Project titles and descriptions All unique project titles and descriptions from the OECD DAC Creditor Reporting System (CRS). https://stats.oecd.org/Index.aspx?DataSetCode=crs1 `text` column is the concatenation of Project Title, Short Description, and Long Description, and is also the column on which duplicate projects were removed. Other columns are included for metadata purposes, or if you want to create a new text column as a concatenation of additional data.
Parikshith/grow-1-monolingual-ha-en-1_1m	--- dataset_info: features: - name: ha dtype: string - name: generated_text dtype: string splits: - name: train num_bytes: 265225402 num_examples: 1100000 download_size: 171871099 dataset_size: 265225402 configs: - config_name: default data_files: - split: train path: data/train-* ---
liuyanchen1015/MULTI_VALUE_cola_possessives_belong	--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 10611 num_examples: 117 - name: test num_bytes: 11076 num_examples: 127 - name: train num_bytes: 85151 num_examples: 983 download_size: 51915 dataset_size: 106838 --- # Dataset Card for "MULTI_VALUE_cola_possessives_belong" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mateusmeladogame/davidmelado	--- license: unknown ---
Jbrcoleman/fake-news	--- license: cc ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/eef0e7be	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1341 dataset_size: 182 --- # Dataset Card for "eef0e7be" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
garcianacho/human_genome_csv	--- license: apache-2.0 task_categories: - token-classification tags: - biology - genome - human genome - bioinformatics --- ## Human Genome Dataset Here is a human genome ready to be used to train LLM.
ShrinivasSK/hi_en_2	--- dataset_info: features: - name: idx dtype: int64 - name: tgt dtype: string - name: src dtype: string splits: - name: train num_bytes: 6376404.6 num_examples: 18000 - name: test num_bytes: 708489.4 num_examples: 2000 download_size: 3796444 dataset_size: 7084894.0 --- # Dataset Card for "hi_en_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_1	--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43766110 num_examples: 18928 - name: epoch_1 num_bytes: 44307023 num_examples: 18928 - name: epoch_2 num_bytes: 44359431 num_examples: 18928 - name: epoch_3 num_bytes: 44381054 num_examples: 18928 - name: epoch_4 num_bytes: 44389571 num_examples: 18928 - name: epoch_5 num_bytes: 44380600 num_examples: 18928 - name: epoch_6 num_bytes: 44375580 num_examples: 18928 - name: epoch_7 num_bytes: 44362518 num_examples: 18928 - name: epoch_8 num_bytes: 44361149 num_examples: 18928 - name: epoch_9 num_bytes: 44355512 num_examples: 18928 - name: epoch_10 num_bytes: 44355603 num_examples: 18928 - name: epoch_11 num_bytes: 44353930 num_examples: 18928 - name: epoch_12 num_bytes: 44354440 num_examples: 18928 - name: epoch_13 num_bytes: 44355243 num_examples: 18928 - name: epoch_14 num_bytes: 44353399 num_examples: 18928 - name: epoch_15 num_bytes: 44350140 num_examples: 18928 - name: epoch_16 num_bytes: 44353177 num_examples: 18928 - name: epoch_17 num_bytes: 44352975 num_examples: 18928 - name: epoch_18 num_bytes: 44350345 num_examples: 18928 - name: epoch_19 num_bytes: 44351723 num_examples: 18928 - name: epoch_20 num_bytes: 44349418 num_examples: 18928 - name: epoch_21 num_bytes: 44351470 num_examples: 18928 - name: epoch_22 num_bytes: 44351707 num_examples: 18928 - name: epoch_23 num_bytes: 44351088 num_examples: 18928 - name: epoch_24 num_bytes: 44350591 num_examples: 18928 - name: epoch_25 num_bytes: 44351243 num_examples: 18928 - name: epoch_26 num_bytes: 44350409 num_examples: 18928 - name: epoch_27 num_bytes: 44349426 num_examples: 18928 - name: epoch_28 num_bytes: 44348904 num_examples: 18928 - name: epoch_29 num_bytes: 44349202 num_examples: 18928 download_size: 700096785 dataset_size: 1330072981 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---
open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora	--- pretty_name: Evaluation run of Aratako/Beyonder-4x7B-random-lora dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aratako/Beyonder-4x7B-random-lora](https://huggingface.co/Aratako/Beyonder-4x7B-random-lora)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-02T20:16:42.836942](https://huggingface.co/datasets/open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora/blob/main/results_2024-04-02T20-16-42.836942.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\": 0.6524116122888561,\n\ \ \"acc_stderr\": 0.03209147540288013,\n \"acc_norm\": 0.6526918811196057,\n\ \ \"acc_norm_stderr\": 0.032750650646658046,\n \"mc1\": 0.5312117503059975,\n\ \ \"mc1_stderr\": 0.01746936487457753,\n \"mc2\": 0.7049250833848263,\n\ \ \"mc2_stderr\": 0.014693924406157995\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6868600682593856,\n \"acc_stderr\": 0.013552671543623496,\n\ \ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.01322671905626613\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6922923720374428,\n\ \ \"acc_stderr\": 0.004606015773125624,\n \"acc_norm\": 0.8740290778729337,\n\ \ \"acc_norm_stderr\": 0.0033113844981586364\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.04115324610336953,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.04115324610336953\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337124,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337124\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\ : 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"\ acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726855,\n \"\ acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726855\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218974,\n \"\ acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218974\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176085,\n \ \ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176085\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8385321100917431,\n \"acc_stderr\": 0.01577623925616323,\n \"\ acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.01577623925616323\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\ acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568603,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568603\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579654,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579654\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163025,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163025\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.03351953879521271,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.03351953879521271\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.38212290502793295,\n\ \ \"acc_stderr\": 0.016251139711570765,\n \"acc_norm\": 0.38212290502793295,\n\ \ \"acc_norm_stderr\": 0.016251139711570765\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.01275285834653313,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.01275285834653313\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5312117503059975,\n\ \ \"mc1_stderr\": 0.01746936487457753,\n \"mc2\": 0.7049250833848263,\n\ \ \"mc2_stderr\": 0.014693924406157995\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.01075935201485593\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6739954510993177,\n \ \ \"acc_stderr\": 0.012911675645682841\n }\n}\n```" repo_url: https://huggingface.co/Aratako/Beyonder-4x7B-random-lora leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|arc:challenge\|25_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-04-02T20-16-42.836942.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|gsm8k\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hellaswag\|10_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T20-16-42.836942.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-management\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|truthfulqa:mc\|0_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-04-02T20-16-42.836942.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '/details_harness\|winogrande\|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-04-02T20-16-42.836942.parquet' - config_name: results data_files: - split: 2024_04_02T20_16_42.836942 path: - results_2024-04-02T20-16-42.836942.parquet - split: latest path: - results_2024-04-02T20-16-42.836942.parquet --- # Dataset Card for Evaluation run of Aratako/Beyonder-4x7B-random-lora <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Aratako/Beyonder-4x7B-random-lora](https://huggingface.co/Aratako/Beyonder-4x7B-random-lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-02T20:16:42.836942](https://huggingface.co/datasets/open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora/blob/main/results_2024-04-02T20-16-42.836942.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": 0.6524116122888561, "acc_stderr": 0.03209147540288013, "acc_norm": 0.6526918811196057, "acc_norm_stderr": 0.032750650646658046, "mc1": 0.5312117503059975, "mc1_stderr": 0.01746936487457753, "mc2": 0.7049250833848263, "mc2_stderr": 0.014693924406157995 }, "harness\|arc:challenge\|25": { "acc": 0.6868600682593856, "acc_stderr": 0.013552671543623496, "acc_norm": 0.712457337883959, "acc_norm_stderr": 0.01322671905626613 }, "harness\|hellaswag\|10": { "acc": 0.6922923720374428, "acc_stderr": 0.004606015773125624, "acc_norm": 0.8740290778729337, "acc_norm_stderr": 0.0033113844981586364 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6518518518518519, "acc_stderr": 0.04115324610336953, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.04115324610336953 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337124, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337124 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4417989417989418, "acc_stderr": 0.025576257061253833, "acc_norm": 0.4417989417989418, "acc_norm_stderr": 0.025576257061253833 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7870967741935484, "acc_stderr": 0.02328766512726855, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726855 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8131313131313131, "acc_stderr": 0.027772533334218974, "acc_norm": 0.8131313131313131, "acc_norm_stderr": 0.027772533334218974 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.29259259259259257, "acc_stderr": 0.027738969632176085, "acc_norm": 0.29259259259259257, "acc_norm_stderr": 0.027738969632176085 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8385321100917431, "acc_stderr": 0.01577623925616323, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.01577623925616323 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5509259259259259, "acc_stderr": 0.03392238405321617, "acc_norm": 0.5509259259259259, "acc_norm_stderr": 0.03392238405321617 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8578431372549019, "acc_stderr": 0.024509803921568603, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.024509803921568603 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579654, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579654 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7520661157024794, "acc_stderr": 0.039418975265163025, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.039418975265163025 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7607361963190185, "acc_stderr": 0.03351953879521271, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.03351953879521271 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500097, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500097 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.38212290502793295, "acc_stderr": 0.016251139711570765, "acc_norm": 0.38212290502793295, "acc_norm_stderr": 0.016251139711570765 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600713, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600713 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47392438070404175, "acc_stderr": 0.01275285834653313, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.01275285834653313 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462937, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462937 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128448, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128448 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5312117503059975, "mc1_stderr": 0.01746936487457753, "mc2": 0.7049250833848263, "mc2_stderr": 0.014693924406157995 }, "harness\|winogrande\|5": { "acc": 0.8216258879242304, "acc_stderr": 0.01075935201485593 }, "harness\|gsm8k\|5": { "acc": 0.6739954510993177, "acc_stderr": 0.012911675645682841 } } ``` ## 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]
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/61f4b25b	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1340 dataset_size: 184 --- # Dataset Card for "61f4b25b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jmaciejowski/multi_news_tokenized_pegasus_large	--- dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 851951820 num_examples: 44972 - name: validation num_bytes: 104816611 num_examples: 5622 - name: test num_bytes: 106588998 num_examples: 5622 download_size: 537120663 dataset_size: 1063357429 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
Indic-LLM-Labs/CulturaX-Kn	--- language: - kn license: mit size_categories: - 1M<n<10M task_categories: - text-generation configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 10347179458 num_examples: 1352142 download_size: 3976072715 dataset_size: 10347179458 --- This is a filtered version of the [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset only containing samples of Kannada language. The dataset contains total of 1352142 samples. ### Dataset Structure: ```python { "text": ..., "timestamp": ..., "url": ..., "source": "mc4" \| "OSCAR-xxxx", } ``` ### Data Sample: ```python {'text': "ಭಟ್ಕಳ : ತಂದೆ ತಾಯಿ ಸ್ಮರಣಾರ್ಥ ; ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಣೆ \| Vartha Bharati- ವಾರ್ತಾ ಭಾರತಿ\nಮುದರಂಗಡಿ ಬಿಜೆಪಿ ಗ್ರಾಪಂ ಸದಸ್ಯರ ವಿರುದ್ಧ ಪ್ರತಿಭಟನೆ\nಹೋಮ್ ಕ್ವಾರಂಟೈನ್ ನಿಯಮ ಉಲ್ಲಂಘನೆ: ಪ್ರಕರಣ ದಾಖಲು\nಭಟ್ಕಳ : ತಂದೆ ತಾಯಿ ಸ್ಮರಣಾರ್ಥ ; ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಣೆ\nವಾರ್ತಾ ಭಾರತಿ Jun 19, 2019, 10:52 PM IST\nಭಟ್ಕಳ : ತಾಲೂಕಿನ ಹುರುಳಿಸಾಲಿನ ನಿವಾಸಿಗಳಾದ ವೃತ್ತಿಯಲ್ಲಿ ಶಿಕ್ಷಕರಾದ ವೆಂಕಟೇಶ ನಾರಾಯಣ ನಾಯ್ಕ ಪಟೇಲರಮನೆ ಇವರ ತಂದೆ ತಾಯಿಗಳ ಅಕಾಲಿಕ ಮರಣದಿಂದ ಅವರ ಮರಣ ದಿನದ ಸವಿನೆನಪಿಗಾಗಿ ಕಳೆದ 9 ವರ್ಷದಿಂದ ಇಲ್ಲಿನ ಶಾಲಾ ಮಕ್ಕಳಿಗೆ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸುತ್ತಾ ಬಂದಿದ್ದು, ಮಂಗಳವಾರದಂದು ಇಲ್ಲಿನ ಸರಕಾರಿ ಹಿರಿಯ ಪ್ರಾಥಮಿಕ ಶಾಲೆ ಮುಟ್ಟಳ್ಳಿಗೆ ತೆರಳಿ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ನೋಟ್ ವಿತರಿಸಿದರು.\nನೋಟ್ ಬುಕ್ ವಿತರಣೆ ಮಾಡಿ ಮಾತನಾಡಿದ ಶಿಕ್ಷಕ ವೆಂಕಟೇಶ ನಾಯ್ಕ 'ವಿದ್ಯಾರ್ಥಿಗಳ ಭವಿಷ್ಯದ ದಿಸೆಯಿಂದ ಹಾಗೂ ತಂದೆ-ತಾಯಿಗಳ ಸವಿನೆನಪಿಗಾಗಿ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸಲಾಗುತ್ತಿದೆ. ದುಡಿಮೆಯ ಒಂದು ಭಾಗವನ್ನು ಸಮಾಜಮುಖಿ ಕೆಲಕ್ಕೆ ಪ್ರತಿ ವರ್ಷ, ನನ್ನ ಮಡದಿ ಜಯಲಕ್ಷ್ಮೀ ನಾಯ್ಕ ಅವರ ಸಹಕಾರದಿಂದ ಕುಟುಂಬದವರ ಸಹಕಾರದಿಂದ ಈ ಕಾರ್ಯ ಮಾಡುತ್ತಿದ್ದೇನೆ. ಸಮಾಜದಲ್ಲಿ ಎಷ್ಟೇ ಎತ್ತರಕ್ಕೆ ಬೆಳೆದರು ತಂದೆತಾಯಿಗಳ ಹಾಗೂ ಗುರುಗಳ ಋಣ ತೀರಿಸಲು ಸಾಧ್ಯವಿಲ್ಲ. ನಾನು ಮಾಡಿದ ಕಾರ್ಯವನ್ನು ಮುಂದಿನ ದಿನದಲ್ಲಿ ದುಡಿಯುವ ವೇಳೆ ನಿಮ್ಮದಿಂದಾಗುವಷ್ಟು ಸಹಾಯ ಸೇವೆ ಮಾಡಿ ಎಂದು ಕರೆ ನೀಡಿದರು.\nನಂತರ ದಂತ ವೈದ್ಯರಾದ ಡಾ. ರವಿ ಮಾತನಾಡಿ ನಮ್ಮ ಸಮಾಜದಲ್ಲಿ ಇಂತಹ ವ್ಯಕ್ತಿಗಳಿರುವದರಿಂದ ನಮ್ಮ ಸಮಾಜವು ಏಳಿಗೆಯತ್ತ ಮುಖ ಮಾಡುತ್ತದೆ. ಮಕ್ಕಳಾದ ನಾವು ಎಲ್ಲೇ ಇರಿಬಹುದು ಹೇಗೆ ಇರಿಬಹುದ ಆದರೆ ತಂದೆ ತಾಯಿಗಳು ನಮಗೆ ಮಾಡಿರುವ ತ್ಯಾಗಕ್ಕೆ ನಾವು ಋಣ ತೀರಿಸಲು ಸಾಧ್ಯವಾಗದಿದ್ದರು ಇಂತಹ ಕೆಲಸ ಮಾಡಿ ಅವರ ತ್ಯಾಗಕ್ಕೆ ಪ್ರತಿಫಲ ಕೊಟ್ಟಂತೆ ಆಗುತ್ತದೆ ಅಂದು ಕಿವಿ ಮಾತನ್ನು ಮಕ್ಕಳಿಗೆ ಹೇಳಿದರು.\nಈ ಸಂಧರ್ಭದಲ್ಲಿ ಮುಟ್ಟಳ್ಳಿ ಶಾಲಾ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸಿದರು.\nಈಗಿನ ಇಲೆಕ್ಟ್ರಾನಿಕ ಜೀವನ ಶೈಲಿಯಲ್ಲಿ ಸಾಕಿದ ತಂದೆ ತಾಯಿಗಳನ್ನು ಅನಾಥಾಶ್ರಾಮಕ್ಕೊ ಅಥವಾ ದಾರಿಯ ಮೇಲೋ ಮನೆಯಿಂದ ಹೊರಗೆ ಹಾಕುವ ಮಕ್ಕಳ ನಡುವೆ ಅವರ ಅಕಾಲಿಕ ಮರಣದಿಂದ ನೊಂದು ಅವರ ಸವಿನೆನಪನ್ನು ಉತ್ತಮ ಕಾರ್ಯ ಮಾಡುವುದರೊಂದಿಗೆ ಸಾರ್ಥಕತೆಯನ್ನು ಮೆರೆದಿದ್ದಾರೆ.\nಈ ಸಂಧರ್ಭದಲ್ಲಿ ಶಾಲೆಯ ಎಸ್.ಡಿ. ಎಂ ಅಧ್ಯಕ್ಷರಾದ ವೆಂಕಟೇಶ ನಾಯ್ಕ, ರಾಜ್ಯ ಸರಕಾರಿ ನೌಕರರ ಸಂಘ ಸದಸ್ಯ ಬಿ.ಕೆ.ನಾಯ್ಕ, ಶಿಕ್ಷಕ ಸಿ.ಡಿ.ಪಡುವಣಿ, ಗಜಾನನ ನಾಯ್ಕ ಮುಖ್ಯ ಶಿಕ್ಷಕರು ವೆಂಕಟೇಶ್ ದೇವಡಿಗ್ ಶಿಕ್ಷಕರು ಉಪಸ್ಥಿತರಿದ್ದರು.", 'timestamp': '2020/07/07 13:00:41', 'url': 'http://www.varthabharati.in/article/karavali/196595', 'source': 'mC4'} ``` ### Use with Datasets ```python from datasets import load_dataset ds = load_dataset("Indic-LLM-Labs/CulturaX-Kn") ```
nmarafo/truthful_qa_TrueFalse	--- license: apache-2.0 task_categories: - table-question-answering language: - en --- # Dataset Card for Dataset Name This is a reduced variation of the truthful_qa dataset (https://huggingface.co/datasets/truthful_qa), modified to associate boolean values with the given answers, with a correct answer as a reference. ## 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] TruthfulQA: @misc{lin2021truthfulqa, title={TruthfulQA: Measuring How Models Mimic Human Falsehoods}, author={Stephanie Lin and Jacob Hilton and Owain Evans}, year={2021}, eprint={2109.07958}, archivePrefix={arXiv}, primaryClass={cs.CL} } 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]
Thundergb/Cesarbr	--- license: openrail ---
CyberHarem/charlotte_genshin	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of charlotte/シャルロット/夏洛蒂 (Genshin Impact) This is the dataset of charlotte/シャルロット/夏洛蒂 (Genshin Impact), containing 276 images and their tags. The core tags of this character are `pink_hair, hat, red_headwear, cabbie_hat, aqua_eyes, medium_hair, breasts, short_hair, 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 \| 276 \| 560.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_genshin/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 276 \| 461.92 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_genshin/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 710 \| 968.04 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/charlotte_genshin/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/charlotte_genshin', 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 \| 24 \| ![](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, monocle, solo, white_gloves, white_shirt, bare_shoulders, detached_sleeves, looking_at_viewer, long_sleeves, open_mouth, red_sleeves, upper_body, holding_camera, hat_feather, suspenders, white_background, :d, sleeveless_shirt, simple_background, bow, puffy_sleeves, upper_teeth_only, gem, blush \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, bare_shoulders, detached_sleeves, long_sleeves, looking_at_viewer, monocle, red_sleeves, simple_background, sleeveless_shirt, solo, white_background, white_shirt, white_gloves, blue_gemstone, hat_feather, skirt, suspenders, puffy_sleeves, white_belt, sideboob, thighs, blush, brooch, open_mouth, :d, thigh_strap, cowboy_shot, upper_body \| \| 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, bare_shoulders, hat_feather, holding_camera, monocle, simple_background, solo, upper_body, white_background, looking_at_viewer, smile, white_gloves, blush, green_eyes, open_mouth, detached_sleeves, long_sleeves \| \| 3 \| 8 \| ![](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, bare_shoulders, blue_gemstone, blue_sky, day, long_sleeves, monocle, outdoors, red_sleeves, sleeveless_shirt, solo, white_gloves, white_shirt, hat_feather, holding_camera, looking_at_viewer, open_mouth, cloud, suspenders, upper_body, blush, brooch, upper_teeth_only, :d, bow, puffy_detached_sleeves, white_belt, blurry_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| monocle \| solo \| white_gloves \| white_shirt \| bare_shoulders \| detached_sleeves \| looking_at_viewer \| long_sleeves \| open_mouth \| red_sleeves \| upper_body \| holding_camera \| hat_feather \| suspenders \| white_background \| :d \| sleeveless_shirt \| simple_background \| bow \| puffy_sleeves \| upper_teeth_only \| gem \| blush \| blue_gemstone \| skirt \| white_belt \| sideboob \| thighs \| brooch \| thigh_strap \| cowboy_shot \| smile \| green_eyes \| blue_sky \| day \| outdoors \| cloud \| puffy_detached_sleeves \| blurry_background \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:----------\|:-------\|:---------------\|:--------------\|:-----------------\|:-------------------\|:--------------------\|:---------------\|:-------------\|:--------------\|:-------------\|:-----------------\|:--------------\|:-------------\|:-------------------\|:-----\|:-------------------\|:--------------------\|:------\|:----------------\|:-------------------\|:------\|:--------\|:----------------\|:--------\|:-------------\|:-----------\|:---------\|:---------\|:--------------\|:--------------\|:--------\|:-------------\|:-----------\|:------\|:-----------\|:--------\|:-------------------------\|:--------------------\| \| 0 \| 24 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| \| X \| \| \| X \| X \| 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 \| \| \| \| \| \| \| \| 3 \| 8 \| ![](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 \| X \| X \| X \|
open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1	--- pretty_name: Evaluation run of Sao10K/Franziska-Mixtral-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Sao10K/Franziska-Mixtral-v1](https://huggingface.co/Sao10K/Franziska-Mixtral-v1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-31T21:01:34.382668](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1/blob/main/results_2024-03-31T21-01-34.382668.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\": 0.6978344149015431,\n\ \ \"acc_stderr\": 0.030588664141069415,\n \"acc_norm\": 0.7011436233589318,\n\ \ \"acc_norm_stderr\": 0.03118141743792802,\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.7006977726617969,\n\ \ \"mc2_stderr\": 0.014781576553666215\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688065,\n\ \ \"acc_norm\": 0.7175767918088737,\n \"acc_norm_stderr\": 0.013155456884097222\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6922923720374428,\n\ \ \"acc_stderr\": 0.004606015773125624,\n \"acc_norm\": 0.8737303326030671,\n\ \ \"acc_norm_stderr\": 0.0033147420770833183\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.033176727875331574,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.033176727875331574\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.769811320754717,\n \"acc_stderr\": 0.02590789712240817,\n\ \ \"acc_norm\": 0.769811320754717,\n \"acc_norm_stderr\": 0.02590789712240817\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8194444444444444,\n\ \ \"acc_stderr\": 0.03216600808802269,\n \"acc_norm\": 0.8194444444444444,\n\ \ \"acc_norm_stderr\": 0.03216600808802269\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7109826589595376,\n\ \ \"acc_stderr\": 0.03456425745086999,\n \"acc_norm\": 0.7109826589595376,\n\ \ \"acc_norm_stderr\": 0.03456425745086999\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6510638297872341,\n \"acc_stderr\": 0.031158522131357787,\n\ \ \"acc_norm\": 0.6510638297872341,\n \"acc_norm_stderr\": 0.031158522131357787\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\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\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8516129032258064,\n\ \ \"acc_stderr\": 0.020222737554330378,\n \"acc_norm\": 0.8516129032258064,\n\ \ \"acc_norm_stderr\": 0.020222737554330378\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.03465304488406796,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.03465304488406796\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\ : 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695482995,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695482995\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8585858585858586,\n \"acc_stderr\": 0.02482590979334333,\n \"\ acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.02482590979334333\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n\ \ \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.026265024608275882,\n\ \ \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.026265024608275882\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.44370860927152317,\n \"acc_stderr\": 0.04056527902281732,\n \"\ acc_norm\": 0.44370860927152317,\n \"acc_norm_stderr\": 0.04056527902281732\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8678899082568807,\n \"acc_stderr\": 0.014517801914598238,\n \"\ acc_norm\": 0.8678899082568807,\n \"acc_norm_stderr\": 0.014517801914598238\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n \"\ acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \ \ \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7219730941704036,\n\ \ \"acc_stderr\": 0.030069584874494043,\n \"acc_norm\": 0.7219730941704036,\n\ \ \"acc_norm_stderr\": 0.030069584874494043\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.0349814938546247,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.0349814938546247\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8425925925925926,\n\ \ \"acc_stderr\": 0.03520703990517963,\n \"acc_norm\": 0.8425925925925926,\n\ \ \"acc_norm_stderr\": 0.03520703990517963\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.0349260647662379,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.0349260647662379\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.019875655027867443,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.019875655027867443\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8773946360153256,\n\ \ \"acc_stderr\": 0.011728672144131565,\n \"acc_norm\": 0.8773946360153256,\n\ \ \"acc_norm_stderr\": 0.011728672144131565\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7803468208092486,\n \"acc_stderr\": 0.022289638852617887,\n\ \ \"acc_norm\": 0.7803468208092486,\n \"acc_norm_stderr\": 0.022289638852617887\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.49050279329608937,\n\ \ \"acc_stderr\": 0.016719484643348752,\n \"acc_norm\": 0.49050279329608937,\n\ \ \"acc_norm_stderr\": 0.016719484643348752\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8104575163398693,\n \"acc_stderr\": 0.02244235826333621,\n\ \ \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.02244235826333621\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.77491961414791,\n\ \ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\ \ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.020736358408060006,\n\ \ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.020736358408060006\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5417209908735332,\n\ \ \"acc_stderr\": 0.012725701656953642,\n \"acc_norm\": 0.5417209908735332,\n\ \ \"acc_norm_stderr\": 0.012725701656953642\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7720588235294118,\n \"acc_stderr\": 0.025483081468029804,\n\ \ \"acc_norm\": 0.7720588235294118,\n \"acc_norm_stderr\": 0.025483081468029804\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7418300653594772,\n \"acc_stderr\": 0.017704531653250068,\n \ \ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.017704531653250068\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866764,\n\ \ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866764\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8706467661691543,\n\ \ \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n\ \ \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276894,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276894\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.7006977726617969,\n\ \ \"mc2_stderr\": 0.014781576553666215\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8089976322020521,\n \"acc_stderr\": 0.011047808761510423\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6027293404094011,\n \ \ \"acc_stderr\": 0.013478659652337792\n }\n}\n```" repo_url: https://huggingface.co/Sao10K/Franziska-Mixtral-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|arc:challenge\|25_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-31T21-01-34.382668.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|gsm8k\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hellaswag\|10_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-31T21-01-34.382668.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-31T21-01-34.382668.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '/details_harness\|winogrande\|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-31T21-01-34.382668.parquet' - config_name: results data_files: - split: 2024_03_31T21_01_34.382668 path: - results_2024-03-31T21-01-34.382668.parquet - split: latest path: - results_2024-03-31T21-01-34.382668.parquet --- # Dataset Card for Evaluation run of Sao10K/Franziska-Mixtral-v1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Sao10K/Franziska-Mixtral-v1](https://huggingface.co/Sao10K/Franziska-Mixtral-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-31T21:01:34.382668](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1/blob/main/results_2024-03-31T21-01-34.382668.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": 0.6978344149015431, "acc_stderr": 0.030588664141069415, "acc_norm": 0.7011436233589318, "acc_norm_stderr": 0.03118141743792802, "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.7006977726617969, "mc2_stderr": 0.014781576553666215 }, "harness\|arc:challenge\|25": { "acc": 0.6877133105802048, "acc_stderr": 0.013542598541688065, "acc_norm": 0.7175767918088737, "acc_norm_stderr": 0.013155456884097222 }, "harness\|hellaswag\|10": { "acc": 0.6922923720374428, "acc_stderr": 0.004606015773125624, "acc_norm": 0.8737303326030671, "acc_norm_stderr": 0.0033147420770833183 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7894736842105263, "acc_stderr": 0.033176727875331574, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.033176727875331574 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.769811320754717, "acc_stderr": 0.02590789712240817, "acc_norm": 0.769811320754717, "acc_norm_stderr": 0.02590789712240817 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8194444444444444, "acc_stderr": 0.03216600808802269, "acc_norm": 0.8194444444444444, "acc_norm_stderr": 0.03216600808802269 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7109826589595376, "acc_stderr": 0.03456425745086999, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.03456425745086999 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6510638297872341, "acc_stderr": 0.031158522131357787, "acc_norm": 0.6510638297872341, "acc_norm_stderr": 0.031158522131357787 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "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": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8516129032258064, "acc_stderr": 0.020222737554330378, "acc_norm": 0.8516129032258064, "acc_norm_stderr": 0.020222737554330378 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.03465304488406796, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.03465304488406796 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695482995, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695482995 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8585858585858586, "acc_stderr": 0.02482590979334333, "acc_norm": 0.8585858585858586, "acc_norm_stderr": 0.02482590979334333 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9481865284974094, "acc_stderr": 0.01599622932024412, "acc_norm": 0.9481865284974094, "acc_norm_stderr": 0.01599622932024412 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206865, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206865 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7941176470588235, "acc_stderr": 0.026265024608275882, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.026265024608275882 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.44370860927152317, "acc_stderr": 0.04056527902281732, "acc_norm": 0.44370860927152317, "acc_norm_stderr": 0.04056527902281732 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8678899082568807, "acc_stderr": 0.014517801914598238, "acc_norm": 0.8678899082568807, "acc_norm_stderr": 0.014517801914598238 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5879629629629629, "acc_stderr": 0.03356787758160831, "acc_norm": 0.5879629629629629, "acc_norm_stderr": 0.03356787758160831 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8649789029535865, "acc_stderr": 0.022245776632003694, "acc_norm": 0.8649789029535865, "acc_norm_stderr": 0.022245776632003694 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7219730941704036, "acc_stderr": 0.030069584874494043, "acc_norm": 0.7219730941704036, "acc_norm_stderr": 0.030069584874494043 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8015267175572519, "acc_stderr": 0.0349814938546247, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.0349814938546247 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8760330578512396, "acc_stderr": 0.030083098716035202, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.030083098716035202 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8425925925925926, "acc_stderr": 0.03520703990517963, "acc_norm": 0.8425925925925926, "acc_norm_stderr": 0.03520703990517963 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7791411042944786, "acc_stderr": 0.03259177392742178, "acc_norm": 0.7791411042944786, "acc_norm_stderr": 0.03259177392742178 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.5357142857142857, "acc_stderr": 0.04733667890053756, "acc_norm": 0.5357142857142857, "acc_norm_stderr": 0.04733667890053756 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8543689320388349, "acc_stderr": 0.0349260647662379, "acc_norm": 0.8543689320388349, "acc_norm_stderr": 0.0349260647662379 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8974358974358975, "acc_stderr": 0.019875655027867443, "acc_norm": 0.8974358974358975, "acc_norm_stderr": 0.019875655027867443 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8773946360153256, "acc_stderr": 0.011728672144131565, "acc_norm": 0.8773946360153256, "acc_norm_stderr": 0.011728672144131565 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7803468208092486, "acc_stderr": 0.022289638852617887, "acc_norm": 0.7803468208092486, "acc_norm_stderr": 0.022289638852617887 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.49050279329608937, "acc_stderr": 0.016719484643348752, "acc_norm": 0.49050279329608937, "acc_norm_stderr": 0.016719484643348752 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8104575163398693, "acc_stderr": 0.02244235826333621, "acc_norm": 0.8104575163398693, "acc_norm_stderr": 0.02244235826333621 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.77491961414791, "acc_stderr": 0.023720088516179027, "acc_norm": 0.77491961414791, "acc_norm_stderr": 0.023720088516179027 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8333333333333334, "acc_stderr": 0.020736358408060006, "acc_norm": 0.8333333333333334, "acc_norm_stderr": 0.020736358408060006 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5035460992907801, "acc_stderr": 0.02982674915328092, "acc_norm": 0.5035460992907801, "acc_norm_stderr": 0.02982674915328092 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5417209908735332, "acc_stderr": 0.012725701656953642, "acc_norm": 0.5417209908735332, "acc_norm_stderr": 0.012725701656953642 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7720588235294118, "acc_stderr": 0.025483081468029804, "acc_norm": 0.7720588235294118, "acc_norm_stderr": 0.025483081468029804 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7418300653594772, "acc_stderr": 0.017704531653250068, "acc_norm": 0.7418300653594772, "acc_norm_stderr": 0.017704531653250068 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302505, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302505 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866764, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866764 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8706467661691543, "acc_stderr": 0.02372983088101853, "acc_norm": 0.8706467661691543, "acc_norm_stderr": 0.02372983088101853 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8713450292397661, "acc_stderr": 0.025679342723276894, "acc_norm": 0.8713450292397661, "acc_norm_stderr": 0.025679342723276894 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.7006977726617969, "mc2_stderr": 0.014781576553666215 }, "harness\|winogrande\|5": { "acc": 0.8089976322020521, "acc_stderr": 0.011047808761510423 }, "harness\|gsm8k\|5": { "acc": 0.6027293404094011, "acc_stderr": 0.013478659652337792 } } ``` ## 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]
mrachilles/NTU60PointsComplete	--- license: mit ---
toilaluan/reward_tuned_prompt_v1	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: model_type dtype: string - name: request_id dtype: int64 - name: topic dtype: string - name: reward dtype: float64 - name: individual_rewards struct: - name: clip_aesthetic_rewarder dtype: float64 - name: pick_rewarder dtype: float64 - name: image_rewarder dtype: float64 - name: hps_v2_rewarder dtype: float64 splits: - name: train num_bytes: 463200 num_examples: 4500 download_size: 160093 dataset_size: 463200 --- # Dataset Card for "reward_tuned_prompt_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mrachilles/ntu60SkeletonPoint

--- license: mit ---

Nadav/pixel_glue_qqp

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': '0' '1': '1' splits: - name: train num_bytes: 4725063877.25 num_examples: 363846 - name: validation num_bytes: 525056314.25 num_examples: 40430 download_size: 5039025536 dataset_size: 5250120191.5 --- # Dataset Card for "pixel_glue_qqp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

RobbeD/csgo-texture-patterns-1024

--- dataset_info: features: - name: description dtype: string - name: finish_style dtype: string - name: weapon dtype: string - name: skin dtype: string - name: finish_catalog dtype: int64 - name: flavor_text dtype: string - name: mask_image dtype: image - name: ao_image dtype: image - name: conditioning_image dtype: image - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1220768222.0 num_examples: 556 download_size: 629049265 dataset_size: 1220768222.0 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "csgo-texture-patterns-1024" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/ukm_2000_girlsfrontline

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of ukm_2000/UKM-2000/UKM-2000 (Girls' Frontline) This is the dataset of ukm_2000/UKM-2000/UKM-2000 (Girls' Frontline), containing 23 images and their tags. The core tags of this character are `long_hair, pink_hair, breasts, bangs, red_eyes, hat, pink_eyes, very_long_hair, 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 | 23 | 31.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 23 | 17.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 51 | 34.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 23 | 27.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 51 | 50.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ukm_2000_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/ukm_2000_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, solo, looking_at_viewer, open_jacket, long_sleeves, black_gloves, blush, white_jacket, animal_hood, gun, sitting, smile, white_background, black_leotard, boots, closed_mouth, fingerless_gloves, hood_up, hooded_jacket, sidelocks, simple_background, skindentation, thigh_strap, thighhighs | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | looking_at_viewer, 1girl, black_bikini, solo, hair_bun, navel, pink_headwear, thigh_strap, baseball_cap, black_gloves, fingerless_gloves, see-through, single_thighhigh, stomach, white_shirt, bare_shoulders, collarbone, crop_top, fishnet_thighhighs, simple_background, standing, thighs, white_background, black_choker, cowboy_shot, large_breasts, off-shoulder_shirt, short_sleeves, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | open_jacket | long_sleeves | black_gloves | blush | white_jacket | animal_hood | gun | sitting | smile | white_background | black_leotard | boots | closed_mouth | fingerless_gloves | hood_up | hooded_jacket | sidelocks | simple_background | skindentation | thigh_strap | thighhighs | black_bikini | hair_bun | navel | pink_headwear | baseball_cap | see-through | single_thighhigh | stomach | white_shirt | bare_shoulders | collarbone | crop_top | fishnet_thighhighs | standing | thighs | black_choker | cowboy_shot | large_breasts | off-shoulder_shirt | short_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:---------------|:---------------|:--------|:---------------|:--------------|:------|:----------|:--------|:-------------------|:----------------|:--------|:---------------|:--------------------|:----------|:----------------|:------------|:--------------------|:----------------|:--------------|:-------------|:---------------|:-----------|:--------|:----------------|:---------------|:--------------|:-------------------|:----------|:--------------|:-----------------|:-------------|:-----------|:---------------------|:-----------|:---------|:---------------|:--------------|:----------------|:---------------------|:----------------| | 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 | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | X | | | | | | X | X | | | | X | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

ElTucuGardella/PaolaX

--- license: unknown ---

GBaker/MedQA-USMLE-4-options-hf

--- license: cc-by-sa-4.0 --- Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large) <h4>Citation information:</h4> @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} }

tyzhu/squad_qa_wrong_rare_v5_full_no_permute

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: answer dtype: string - name: context_id dtype: string - name: correct_id dtype: string - name: inputs dtype: string - name: targets dtype: string splits: - name: train num_bytes: 7639879.229800348 num_examples: 4778 - name: validation num_bytes: 349767 num_examples: 300 download_size: 1200683 dataset_size: 7989646.229800348 --- # Dataset Card for "squad_qa_wrong_rare_v5_full_no_permute" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

m-rousseau/oqa-v1

--- license: apache-2.0 ---

Sourabh2/Daily_Conversation_Hinglish

--- dataset_info: features: - name: seed dtype: string - name: question dtype: string - name: question_raw dtype: string - name: answer dtype: string splits: - name: train num_bytes: 1271518 num_examples: 1786 download_size: 258915 dataset_size: 1271518 configs: - config_name: default data_files: - split: train path: data/train-* ---

AdapterOcean/chemistry_dataset_standardized_cluster_1_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: 6207644 num_examples: 5502 download_size: 2530400 dataset_size: 6207644 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "chemistry_dataset_standardized_cluster_1_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

elements-dev/hq_portrait_sdxl_subset

--- dataset_info: features: - name: image dtype: image - name: blip2 dtype: string - name: foreground_canny_edge_image dtype: image splits: - name: train num_bytes: 26844364.0 num_examples: 100 download_size: 25919375 dataset_size: 26844364.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

huggingartists/rex-orange-county

--- language: - en tags: - huggingartists - lyrics --- # Dataset Card for "huggingartists/rex-orange-county" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [About](#about) ## Dataset Description - **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of the generated dataset:** 0.116278 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/348ad82a8d34eaff777b6743ca0f2d70.400x400x1.jpg')"> </div> </div> <a href="https://huggingface.co/huggingartists/rex-orange-county"> <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">Rex Orange County</div> <a href="https://genius.com/artists/rex-orange-county"> <div style="text-align: center; font-size: 14px;">@rex-orange-county</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/rex-orange-county). ### 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/rex-orange-county") ``` ## 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| |------:|---------:|---:| |66| -| -| '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/rex-orange-county") 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)

irds/lotte_pooled_dev_forum

--- pretty_name: '`lotte/pooled/dev/forum`' viewer: false source_datasets: ['irds/lotte_pooled_dev'] task_categories: - text-retrieval --- # Dataset Card for `lotte/pooled/dev/forum` The `lotte/pooled/dev/forum` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/pooled/dev/forum). # Data This dataset provides: - `queries` (i.e., topics); count=10,097 - `qrels`: (relevance assessments); count=68,685 - For `docs`, use [`irds/lotte_pooled_dev`](https://huggingface.co/datasets/irds/lotte_pooled_dev) ## Usage ```python from datasets import load_dataset queries = load_dataset('irds/lotte_pooled_dev_forum', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} qrels = load_dataset('irds/lotte_pooled_dev_forum', 'qrels') for record in qrels: record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @article{Santhanam2021ColBERTv2, title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia", journal= "arXiv preprint arXiv:2112.01488", year = "2021", url = "https://arxiv.org/abs/2112.01488" } ```

AarushSah/scibowl-synthetic

--- license: apache-2.0 language: - en tags: - chemistry - biology - earth science - claude - claude 3 opus - synthetic - astronomy - general science - physics pretty_name: S size_categories: - 1K<n<10K --- # Scibowl-synthetic (v0.1) ## Dataset Description Scibowl-synthetic is a dataset of science bowl questions that have been answered by the Claude 3 Opus language model. The dataset is designed to provide a high-quality resource for teaching science concepts to language models. ## Dataset Summary - **Repository:** https://huggingface.co/datasets/AarushSah/scibowl-synthetic/ - **Point of Contact:** Aarush Sah ## Dataset Composition The dataset consists of 5,046 text-based examples. Each example includes a science bowl question, the expected answer, the answer provided by Claude, the thought process of Claude (wrapped in `<thinking>` tags), and the final response (wrapped in `<answer>` tags). ## Data Collection Process The science bowl questions were collected by downloading all sample questions from the High School (HS) level. The questions were then parsed using Meta Nougat, cleaned, and processed through the Claude 3 Opus language model. The following system prompt was used to generate the answers: ``` You will answer the question posed by the user step by step, with detailed reasoning explaining how you arrived at that answer. Think before you answer, and explain your thought process. Make sure you are factual and evidence based. Double check your responses. Wrap your thought process in <thinking> tags. Wrap your final response in <answer> tags. Within the answer tags, only put the answer, no reasoning. ``` ## Dataset Structure Each example in the dataset is represented as a JSON object with the following fields: - `question`: The science bowl question. - `expected`: The expected answer to the question. - `answer`: The answer provided by Claude. - `thinking`: Claude's thought process, wrapped in `<thinking>` tags. - `response`: The complete response from Claude, including the thought process and final answer. - `correct`: A boolean value indicating whether Claude's answer matches the expected answer. Note that this value is not fully reliable and may contain false negatives. ## Dataset Splits Currently, there are no predefined splits for the dataset. Users can create their own splits as needed. ## Dataset Use The Scibowl-synthetic dataset can be used for various purposes, such as: - Fine-tuning language models on science-related tasks. - Evaluating the performance of language models on answering science questions. - Studying the reasoning and thought processes of language models. ## Potential Issues and Biases As the answers in the dataset are generated by the Claude 3 Opus language model, they may be subject to the biases and limitations inherent in the model. Users should be aware of these potential biases when using the dataset. Additionally, the `correct` boolean value in the dataset is not fully reliable and may contain false negatives. Users should exercise caution when using this value for evaluation purposes. ## Licensing Information The Scibowl-synthetic dataset is released under the Apache 2.0 license. ## Citation Information If you use this dataset in your research or projects, please cite it as follows: ```bibtex @misc{Scibowl-synthetic, title = {Scibowl-synthetic: A Dataset of Science Bowl Questions Answered by Claude 3 Opus}, author = {Aarush Sah}, year = {2024}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/AarushSah/scibowl-synthetic/} } ``` ## Contributions If you have any suggestions, improvements, or additional data to contribute, please open an issue or submit a pull request in the dataset repository.

burglarhobbit/temp-cultura-x

--- license: apache-2.0 dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string - name: source dtype: string - name: text_en_tr dtype: string - name: text_gu_rm dtype: string splits: - name: train num_bytes: 29478 num_examples: 3 download_size: 54682 dataset_size: 29478 configs: - config_name: default data_files: - split: train path: data/train-* ---

davidgaofc/pairwise_setup

--- license: mit dataset_info: features: - name: Question dtype: string - name: SFT dtype: string - name: Base_PPO dtype: string - name: Prima_PPO dtype: string splits: - name: train num_bytes: 914772 num_examples: 1640 download_size: 264717 dataset_size: 914772 configs: - config_name: default data_files: - split: train path: data/train-* ---

zZWipeoutZz/rogue_style

--- license: creativeml-openrail-m --- <h4> Usage </h4> To use this embedding you have to download the file and put it into the "\stable-diffusion-webui\embeddings" folder To use it in a prompt add <em style="font-weight:600">art by rogue_style </em> add <b>[ ]</b> around it to reduce its weight. <h4> Included Files </h4> <ul> <li>500 steps <em>Usage: art by rogue_style-500</em></li> <li>3500 steps <em>Usage: art by rogue_style-3500</em></li> <li>6500 steps <em>Usage: art by rogue_style</em> </li> </ul> cheers<br> Wipeout <h4> Example Pictures </h4> <table> <tbody> <tr> <td><img height="100%/" width="100%" src="https://i.imgur.com/JefZ3cA.png"></td> <td><img height="100%/" width="100%" src="https://i.imgur.com/YBJzVIi.png"></td> <td><img height="100%/" width="100%" src="https://i.imgur.com/96iutfu.png"></td> <td><img height="100%/" width="100%" src="https://i.imgur.com/SBKfnc4.png"></td> </tr> </tbody> </table> <h4> prompt comparison </h4> <em> click the image to enlarge</em> <a href="https://i.imgur.com/a6te4zG.png" target="_blank"><img height="50%" width="50%" src="https://i.imgur.com/a6te4zG.png"></a>

davanstrien/ner-test

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: string - name: ner_tags sequence: string - name: tokens sequence: string splits: - name: train num_bytes: 1548186 num_examples: 5216 - name: valid num_bytes: 392764 num_examples: 1304 download_size: 0 dataset_size: 1940950 --- # Dataset Card for "ner-test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

edbeeching/gia-dataset-tokenized-debug2

--- dataset_info: config_name: atari-alien features: - name: input_types sequence: int64 - name: input_ids sequence: int32 - name: patches sequence: sequence: sequence: sequence: uint8 - name: loss_mask sequence: bool - name: patch_positions sequence: sequence: sequence: float64 - name: local_positions sequence: int64 - name: attention_mask sequence: bool splits: - name: test num_bytes: 442153668 num_examples: 335 download_size: 35972017 dataset_size: 442153668 configs: - config_name: atari-alien data_files: - split: test path: atari-alien/test-* --- # Dataset Card for "gia-dataset-tokenized-debug2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HuggingFaceM4/gradio_dope_data_points_test

Invalid username or password.

CyberHarem/kashin_koji_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Kashin Koji/果心居士 (Fate/Grand Order) This is the dataset of Kashin Koji/果心居士 (Fate/Grand Order), containing 34 images and their tags. The core tags of this character are `heterochromia, long_hair, multicolored_hair, red_eyes, white_hair, black_hair, bangs, grey_hair, two-tone_hair, hair_ornament, green_eyes, twintails, very_long_hair, blue_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 34 | 72.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 34 | 33.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 83 | 73.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 34 | 58.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 83 | 115.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kashin_koji_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/kashin_koji_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, parted_lips, split-color_hair, red_gloves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | parted_lips | split-color_hair | red_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------|:-------------------|:-------------| | 0 | 25 | ![](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 |

eunbinni/ola_llama2_13B_t1_data

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 691281335 num_examples: 580812 download_size: 399933748 dataset_size: 691281335 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ola_llama2_13B_t1_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/weser_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of weser/ヴェーザー/威悉 (Azur Lane) This is the dataset of weser/ヴェーザー/威悉 (Azur Lane), containing 28 images and their tags. The core tags of this character are `breasts, red_hair, short_hair, red_eyes, bangs, large_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 | 28 | 32.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 28 | 21.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 60 | 39.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 28 | 30.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 60 | 51.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/weser_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/weser_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, looking_at_viewer, solo, bare_shoulders, cleavage, official_alternate_costume, smile, thigh_strap, black_dress, covered_navel | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, jacket, long_sleeves, looking_at_viewer, thigh_boots, necktie, black_footwear, black_thighhighs, choker, dress, high_heel_boots, sitting, smile | | 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, bare_shoulders, crop_top, looking_at_viewer, midriff, navel, solo, collarbone, off-shoulder_shirt, open_mouth, simple_background, stomach, white_background, :o, blush, cowboy_shot, jeans, long_sleeves, standing, alternate_costume, belt, black_shirt, blue_pants, cleavage, hair_between_eyes, hand_up, holding, parted_lips | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | bare_shoulders | cleavage | official_alternate_costume | smile | thigh_strap | black_dress | covered_navel | jacket | long_sleeves | thigh_boots | necktie | black_footwear | black_thighhighs | choker | dress | high_heel_boots | sitting | crop_top | midriff | navel | collarbone | off-shoulder_shirt | open_mouth | simple_background | stomach | white_background | :o | blush | cowboy_shot | jeans | standing | alternate_costume | belt | black_shirt | blue_pants | hair_between_eyes | hand_up | holding | parted_lips | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------|:-----------|:-----------------------------|:--------|:--------------|:--------------|:----------------|:---------|:---------------|:--------------|:----------|:-----------------|:-------------------|:---------|:--------|:------------------|:----------|:-----------|:----------|:--------|:-------------|:---------------------|:-------------|:--------------------|:----------|:-------------------|:-----|:--------|:--------------|:--------|:-----------|:--------------------|:-------|:--------------|:-------------|:--------------------|:----------|:----------|:--------------| | 0 | 12 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 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 |

carslab/life_coach_athletes

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 1234000 num_examples: 5000 download_size: 15528 dataset_size: 1234000 configs: - config_name: default data_files: - split: train path: data/train-* ---

felipesampaio/gumballjoaovg

--- license: openrail ---

JovialValley/phoneme_totaldataset_4

--- dataset_info: features: - name: name dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: label dtype: string - name: emotion dtype: string - name: emotion_str dtype: string splits: - name: train num_bytes: 164035246.0 num_examples: 390 - name: test num_bytes: 40309237.0 num_examples: 97 download_size: 137553091 dataset_size: 204344483.0 --- # Dataset Card for "phoneme_totaldataset_4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/bbf77e22

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1332 dataset_size: 182 --- # Dataset Card for "bbf77e22" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

GalaktischeGurke/invoices_instruct_vf

--- dataset_info: features: - name: ground_truth dtype: string splits: - name: train num_bytes: 911760 num_examples: 501 download_size: 364209 dataset_size: 911760 --- # Dataset Card for "invoices_instruct_vf" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

heliosprime/twitter_dataset_1713187618

--- 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: 16759 num_examples: 45 download_size: 17172 dataset_size: 16759 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "twitter_dataset_1713187618" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bgspaditya/byt-mal-minpro

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: url dtype: string - name: type dtype: string - name: type_code dtype: int64 splits: - name: train num_bytes: 43302335.10276401 num_examples: 520952 - name: val num_bytes: 5412791.887845501 num_examples: 65119 - name: test num_bytes: 5412875.009390486 num_examples: 65120 download_size: 32733332 dataset_size: 54128002.0 --- # Dataset Card for "byt-mal-minpro" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

BangumiBase/akibameidosensou

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Akiba Meido Sensou This is the image base of bangumi Akiba Meido Sensou, we detected 48 characters, 2198 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 87 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 185 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 39 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 70 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 169 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 314 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 29 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 16 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 28 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 24 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 37 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 21 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 60 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 31 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 35 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 158 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 16 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 13 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 9 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 12 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 33 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 85 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 34 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 9 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 10 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 8 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 10 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 23 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 21 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 38 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | 30 | 6 | [Download](30/dataset.zip) | ![preview 1](30/preview_1.png) | ![preview 2](30/preview_2.png) | ![preview 3](30/preview_3.png) | ![preview 4](30/preview_4.png) | ![preview 5](30/preview_5.png) | ![preview 6](30/preview_6.png) | N/A | N/A | | 31 | 7 | [Download](31/dataset.zip) | ![preview 1](31/preview_1.png) | ![preview 2](31/preview_2.png) | ![preview 3](31/preview_3.png) | ![preview 4](31/preview_4.png) | ![preview 5](31/preview_5.png) | ![preview 6](31/preview_6.png) | ![preview 7](31/preview_7.png) | N/A | | 32 | 9 | [Download](32/dataset.zip) | ![preview 1](32/preview_1.png) | ![preview 2](32/preview_2.png) | ![preview 3](32/preview_3.png) | ![preview 4](32/preview_4.png) | ![preview 5](32/preview_5.png) | ![preview 6](32/preview_6.png) | ![preview 7](32/preview_7.png) | ![preview 8](32/preview_8.png) | | 33 | 16 | [Download](33/dataset.zip) | ![preview 1](33/preview_1.png) | ![preview 2](33/preview_2.png) | ![preview 3](33/preview_3.png) | ![preview 4](33/preview_4.png) | ![preview 5](33/preview_5.png) | ![preview 6](33/preview_6.png) | ![preview 7](33/preview_7.png) | ![preview 8](33/preview_8.png) | | 34 | 11 | [Download](34/dataset.zip) | ![preview 1](34/preview_1.png) | ![preview 2](34/preview_2.png) | ![preview 3](34/preview_3.png) | ![preview 4](34/preview_4.png) | ![preview 5](34/preview_5.png) | ![preview 6](34/preview_6.png) | ![preview 7](34/preview_7.png) | ![preview 8](34/preview_8.png) | | 35 | 15 | [Download](35/dataset.zip) | ![preview 1](35/preview_1.png) | ![preview 2](35/preview_2.png) | ![preview 3](35/preview_3.png) | ![preview 4](35/preview_4.png) | ![preview 5](35/preview_5.png) | ![preview 6](35/preview_6.png) | ![preview 7](35/preview_7.png) | ![preview 8](35/preview_8.png) | | 36 | 7 | [Download](36/dataset.zip) | ![preview 1](36/preview_1.png) | ![preview 2](36/preview_2.png) | ![preview 3](36/preview_3.png) | ![preview 4](36/preview_4.png) | ![preview 5](36/preview_5.png) | ![preview 6](36/preview_6.png) | ![preview 7](36/preview_7.png) | N/A | | 37 | 28 | [Download](37/dataset.zip) | ![preview 1](37/preview_1.png) | ![preview 2](37/preview_2.png) | ![preview 3](37/preview_3.png) | ![preview 4](37/preview_4.png) | ![preview 5](37/preview_5.png) | ![preview 6](37/preview_6.png) | ![preview 7](37/preview_7.png) | ![preview 8](37/preview_8.png) | | 38 | 136 | [Download](38/dataset.zip) | ![preview 1](38/preview_1.png) | ![preview 2](38/preview_2.png) | ![preview 3](38/preview_3.png) | ![preview 4](38/preview_4.png) | ![preview 5](38/preview_5.png) | ![preview 6](38/preview_6.png) | ![preview 7](38/preview_7.png) | ![preview 8](38/preview_8.png) | | 39 | 14 | [Download](39/dataset.zip) | ![preview 1](39/preview_1.png) | ![preview 2](39/preview_2.png) | ![preview 3](39/preview_3.png) | ![preview 4](39/preview_4.png) | ![preview 5](39/preview_5.png) | ![preview 6](39/preview_6.png) | ![preview 7](39/preview_7.png) | ![preview 8](39/preview_8.png) | | 40 | 9 | [Download](40/dataset.zip) | ![preview 1](40/preview_1.png) | ![preview 2](40/preview_2.png) | ![preview 3](40/preview_3.png) | ![preview 4](40/preview_4.png) | ![preview 5](40/preview_5.png) | ![preview 6](40/preview_6.png) | ![preview 7](40/preview_7.png) | ![preview 8](40/preview_8.png) | | 41 | 9 | [Download](41/dataset.zip) | ![preview 1](41/preview_1.png) | ![preview 2](41/preview_2.png) | ![preview 3](41/preview_3.png) | ![preview 4](41/preview_4.png) | ![preview 5](41/preview_5.png) | ![preview 6](41/preview_6.png) | ![preview 7](41/preview_7.png) | ![preview 8](41/preview_8.png) | | 42 | 22 | [Download](42/dataset.zip) | ![preview 1](42/preview_1.png) | ![preview 2](42/preview_2.png) | ![preview 3](42/preview_3.png) | ![preview 4](42/preview_4.png) | ![preview 5](42/preview_5.png) | ![preview 6](42/preview_6.png) | ![preview 7](42/preview_7.png) | ![preview 8](42/preview_8.png) | | 43 | 10 | [Download](43/dataset.zip) | ![preview 1](43/preview_1.png) | ![preview 2](43/preview_2.png) | ![preview 3](43/preview_3.png) | ![preview 4](43/preview_4.png) | ![preview 5](43/preview_5.png) | ![preview 6](43/preview_6.png) | ![preview 7](43/preview_7.png) | ![preview 8](43/preview_8.png) | | 44 | 11 | [Download](44/dataset.zip) | ![preview 1](44/preview_1.png) | ![preview 2](44/preview_2.png) | ![preview 3](44/preview_3.png) | ![preview 4](44/preview_4.png) | ![preview 5](44/preview_5.png) | ![preview 6](44/preview_6.png) | ![preview 7](44/preview_7.png) | ![preview 8](44/preview_8.png) | | 45 | 5 | [Download](45/dataset.zip) | ![preview 1](45/preview_1.png) | ![preview 2](45/preview_2.png) | ![preview 3](45/preview_3.png) | ![preview 4](45/preview_4.png) | ![preview 5](45/preview_5.png) | N/A | N/A | N/A | | 46 | 7 | [Download](46/dataset.zip) | ![preview 1](46/preview_1.png) | ![preview 2](46/preview_2.png) | ![preview 3](46/preview_3.png) | ![preview 4](46/preview_4.png) | ![preview 5](46/preview_5.png) | ![preview 6](46/preview_6.png) | ![preview 7](46/preview_7.png) | N/A | | noise | 252 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

autoevaluate/autoeval-eval-tweet_eval-emotion-dbaa98-66233145581

--- type: predictions tags: - autotrain - evaluation datasets: - tweet_eval eval_info: task: multi_class_classification model: FelixHonikker/bert-emotion metrics: [] dataset_name: tweet_eval dataset_config: emotion dataset_split: train col_mapping: text: text 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: Multi-class Text Classification * Model: FelixHonikker/bert-emotion * Dataset: tweet_eval * Config: emotion * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@Ayushkm2799](https://huggingface.co/Ayushkm2799) for evaluating this model.

deepapaikar/Katzbot_SC_pairs_2col

--- license: apache-2.0 ---

matthewlqin/cleaned

--- dataset_info: features: - name: image dtype: image - name: text sequence: string splits: - name: train num_bytes: 789717609.75 num_examples: 3322 download_size: 395091356 dataset_size: 789717609.75 --- # Dataset Card for "cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Harsh-7300/english_to_french

--- license: mit dataset_card: H@rsh7300 language: - en - fr task_categories: - translation pretty_name: dataset3 size_categories: - 1K<n<10K tags: - legal --- # Dataset Card for Dataset Name  This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description  - **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]

ewhfef/pubmed-10k

--- task_categories: - text-generation ---

NathanRoll/TalkBank_CA_Bergmann

--- dataset_info: features: - name: audio sequence: float32 - name: __index_level_0__ dtype: string splits: - name: train num_bytes: 170179623 num_examples: 83 download_size: 169288052 dataset_size: 170179623 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "TalkBank_CA_Bergmann" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/saeki_sayaka_yagatekimininaru

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of Saeki Sayaka This is the dataset of Saeki Sayaka, containing 129 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 | 129 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 304 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | raw-stage3-eyes | 349 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. | | 384x512 | 129 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x704 | 129 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x880 | 129 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 304 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 304 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-p512-640 | 248 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. | | stage3-eyes-640 | 349 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. | | stage3-eyes-800 | 349 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |

XAC-Moon/MOON

--- license: apache-2.0 ---

LangChainDatasets/openapi-chain-klarna-products-get

--- license: mit ---

WillHeld/phl_accent_cv

--- dataset_info: features: - name: client_id dtype: string - name: path dtype: string - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accents dtype: string - name: variant dtype: 'null' - name: locale dtype: string - name: segment dtype: 'null' - name: label dtype: int64 - name: embed sequence: float64 - name: audio dtype: audio splits: - name: train num_bytes: 762096970.116 num_examples: 4287 download_size: 724530999 dataset_size: 762096970.116 configs: - config_name: default data_files: - split: train path: data/train-* ---

Garydesu/RStest

--- dataset_info: features: - name: image sequence: sequence: sequence: uint8 - name: label sequence: sequence: uint8 splits: - name: train num_bytes: 1393870128 num_examples: 166 download_size: 438686388 dataset_size: 1393870128 ---

Eitanli/rewrite_instructions_bu

--- dataset_info: features: - name: id dtype: int64 - name: recipe dtype: string - name: instructions dtype: string splits: - name: train num_bytes: 160548334 num_examples: 74401 download_size: 81393986 dataset_size: 160548334 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "rewrite_instructions_bu" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

wbensvage/clothes_desc

--- license: apache-2.0 annotations_creators: - human generated by using detail_desc and color language: - en language_creators: - other multilinguality: - monolingual pretty_name: 'H&M Clothes captions' size_categories: - n=1K source_datasets: - www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations tags: [] task_categories: - text-to-image task_ids: [] --- # Dataset Card for H&M Clothes captions _Dataset used to train/finetune [Clothes text to image model] Captions are generated by using the 'detail_desc' and 'colour_group_name' or 'perceived_colour_master_name' from kaggle/competitions/h-and-m-personalized-fashion-recommendations. Original images were also obtained from the url (https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/data?select=images) For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided. ---

Nikutka/L1_poleval_korpus_wzorcowy

--- dataset_info: features: - name: content dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 20564 num_examples: 253 - name: test num_bytes: 1963 num_examples: 25 download_size: 18165 dataset_size: 22527 --- # Dataset Card for "L1_poleval_korpus_wzorcowy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_jeff31415__TinyLlama-1.1B-1T-OpenOrca

--- pretty_name: Evaluation run of jeff31415/TinyLlama-1.1B-1T-OpenOrca dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [jeff31415/TinyLlama-1.1B-1T-OpenOrca](https://huggingface.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca)\ \ 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_jeff31415__TinyLlama-1.1B-1T-OpenOrca\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-09T19:50:28.018627](https://huggingface.co/datasets/open-llm-leaderboard/details_jeff31415__TinyLlama-1.1B-1T-OpenOrca/blob/main/results_2024-03-09T19-50-28.018627.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\": 0.25775935350308826,\n\ \ \"acc_stderr\": 0.030862569052274226,\n \"acc_norm\": 0.2586688775762298,\n\ \ \"acc_norm_stderr\": 0.03162013433693599,\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871114,\n \"mc2\": 0.38577014111881996,\n\ \ \"mc2_stderr\": 0.014141938216925623\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.2909556313993174,\n \"acc_stderr\": 0.013273077865907576,\n\ \ \"acc_norm\": 0.31313993174061433,\n \"acc_norm_stderr\": 0.013552671543623497\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4082852021509659,\n\ \ \"acc_stderr\": 0.004905119039849457,\n \"acc_norm\": 0.523401712806214,\n\ \ \"acc_norm_stderr\": 0.004984313205791438\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \ \ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3037037037037037,\n\ \ \"acc_stderr\": 0.03972552884785136,\n \"acc_norm\": 0.3037037037037037,\n\ \ \"acc_norm_stderr\": 0.03972552884785136\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.03317672787533157,\n\ \ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.03317672787533157\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.33,\n\ \ \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \ \ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.23018867924528302,\n \"acc_stderr\": 0.025907897122408173,\n\ \ \"acc_norm\": 0.23018867924528302,\n \"acc_norm_stderr\": 0.025907897122408173\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.2152777777777778,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \ \ \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774709\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n\ \ \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \ \ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.21965317919075145,\n\ \ \"acc_stderr\": 0.031568093627031744,\n \"acc_norm\": 0.21965317919075145,\n\ \ \"acc_norm_stderr\": 0.031568093627031744\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383888,\n\ \ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383888\n\ \ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\ : {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.028659179374292323,\n\ \ \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.028659179374292323\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\ \ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\ \ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135303,\n\ \ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135303\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.25396825396825395,\n \"acc_stderr\": 0.022418042891113942,\n \"\ acc_norm\": 0.25396825396825395,\n \"acc_norm_stderr\": 0.022418042891113942\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.20634920634920634,\n\ \ \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n\ \ \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24516129032258063,\n\ \ \"acc_stderr\": 0.02447224384089553,\n \"acc_norm\": 0.24516129032258063,\n\ \ \"acc_norm_stderr\": 0.02447224384089553\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.03144712581678242,\n\ \ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.03144712581678242\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\"\ : 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.03401506715249039,\n\ \ \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.03401506715249039\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.2222222222222222,\n \"acc_stderr\": 0.029620227874790465,\n \"\ acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.029620227874790465\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.20725388601036268,\n \"acc_stderr\": 0.02925282329180363,\n\ \ \"acc_norm\": 0.20725388601036268,\n \"acc_norm_stderr\": 0.02925282329180363\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.2153846153846154,\n \"acc_stderr\": 0.020843034557462878,\n\ \ \"acc_norm\": 0.2153846153846154,\n \"acc_norm_stderr\": 0.020843034557462878\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2777777777777778,\n \"acc_stderr\": 0.027309140588230182,\n \ \ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230182\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.23949579831932774,\n \"acc_stderr\": 0.027722065493361276,\n\ \ \"acc_norm\": 0.23949579831932774,\n \"acc_norm_stderr\": 0.027722065493361276\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"\ acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.20917431192660552,\n \"acc_stderr\": 0.017437937173343233,\n \"\ acc_norm\": 0.20917431192660552,\n \"acc_norm_stderr\": 0.017437937173343233\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.37037037037037035,\n \"acc_stderr\": 0.03293377139415191,\n \"\ acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.03293377139415191\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.20588235294117646,\n \"acc_stderr\": 0.028379449451588674,\n \"\ acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.028379449451588674\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \ \ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.35874439461883406,\n\ \ \"acc_stderr\": 0.03219079200419996,\n \"acc_norm\": 0.35874439461883406,\n\ \ \"acc_norm_stderr\": 0.03219079200419996\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.2824427480916031,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.2824427480916031,\n \"acc_norm_stderr\": 0.03948406125768361\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.2396694214876033,\n \"acc_stderr\": 0.038968789850704164,\n \"\ acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.038968789850704164\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\ \ \"acc_stderr\": 0.04236511258094634,\n \"acc_norm\": 0.25925925925925924,\n\ \ \"acc_norm_stderr\": 0.04236511258094634\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.034878251684978906,\n\ \ \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.034878251684978906\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.25,\n\ \ \"acc_stderr\": 0.04109974682633932,\n \"acc_norm\": 0.25,\n \ \ \"acc_norm_stderr\": 0.04109974682633932\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\ \ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.24358974358974358,\n\ \ \"acc_stderr\": 0.028120966503914404,\n \"acc_norm\": 0.24358974358974358,\n\ \ \"acc_norm_stderr\": 0.028120966503914404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2720306513409962,\n\ \ \"acc_stderr\": 0.01591336744750052,\n \"acc_norm\": 0.2720306513409962,\n\ \ \"acc_norm_stderr\": 0.01591336744750052\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.023176298203992016,\n\ \ \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.023176298203992016\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.26256983240223464,\n\ \ \"acc_stderr\": 0.014716824273017761,\n \"acc_norm\": 0.26256983240223464,\n\ \ \"acc_norm_stderr\": 0.014716824273017761\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.238562091503268,\n \"acc_stderr\": 0.024404394928087866,\n\ \ \"acc_norm\": 0.238562091503268,\n \"acc_norm_stderr\": 0.024404394928087866\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2347266881028939,\n\ \ \"acc_stderr\": 0.024071805887677045,\n \"acc_norm\": 0.2347266881028939,\n\ \ \"acc_norm_stderr\": 0.024071805887677045\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.2932098765432099,\n \"acc_stderr\": 0.025329888171900915,\n\ \ \"acc_norm\": 0.2932098765432099,\n \"acc_norm_stderr\": 0.025329888171900915\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180844,\n \ \ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180844\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23663624511082137,\n\ \ \"acc_stderr\": 0.010855137351572742,\n \"acc_norm\": 0.23663624511082137,\n\ \ \"acc_norm_stderr\": 0.010855137351572742\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.1801470588235294,\n \"acc_stderr\": 0.02334516361654486,\n\ \ \"acc_norm\": 0.1801470588235294,\n \"acc_norm_stderr\": 0.02334516361654486\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.2581699346405229,\n \"acc_stderr\": 0.017704531653250068,\n \ \ \"acc_norm\": 0.2581699346405229,\n \"acc_norm_stderr\": 0.017704531653250068\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2545454545454545,\n\ \ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.2545454545454545,\n\ \ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.24897959183673468,\n \"acc_stderr\": 0.02768297952296023,\n\ \ \"acc_norm\": 0.24897959183673468,\n \"acc_norm_stderr\": 0.02768297952296023\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\ \ \"acc_stderr\": 0.030147775935409224,\n \"acc_norm\": 0.23880597014925373,\n\ \ \"acc_norm_stderr\": 0.030147775935409224\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2710843373493976,\n\ \ \"acc_stderr\": 0.03460579907553027,\n \"acc_norm\": 0.2710843373493976,\n\ \ \"acc_norm_stderr\": 0.03460579907553027\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.30409356725146197,\n \"acc_stderr\": 0.03528211258245232,\n\ \ \"acc_norm\": 0.30409356725146197,\n \"acc_norm_stderr\": 0.03528211258245232\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23623011015911874,\n\ \ \"mc1_stderr\": 0.014869755015871114,\n \"mc2\": 0.38577014111881996,\n\ \ \"mc2_stderr\": 0.014141938216925623\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.5824782951854776,\n \"acc_stderr\": 0.013859978264440251\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.016679302501895376,\n \ \ \"acc_stderr\": 0.0035275958887224465\n }\n}\n```" repo_url: https://huggingface.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|arc:challenge|25_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-09T19-50-28.018627.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|gsm8k|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hellaswag|10_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-50-28.018627.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-09T19-50-28.018627.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|truthfulqa:mc|0_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-09T19-50-28.018627.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_09T19_50_28.018627 path: - '**/details_harness|winogrande|5_2024-03-09T19-50-28.018627.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-09T19-50-28.018627.parquet' - config_name: results data_files: - split: 2024_03_09T19_50_28.018627 path: - results_2024-03-09T19-50-28.018627.parquet - split: latest path: - results_2024-03-09T19-50-28.018627.parquet --- # Dataset Card for Evaluation run of jeff31415/TinyLlama-1.1B-1T-OpenOrca  Dataset automatically created during the evaluation run of model [jeff31415/TinyLlama-1.1B-1T-OpenOrca](https://huggingface.co/jeff31415/TinyLlama-1.1B-1T-OpenOrca) 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_jeff31415__TinyLlama-1.1B-1T-OpenOrca", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-09T19:50:28.018627](https://huggingface.co/datasets/open-llm-leaderboard/details_jeff31415__TinyLlama-1.1B-1T-OpenOrca/blob/main/results_2024-03-09T19-50-28.018627.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": 0.25775935350308826, "acc_stderr": 0.030862569052274226, "acc_norm": 0.2586688775762298, "acc_norm_stderr": 0.03162013433693599, "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871114, "mc2": 0.38577014111881996, "mc2_stderr": 0.014141938216925623 }, "harness|arc:challenge|25": { "acc": 0.2909556313993174, "acc_stderr": 0.013273077865907576, "acc_norm": 0.31313993174061433, "acc_norm_stderr": 0.013552671543623497 }, "harness|hellaswag|10": { "acc": 0.4082852021509659, "acc_stderr": 0.004905119039849457, "acc_norm": 0.523401712806214, "acc_norm_stderr": 0.004984313205791438 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.27, "acc_stderr": 0.04461960433384739, "acc_norm": 0.27, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3037037037037037, "acc_stderr": 0.03972552884785136, "acc_norm": 0.3037037037037037, "acc_norm_stderr": 0.03972552884785136 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.21052631578947367, "acc_stderr": 0.03317672787533157, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.03317672787533157 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.33, "acc_stderr": 0.047258156262526045, "acc_norm": 0.33, "acc_norm_stderr": 0.047258156262526045 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.23018867924528302, "acc_stderr": 0.025907897122408173, "acc_norm": 0.23018867924528302, "acc_norm_stderr": 0.025907897122408173 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2152777777777778, "acc_stderr": 0.03437079344106135, "acc_norm": 0.2152777777777778, "acc_norm_stderr": 0.03437079344106135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.16, "acc_stderr": 0.03684529491774709, "acc_norm": 0.16, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.21965317919075145, "acc_stderr": 0.031568093627031744, "acc_norm": 0.21965317919075145, "acc_norm_stderr": 0.031568093627031744 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383888, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383888 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.25957446808510637, "acc_stderr": 0.028659179374292323, "acc_norm": 0.25957446808510637, "acc_norm_stderr": 0.028659179374292323 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.24561403508771928, "acc_stderr": 0.04049339297748141, "acc_norm": 0.24561403508771928, "acc_norm_stderr": 0.04049339297748141 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2413793103448276, "acc_stderr": 0.03565998174135303, "acc_norm": 0.2413793103448276, "acc_norm_stderr": 0.03565998174135303 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.25396825396825395, "acc_stderr": 0.022418042891113942, "acc_norm": 0.25396825396825395, "acc_norm_stderr": 0.022418042891113942 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.20634920634920634, "acc_stderr": 0.036196045241242515, "acc_norm": 0.20634920634920634, "acc_norm_stderr": 0.036196045241242515 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.21, "acc_stderr": 0.04093601807403326, "acc_norm": 0.21, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.24516129032258063, "acc_stderr": 0.02447224384089553, "acc_norm": 0.24516129032258063, "acc_norm_stderr": 0.02447224384089553 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.27586206896551724, "acc_stderr": 0.03144712581678242, "acc_norm": 0.27586206896551724, "acc_norm_stderr": 0.03144712581678242 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.2545454545454545, "acc_stderr": 0.03401506715249039, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.03401506715249039 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.2222222222222222, "acc_stderr": 0.029620227874790465, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.029620227874790465 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.20725388601036268, "acc_stderr": 0.02925282329180363, "acc_norm": 0.20725388601036268, "acc_norm_stderr": 0.02925282329180363 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2153846153846154, "acc_stderr": 0.020843034557462878, "acc_norm": 0.2153846153846154, "acc_norm_stderr": 0.020843034557462878 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.027309140588230182, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.027309140588230182 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.23949579831932774, "acc_stderr": 0.027722065493361276, "acc_norm": 0.23949579831932774, "acc_norm_stderr": 0.027722065493361276 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2185430463576159, "acc_stderr": 0.03374235550425694, "acc_norm": 0.2185430463576159, "acc_norm_stderr": 0.03374235550425694 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.20917431192660552, "acc_stderr": 0.017437937173343233, "acc_norm": 0.20917431192660552, "acc_norm_stderr": 0.017437937173343233 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.03293377139415191, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.03293377139415191 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.20588235294117646, "acc_stderr": 0.028379449451588674, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.028379449451588674 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.25738396624472576, "acc_stderr": 0.028458820991460302, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.028458820991460302 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.35874439461883406, "acc_stderr": 0.03219079200419996, "acc_norm": 0.35874439461883406, "acc_norm_stderr": 0.03219079200419996 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2824427480916031, "acc_stderr": 0.03948406125768361, "acc_norm": 0.2824427480916031, "acc_norm_stderr": 0.03948406125768361 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2396694214876033, "acc_stderr": 0.038968789850704164, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.038968789850704164 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.25925925925925924, "acc_stderr": 0.04236511258094634, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.04236511258094634 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.26993865030674846, "acc_stderr": 0.034878251684978906, "acc_norm": 0.26993865030674846, "acc_norm_stderr": 0.034878251684978906 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.25, "acc_stderr": 0.04109974682633932, "acc_norm": 0.25, "acc_norm_stderr": 0.04109974682633932 }, "harness|hendrycksTest-management|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness|hendrycksTest-marketing|5": { "acc": 0.24358974358974358, "acc_stderr": 0.028120966503914404, "acc_norm": 0.24358974358974358, "acc_norm_stderr": 0.028120966503914404 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2720306513409962, "acc_stderr": 0.01591336744750052, "acc_norm": 0.2720306513409962, "acc_norm_stderr": 0.01591336744750052 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24566473988439305, "acc_stderr": 0.023176298203992016, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.023176298203992016 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.26256983240223464, "acc_stderr": 0.014716824273017761, "acc_norm": 0.26256983240223464, "acc_norm_stderr": 0.014716824273017761 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.238562091503268, "acc_stderr": 0.024404394928087866, "acc_norm": 0.238562091503268, "acc_norm_stderr": 0.024404394928087866 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2347266881028939, "acc_stderr": 0.024071805887677045, "acc_norm": 0.2347266881028939, "acc_norm_stderr": 0.024071805887677045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.2932098765432099, "acc_stderr": 0.025329888171900915, "acc_norm": 0.2932098765432099, "acc_norm_stderr": 0.025329888171900915 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.25886524822695034, "acc_stderr": 0.026129572527180844, "acc_norm": 0.25886524822695034, "acc_norm_stderr": 0.026129572527180844 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.23663624511082137, "acc_stderr": 0.010855137351572742, "acc_norm": 0.23663624511082137, "acc_norm_stderr": 0.010855137351572742 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.1801470588235294, "acc_stderr": 0.02334516361654486, "acc_norm": 0.1801470588235294, "acc_norm_stderr": 0.02334516361654486 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.2581699346405229, "acc_stderr": 0.017704531653250068, "acc_norm": 0.2581699346405229, "acc_norm_stderr": 0.017704531653250068 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2545454545454545, "acc_stderr": 0.041723430387053825, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.041723430387053825 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.24897959183673468, "acc_stderr": 0.02768297952296023, "acc_norm": 0.24897959183673468, "acc_norm_stderr": 0.02768297952296023 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23880597014925373, "acc_stderr": 0.030147775935409224, "acc_norm": 0.23880597014925373, "acc_norm_stderr": 0.030147775935409224 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-virology|5": { "acc": 0.2710843373493976, "acc_stderr": 0.03460579907553027, "acc_norm": 0.2710843373493976, "acc_norm_stderr": 0.03460579907553027 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.30409356725146197, "acc_stderr": 0.03528211258245232, "acc_norm": 0.30409356725146197, "acc_norm_stderr": 0.03528211258245232 }, "harness|truthfulqa:mc|0": { "mc1": 0.23623011015911874, "mc1_stderr": 0.014869755015871114, "mc2": 0.38577014111881996, "mc2_stderr": 0.014141938216925623 }, "harness|winogrande|5": { "acc": 0.5824782951854776, "acc_stderr": 0.013859978264440251 }, "harness|gsm8k|5": { "acc": 0.016679302501895376, "acc_stderr": 0.0035275958887224465 } } ``` ## 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]

noahzhy/lpr_data

--- license: mit language: - ko pretty_name: l size_categories: - 100K<n<1M ---

yuri-no/openbookqa-ITA

--- dataset_info: features: - name: id dtype: string - name: question_stem dtype: string - name: choices struct: - name: label sequence: string - name: text sequence: string - name: answerKey dtype: string - name: fact1 dtype: string - name: humanScore dtype: float64 - name: clarity dtype: float64 splits: - name: test num_bytes: 142808 num_examples: 500 download_size: 78813 dataset_size: 142808 configs: - config_name: default data_files: - split: test path: data/test-* task_categories: - question-answering language: - it size_categories: - n<1K --- ## Dataset Details This is an Italian translated version of the **test only** dataset [allenai/openbookqa](https://huggingface.co/datasets/allenai/openbookqa). The dataset was translated using the Palm 2 Google API.

liuyanchen1015/MULTI_VALUE_qqp_reflex_number

--- dataset_info: features: - name: question1 dtype: string - name: question2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 65737 num_examples: 383 - name: test num_bytes: 685215 num_examples: 4019 - name: train num_bytes: 609260 num_examples: 3544 download_size: 722259 dataset_size: 1360212 --- # Dataset Card for "MULTI_VALUE_qqp_reflex_number" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Joe02/Belm_style_refs

--- license: other ---

joey234/medmcqa-rule-neg

--- dataset_info: features: - name: id dtype: string - name: opa dtype: string - name: opb dtype: string - name: opc dtype: string - name: opd dtype: string - name: cop dtype: class_label: names: '0': a '1': b '2': c '3': d - name: choice_type dtype: string - name: exp dtype: string - name: subject_name dtype: string - name: topic_name dtype: string - name: question dtype: string splits: - name: test num_bytes: 1417364 num_examples: 6150 - name: validation num_bytes: 2233369 num_examples: 4183 download_size: 2422050 dataset_size: 3650733 --- # Dataset Card for "medmcqa-rule-neg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ulangi/Gtzan

--- license: apache-2.0 ---

AlienKevin/LIHKG

--- license: mit language: - yue pretty_name: 連登 size_categories: - 1M<n<10M --- Scraped conversations of the LIHKG forum. Content scraped by Ayaka: https://github.com/ayaka14732/lihkg-scraper

hardikch05/Text-to-sql-v1-custom-1000

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 422792 num_examples: 1000 download_size: 158487 dataset_size: 422792 configs: - config_name: default data_files: - split: train path: data/train-* ---

vwxyzjn/summarize_from_feedback_oai_preprocessing_1711138793

--- dataset_info: features: - name: info struct: - name: id dtype: string - name: post dtype: string - name: title dtype: string - name: subreddit dtype: string - name: site dtype: string - name: article dtype: string - name: summaries list: - name: text dtype: string - name: policy dtype: string - name: note dtype: string - name: choice dtype: int32 - name: worker dtype: string - name: batch dtype: string - name: split dtype: string - name: extra struct: - name: confidence dtype: int32 - name: query_token sequence: int64 - name: query dtype: string - name: chosen dtype: string - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: rejected dtype: string - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 - name: chosen_policy dtype: string - name: rejected_policy dtype: string - name: policies dtype: string - name: chosen_len_minus_rejected_len dtype: int64 - name: query_chosen dtype: string - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: query_rejected dtype: string - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: query_token_len dtype: int64 - name: query_chosen_token_response_label sequence: int64 - name: query_rejected_token_response_label sequence: int64 splits: - name: train num_bytes: 3160687523 num_examples: 92858 - name: validation num_bytes: 2859977775 num_examples: 83802 - name: validation_cnndm num_bytes: 225375023 num_examples: 2284 download_size: 291050539 dataset_size: 6246040321 --- # Dataset Card for "summarize_from_feedback_oai_preprocessing_1711138793" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0

--- pretty_name: Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)\ \ 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_upstage__SOLAR-10.7B-Instruct-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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\": 0.6657586984797939,\n\ \ \"acc_stderr\": 0.03165995758526614,\n \"acc_norm\": 0.6666511531376961,\n\ \ \"acc_norm_stderr\": 0.0323050384069596,\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.013621696119173307,\n\ \ \"acc_norm\": 0.7107508532423208,\n \"acc_norm_stderr\": 0.01325001257939344\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7070304720175263,\n\ \ \"acc_stderr\": 0.004541944342035901,\n \"acc_norm\": 0.8815972913762199,\n\ \ \"acc_norm_stderr\": 0.003224240722351317\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361072,\n\ \ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361072\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.02872750295788027,\n\ \ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.02872750295788027\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.44,\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\"\ : 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n\ \ \"acc_norm_stderr\": 0.05021167315686779\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\": 0.6647398843930635,\n\ \ \"acc_stderr\": 0.03599586301247077,\n \"acc_norm\": 0.6647398843930635,\n\ \ \"acc_norm_stderr\": 0.03599586301247077\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236785,\n\ \ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236785\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\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130726,\n \"\ acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130726\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8032258064516129,\n \"acc_stderr\": 0.022616409420742025,\n \"\ acc_norm\": 0.8032258064516129,\n \"acc_norm_stderr\": 0.022616409420742025\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\ : 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721175,\n \ \ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721175\n \ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8737373737373737,\n \"acc_stderr\": 0.02366435940288023,\n \"\ acc_norm\": 0.8737373737373737,\n \"acc_norm_stderr\": 0.02366435940288023\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\ \ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3814814814814815,\n \"acc_stderr\": 0.029616718927497593,\n \ \ \"acc_norm\": 0.3814814814814815,\n \"acc_norm_stderr\": 0.029616718927497593\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \ \ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\ acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\ acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.0251956584289318,\n \"acc_norm\"\ : 0.8480392156862745,\n \"acc_norm_stderr\": 0.0251956584289318\n },\n\ \ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\ \ 0.8565400843881856,\n \"acc_stderr\": 0.022818291821017012,\n \"\ acc_norm\": 0.8565400843881856,\n \"acc_norm_stderr\": 0.022818291821017012\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\ \ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\ \ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\ \ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\ acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\ \ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\ \ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\ \ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\ \ \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n\ \ \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\ \ \"acc_stderr\": 0.02280138253459753,\n \"acc_norm\": 0.8589743589743589,\n\ \ \"acc_norm_stderr\": 0.02280138253459753\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8033205619412516,\n\ \ \"acc_stderr\": 0.014214138556913917,\n \"acc_norm\": 0.8033205619412516,\n\ \ \"acc_norm_stderr\": 0.014214138556913917\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123567,\n\ \ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123567\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39329608938547483,\n\ \ \"acc_stderr\": 0.016337268694270112,\n \"acc_norm\": 0.39329608938547483,\n\ \ \"acc_norm_stderr\": 0.016337268694270112\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\ \ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n\ \ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\ \ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7901234567901234,\n \"acc_stderr\": 0.02265834408598137,\n\ \ \"acc_norm\": 0.7901234567901234,\n \"acc_norm_stderr\": 0.02265834408598137\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4934810951760104,\n\ \ \"acc_stderr\": 0.012769150688867503,\n \"acc_norm\": 0.4934810951760104,\n\ \ \"acc_norm_stderr\": 0.012769150688867503\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103135,\n\ \ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103135\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6911764705882353,\n \"acc_stderr\": 0.018690850273595294,\n \ \ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.018690850273595294\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\ \ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\ \ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\ \ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n\ \ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5667074663402693,\n\ \ \"mc1_stderr\": 0.017347024450107485,\n \"mc2\": 0.7142943510205136,\n\ \ \"mc2_stderr\": 0.015024530295000761\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8358326756116812,\n \"acc_stderr\": 0.01041084977522279\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6474601971190296,\n \ \ \"acc_stderr\": 0.013159909755930337\n }\n}\n```" repo_url: https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0 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_13T21_02_33.929144 path: - '**/details_harness|arc:challenge|25_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-13T21-02-33.929144.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|gsm8k|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hellaswag|10_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T21-02-33.929144.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|truthfulqa:mc|0_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-13T21-02-33.929144.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_13T21_02_33.929144 path: - '**/details_harness|winogrande|5_2023-12-13T21-02-33.929144.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-13T21-02-33.929144.parquet' - config_name: results data_files: - split: 2023_12_13T21_02_33.929144 path: - results_2023-12-13T21-02-33.929144.parquet - split: latest path: - results_2023-12-13T21-02-33.929144.parquet --- # Dataset Card for Evaluation run of upstage/SOLAR-10.7B-Instruct-v1.0  Dataset automatically created during the evaluation run of model [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) 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_upstage__SOLAR-10.7B-Instruct-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-13T21:02:33.929144](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__SOLAR-10.7B-Instruct-v1.0/blob/main/results_2023-12-13T21-02-33.929144.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": 0.6657586984797939, "acc_stderr": 0.03165995758526614, "acc_norm": 0.6666511531376961, "acc_norm_stderr": 0.0323050384069596, "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness|arc:challenge|25": { "acc": 0.6808873720136519, "acc_stderr": 0.013621696119173307, "acc_norm": 0.7107508532423208, "acc_norm_stderr": 0.01325001257939344 }, "harness|hellaswag|10": { "acc": 0.7070304720175263, "acc_stderr": 0.004541944342035901, "acc_norm": 0.8815972913762199, "acc_norm_stderr": 0.003224240722351317 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.41, "acc_stderr": 0.049431107042371025, "acc_norm": 0.41, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7368421052631579, "acc_stderr": 0.03583496176361072, "acc_norm": 0.7368421052631579, "acc_norm_stderr": 0.03583496176361072 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.0440844002276808, "acc_norm": 0.74, "acc_norm_stderr": 0.0440844002276808 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6792452830188679, "acc_stderr": 0.02872750295788027, "acc_norm": 0.6792452830188679, "acc_norm_stderr": 0.02872750295788027 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.05021167315686779, "acc_norm": 0.52, "acc_norm_stderr": 0.05021167315686779 }, "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": 0.6647398843930635, "acc_stderr": 0.03599586301247077, "acc_norm": 0.6647398843930635, "acc_norm_stderr": 0.03599586301247077 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6297872340425532, "acc_stderr": 0.03156564682236785, "acc_norm": 0.6297872340425532, "acc_norm_stderr": 0.03156564682236785 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, 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"acc_norm": 0.6909090909090909, "acc_norm_stderr": 0.044262946482000985 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.0282638899437846, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.0282638899437846 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness|hendrycksTest-virology|5": { "acc": 0.5843373493975904, "acc_stderr": 0.03836722176598052, "acc_norm": 0.5843373493975904, "acc_norm_stderr": 0.03836722176598052 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03126781714663179, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness|truthfulqa:mc|0": { "mc1": 0.5667074663402693, "mc1_stderr": 0.017347024450107485, "mc2": 0.7142943510205136, "mc2_stderr": 0.015024530295000761 }, "harness|winogrande|5": { "acc": 0.8358326756116812, "acc_stderr": 0.01041084977522279 }, "harness|gsm8k|5": { "acc": 0.6474601971190296, "acc_stderr": 0.013159909755930337 } } ``` ## 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]

theoxo/proofwriter-deduction-balanced

--- license: cc-by-4.0 --- A processed subset of the OWA section of the [ProofWriter dataset](https://allenai.org/data/proofwriter). Each train/test split contains 300 entries, each of which has a unique set of theories and a single question for those theories. Both splits are balanced so that the depth of the proof required to answer the question varies evenly between 0-5 (50 entries each), and the labels are balanced (100 each). 'Unknown' labels have been replaced by 'Uncertain' to match other datasets.

jieunnie/ColorLand2

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 1048630.0 num_examples: 1 download_size: 68080 dataset_size: 1048630.0 --- # Dataset Card for "ColorLand2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

IlyaGusev/ru_turbo_saiga

--- dataset_info: features: - name: messages sequence: - name: role dtype: string - name: content dtype: string - name: seed dtype: string - name: source dtype: string - name: model_name dtype: string splits: - name: train num_bytes: 87316730 num_examples: 37731 download_size: 21742388 dataset_size: 87316730 license: cc-by-4.0 task_categories: - text-generation - text2text-generation language: - ru tags: - chat size_categories: - 10K<n<100K --- # Saiga Dataset of ChatGPT-generated chats in Russian. <img src="https://cdn.midjourney.com/0db33d04-9d39-45f3-acb2-e5c789852e23/0_3.png" > Based on the [Baize](https://github.com/project-baize/baize-chatbot) paper. Code: [link](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chat.py). Prompt: ``` Идёт диалог между пользователем и ИИ ассистентом. Пользователь и ассистент общаются на тему: {{seed}} Реплики человека начинаются с [Пользователь], реплики ассистента начинаются с [Ассистент]. Пользователь задаёт вопросы на основе темы и предыдущих сообщений. Пользователь обрывает беседу, когда у него не остается вопросов. Ассистент даёт максимально полные, информативные, точные и творческие ответы. Ассистент старается не задавать вопросов, за исключением уточняющих. Ассистент может отвечать несколькими абзацами. Ассистент может использовать Markdown. Закончи диалог точно в таком же формате. [Пользователь] Привет! [Ассистент] Привет! Чем я могу помочь? ``` ## Legal disclaimer Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit for us developing models that compete with OpenAI. Not for you.

fathyshalab/reklamation24_full

--- dataset_info: features: - name: text dtype: string - name: label dtype: int64 - name: label_name dtype: string - name: __index_level_0__ dtype: int64 - name: domain dtype: string splits: - name: train num_bytes: 3300008 num_examples: 6199 - name: test num_bytes: 831948 num_examples: 1559 download_size: 2038299 dataset_size: 4131956 --- # Dataset Card for "reklamation24_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ruslanasenov/llm-tolkien

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: input_ids sequence: int32 splits: - name: train num_bytes: 2196528.0 num_examples: 268 - name: test num_bytes: 245880.0 num_examples: 30 download_size: 1124977 dataset_size: 2442408.0 --- # Dataset Card for "llm-tolkien" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ahmedtremo/guanaco-llama2-1k

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1655208 num_examples: 1000 download_size: 966969 dataset_size: 1655208 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "guanaco-llama2-1k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Deivid457/Nobru

--- license: openrail ---

ro-h/regulatory_comments

--- language: - en tags: - government - api - policy pretty_name: Regulation.gov Public Comments size_categories: - n<1K task_categories: - text-classification --- # Dataset Card for Regulatory Comments (Predownloaded; No API Call) United States governmental agencies often make proposed regulations open to the public for comment. Proposed regulations are organized into "dockets". This dataset will use Regulation.gov public API to aggregate and clean public comments for dockets that mention substance use. Each example will consist of one docket, and include metadata such as docket id, docket title, etc. Each docket entry will also include information about the top 10 comments, including comment metadata and comment text. In this version, the data has been preloaded and saved to the repository. Raw data can be found in docket_comments_all.json. The code used to call the api can be found in api_call.py. If the user wants to call from the API directly, reference [https://huggingface.co/datasets/ro-h/regulatory_comments_api]. For an example of how to use this dataset, reference [https://colab.research.google.com/drive/1AiFznbHaDVszcmXYS3Ht5QLov2bvfQFX?usp=sharing]. ## Dataset Details ### Dataset Description and Structure This dataset will contain approximately 100 dockets. The number of dockets included are rate-limited by the government API. If a larger set of dockets are required, consider requesting a rate-unlimited API key and directly calling from the API using [https://huggingface.co/datasets/ro-h/regulatory_comments_api]. Each docket will be associated with at least one comment. The maximum number of comments per docket is 10. Comments will be retrieved in relevance order according to Regulation.gov. The following information is included in this dataset: **Docket Metadata** id (int): A unique numerical identifier assigned to each regulatory docket. agency (str): The abbreviation for the agency posting the regulatory docket (e.g., "FDA") title (str): The official title or name of the regulatory docket. This title typically summarizes the main issue or area of regulation covered by the docket. update_date (str): The date when the docket was last modified on Regulations.gov. update_time (str): The time when the docket was last modified on Regulations.gov. purpose (str): Whether the docket was rulemaking, non-rulemaking, or other. keywords (list): A string of keywords, as determined by Regulations.gov. **Comment Metadata** Note that huggingface converts lists of dictionaries to dictionaries of lists. comment_id (int): A unique numerical identifier for each public comment submitted on the docket. comment_url (str): A URL or web link to the specific comment or docket on Regulations.gov. This allows direct access to the original document or page for replicability purposes. comment_date (str): The date when the comment was posted on Regulations.gov. This is important for understanding the timeline of public engagement. comment_time (str): The time when the comment was posted on Regulations.gov. commenter_fname (str): The first name of the individual or entity that submitted the comment. This could be a person, organization, business, or government entity. commenter_lname (str): The last name of the individual or entity that submitted the comment. comment_length (int): The length of the comment in terms of the number of characters (spaces included) **Comment Content** text (str): The actual text of the comment submitted. This is the primary content for analysis, containing the commenter's views, arguments, and feedback on the regulatory matter. ### Dataset Limitations Commenter name features were phased in later in the system, so some dockets will have no first name/last name entries. Further, some comments were uploaded solely via attachment, and are stored in the system as null since the API has no access to comment attachments. However, many large companies will upload their comments via attachments, making any sentiment analysis biased towards individual commenters. - **Curated by:** Ro Huang ### Dataset Sources - **Repository:** [https://huggingface.co/datasets/ro-h/regulatory_comments_api] - **Original Website:** [https://www.regulations.gov/] - **API Website:** [https://open.gsa.gov/api/regulationsgov/] ## Uses This dataset may be used by researchers or policy-stakeholders curious about the influence of public comments on regulation development. For example, sentiment analysis may be run on comment text; alternatively, simple descriptive analysis on the comment length and agency regulation may prove interesting. ## Dataset Creation ### Curation Rationale After a law is passed, it may require specific details or guidelines to be practically enforceable or operable. Federal agencies and the Executive branch engage in rulemaking, which specify the practical ways that legislation can get turned into reality. Then, they will open a Public Comment period in which they will receive comments, suggestions, and questions on the regulations they proposed. After taking in the feedback, the agency will modify their regulation and post a final rule. As an example, imagine that the legislative branch of the government passes a bill to increase the number of hospitals nationwide. While the Congressman drafting the bill may have provided some general guidelines (e.g., there should be at least one hospital in a zip code), there is oftentimes ambiguity on how the bill’s goals should be achieved. The Department of Health and Human Services is tasked with implementing this new law, given its relevance to national healthcare infrastructure. The agency would draft and publish a set of proposed rules, which might include criteria for where new hospitals can be built, standards for hospital facilities, and the process for applying for federal funding. During the Public Comment period, healthcare providers, local governments, and the public can provide feedback or express concerns about the proposed rules. The agency will then read through these public comments, and modify their regulation accordingly. While this is a vital part of the United States regulatory process, there is little understanding of how agencies approach public comments and modify their proposed regulations. Further, the data extracted from the API is often unclean and difficult to navigate. This dataset seeks to offer some clarity through aggregating comments related to substance use, an issue that a diversity of stakeholders have investment in. #### Data Collection and Processing **Filtering Methods:** For each docket, we retrieve relevant metadata such as docket ID, title, context, purpose, and keywords. Additionally, the top 10 comments for each docket are collected, including their metadata (comment ID, URL, date, title, commenter's first and last name) and the comment text itself. The process focuses on the first page of 25 comments for each docket, and the top 10 comments are selected based on their order of appearance in the API response. Dockets with no comments are filtered out. **Data Normalization:** The collected data is normalized into a structured format. Each docket and its associated comments are organized into a nested dictionary structure. This structure includes key information about the docket and a list of comments, each with its detailed metadata. **Data Cleaning:** HTML text tags are removed from comment text. However, the content of the comment remains unedited, meaning any typos or grammatical errors in the original comment are preserved. **Tools and Libraries Used:** Requests Library: Used for making API calls to the Regulations.gov API to fetch dockets and comments data. Datasets Library from HuggingFace: Employed for defining and managing the dataset's structure and generation process. Python: The entire data collection and processing script is written in Python. **Error Handling:** In the event of a failed API request (indicated by a non-200 HTTP response status), the data collection process for the current docket is halted, and the process moves to the next docket.

CyberHarem/mutsuki_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of mutsuki/睦月/睦月 (Azur Lane) This is the dataset of mutsuki/睦月/睦月 (Azur Lane), containing 139 images and their tags. The core tags of this character are `animal_ears, brown_hair, cat_ears, green_eyes, hat, twintails, school_hat, short_hair, yellow_headwear, tail, ribbon, cat_tail, animal_ear_fluff, fang, short_twintails, bangs, low_twintails, bow, cat_girl, ears_through_headwear`, 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 | 139 | 137.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 139 | 87.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 325 | 188.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 139 | 125.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 325 | 252.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mutsuki_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/mutsuki_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 34 | ![](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) | kindergarten_uniform, blue_shirt, long_sleeves, looking_at_viewer, open_mouth, 1girl, blush, solo, yellow_skirt, yellow_neckerchief, pleated_skirt, :d, holding_lollipop, blunt_bangs, sailor_collar, jingle_bell, shoes, white_socks, white_background, hair_bow | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, full_body, kindergarten_uniform, looking_at_viewer, open_mouth, pleated_skirt, red_skirt, smile, solo, standing, tail_bow, white_pantyhose, ;d, lifebuoy, lollipop, one_eye_closed, arm_up, candy_wrapper, chibi, jingle_bell, simple_background, white_background, white_shirt, black_footwear, blunt_bangs, brown_footwear, holding_candy, legs_apart, mary_janes, outstretched_arm, paw_print, pigeon-toed, pink_bowtie, puffy_long_sleeves, rigging, torpedo_tubes, turret | | 2 | 6 | ![](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, blush, hetero, loli, open_mouth, sex, solo_focus, vaginal, 1boy, navel, penis, spread_legs, bar_censor, nude, tears, cum_in_pussy, nipples, sweat | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bell, blush, christmas, green_bow, open_mouth, red_bow, santa_hat, solo, candy_cane, fur-trimmed_headwear, looking_at_viewer, red_headwear, white_dress, wrist_cuffs, brown_footwear, red_capelet, striped_bow, :d, ;d, animal, blunt_bangs, chick, fur-trimmed_boots, fur-trimmed_capelet, holding_food, one_eye_closed, sack, snowflakes, white_background, white_thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | kindergarten_uniform | blue_shirt | long_sleeves | looking_at_viewer | open_mouth | 1girl | blush | solo | yellow_skirt | yellow_neckerchief | pleated_skirt | :d | holding_lollipop | blunt_bangs | sailor_collar | jingle_bell | shoes | white_socks | white_background | hair_bow | full_body | red_skirt | smile | standing | tail_bow | white_pantyhose | ;d | lifebuoy | lollipop | one_eye_closed | arm_up | candy_wrapper | chibi | simple_background | white_shirt | black_footwear | brown_footwear | holding_candy | legs_apart | mary_janes | outstretched_arm | paw_print | pigeon-toed | pink_bowtie | puffy_long_sleeves | rigging | torpedo_tubes | turret | hetero | loli | sex | solo_focus | vaginal | 1boy | navel | penis | spread_legs | bar_censor | nude | tears | cum_in_pussy | nipples | sweat | bell | christmas | green_bow | red_bow | santa_hat | candy_cane | fur-trimmed_headwear | red_headwear | white_dress | wrist_cuffs | red_capelet | striped_bow | animal | chick | fur-trimmed_boots | fur-trimmed_capelet | holding_food | sack | snowflakes | white_thighhighs | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------|:-------------|:---------------|:--------------------|:-------------|:--------|:--------|:-------|:---------------|:---------------------|:----------------|:-----|:-------------------|:--------------|:----------------|:--------------|:--------|:--------------|:-------------------|:-----------|:------------|:------------|:--------|:-----------|:-----------|:------------------|:-----|:-----------|:-----------|:-----------------|:---------|:----------------|:--------|:--------------------|:--------------|:-----------------|:-----------------|:----------------|:-------------|:-------------|:-------------------|:------------|:--------------|:--------------|:---------------------|:----------|:----------------|:---------|:---------|:-------|:------|:-------------|:----------|:-------|:--------|:--------|:--------------|:-------------|:-------|:--------|:---------------|:----------|:--------|:-------|:------------|:------------|:----------|:------------|:-------------|:-----------------------|:---------------|:--------------|:--------------|:--------------|:--------------|:---------|:--------|:--------------------|:----------------------|:---------------|:-------|:-------------|:-------------------| | 0 | 34 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | | X | X | X | X | X | | | X | | | X | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](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 | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | | | | X | X | X | X | X | | | | X | | X | | | | | X | | | | | | | | X | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

hippocrates/PmcPatient_test

--- dataset_info: features: - name: id dtype: string - name: query dtype: string - name: answer dtype: string splits: - name: train num_bytes: 388938338 num_examples: 126454 - name: valid num_bytes: 388938338 num_examples: 126454 - name: test num_bytes: 388938338 num_examples: 126454 download_size: 622007712 dataset_size: 1166815014 configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* - split: test path: data/test-* ---

eunbinni/ola_polyglot_3.8B_t2_data

--- dataset_info: features: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 46498843 num_examples: 139107 download_size: 28667291 dataset_size: 46498843 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ola_polyglot_3.8B_t2_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

DipakBundheliya/Shipping-label-NER

--- license: afl-3.0 ---

james-burton/fake_job_postings2_ord

--- dataset_info: features: - name: title dtype: string - name: salary_range dtype: string - name: description dtype: string - name: required_experience dtype: float64 - name: required_education dtype: float64 - name: fraudulent dtype: int64 splits: - name: train num_bytes: 14528605 num_examples: 10816 - name: validation num_bytes: 2469547 num_examples: 1909 - name: test num_bytes: 4328842 num_examples: 3182 download_size: 0 dataset_size: 21326994 --- # Dataset Card for "fake_job_postings2_ord" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rbeauchamp/blip_50k_val

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: seed dtype: uint32 - name: step dtype: uint16 - name: cfg dtype: float32 - name: sampler dtype: string - name: width dtype: uint16 - name: height dtype: uint16 - name: user_name dtype: string - name: timestamp dtype: timestamp[us, tz=UTC] - name: image_nsfw dtype: float32 - name: prompt_nsfw dtype: float32 splits: - name: train num_bytes: 4614195691.6 num_examples: 10000 download_size: 4624195058 dataset_size: 4614195691.6 --- # Dataset Card for "blip_50k_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_allbyai__ToRoLaMa-7b-v1.0

--- pretty_name: Evaluation run of allbyai/ToRoLaMa-7b-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [allbyai/ToRoLaMa-7b-v1.0](https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0) 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_allbyai__ToRoLaMa-7b-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-05T09:43:59.013115](https://huggingface.co/datasets/open-llm-leaderboard/details_allbyai__ToRoLaMa-7b-v1.0/blob/main/results_2024-01-05T09-43-59.013115.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\": 0.45235843004419446,\n\ \ \"acc_stderr\": 0.03423154255354607,\n \"acc_norm\": 0.45929415154501946,\n\ \ \"acc_norm_stderr\": 0.035110482824261206,\n \"mc1\": 0.30354957160342716,\n\ \ \"mc1_stderr\": 0.016095884155386844,\n \"mc2\": 0.44894454656581184,\n\ \ \"mc2_stderr\": 0.015890874190577126\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.47013651877133106,\n \"acc_stderr\": 0.014585305840007104,\n\ \ \"acc_norm\": 0.5170648464163823,\n \"acc_norm_stderr\": 0.0146028783885366\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.566122286397132,\n\ \ \"acc_stderr\": 0.004945956744943814,\n \"acc_norm\": 0.7381995618402709,\n\ \ \"acc_norm_stderr\": 0.00438716120308797\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\": 0.42962962962962964,\n\ \ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\ \ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.45394736842105265,\n \"acc_stderr\": 0.04051646342874143,\n\ \ \"acc_norm\": 0.45394736842105265,\n \"acc_norm_stderr\": 0.04051646342874143\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\": 0.46037735849056605,\n \"acc_stderr\": 0.030676096599389188,\n\ \ \"acc_norm\": 0.46037735849056605,\n \"acc_norm_stderr\": 0.030676096599389188\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.041553199555931467,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.041553199555931467\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n\ \ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \ \ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3699421965317919,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.3699421965317919,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\ \ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\ \ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4127659574468085,\n \"acc_stderr\": 0.03218471141400351,\n\ \ \"acc_norm\": 0.4127659574468085,\n \"acc_norm_stderr\": 0.03218471141400351\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n\ \ \"acc_stderr\": 0.04372748290278006,\n \"acc_norm\": 0.3157894736842105,\n\ \ \"acc_norm_stderr\": 0.04372748290278006\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n\ \ \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.29894179894179895,\n \"acc_stderr\": 0.023577604791655826,\n \"\ acc_norm\": 0.29894179894179895,\n \"acc_norm_stderr\": 0.023577604791655826\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\ \ \"acc_stderr\": 0.04006168083848877,\n \"acc_norm\": 0.2777777777777778,\n\ \ \"acc_norm_stderr\": 0.04006168083848877\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5258064516129032,\n\ \ \"acc_stderr\": 0.028406095057653326,\n \"acc_norm\": 0.5258064516129032,\n\ \ \"acc_norm_stderr\": 0.028406095057653326\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n\ \ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n\ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.6060606060606061,\n \"acc_stderr\": 0.0381549430868893,\n\ \ \"acc_norm\": 0.6060606060606061,\n \"acc_norm_stderr\": 0.0381549430868893\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.5757575757575758,\n \"acc_stderr\": 0.035212249088415845,\n \"\ acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.035212249088415845\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.6476683937823834,\n \"acc_stderr\": 0.03447478286414358,\n\ \ \"acc_norm\": 0.6476683937823834,\n \"acc_norm_stderr\": 0.03447478286414358\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.3769230769230769,\n \"acc_stderr\": 0.024570975364225995,\n\ \ \"acc_norm\": 0.3769230769230769,\n \"acc_norm_stderr\": 0.024570975364225995\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085622,\n \ \ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085622\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.42436974789915966,\n \"acc_stderr\": 0.03210479051015776,\n\ \ \"acc_norm\": 0.42436974789915966,\n \"acc_norm_stderr\": 0.03210479051015776\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.26490066225165565,\n \"acc_stderr\": 0.03603038545360384,\n \"\ acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.03603038545360384\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.5486238532110091,\n \"acc_stderr\": 0.021335714711268786,\n \"\ acc_norm\": 0.5486238532110091,\n \"acc_norm_stderr\": 0.021335714711268786\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.2638888888888889,\n \"acc_stderr\": 0.030058202704309846,\n \"\ acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.030058202704309846\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239172,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239172\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5907172995780591,\n \"acc_stderr\": 0.032007041833595914,\n \ \ \"acc_norm\": 0.5907172995780591,\n \"acc_norm_stderr\": 0.032007041833595914\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.547085201793722,\n\ \ \"acc_stderr\": 0.033408675019233246,\n \"acc_norm\": 0.547085201793722,\n\ \ \"acc_norm_stderr\": 0.033408675019233246\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.5190839694656488,\n \"acc_stderr\": 0.04382094705550988,\n\ \ \"acc_norm\": 0.5190839694656488,\n \"acc_norm_stderr\": 0.04382094705550988\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.6446280991735537,\n \"acc_stderr\": 0.0436923632657398,\n \"acc_norm\"\ : 0.6446280991735537,\n \"acc_norm_stderr\": 0.0436923632657398\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5462962962962963,\n\ \ \"acc_stderr\": 0.04812917324536824,\n \"acc_norm\": 0.5462962962962963,\n\ \ \"acc_norm_stderr\": 0.04812917324536824\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.49079754601226994,\n \"acc_stderr\": 0.03927705600787443,\n\ \ \"acc_norm\": 0.49079754601226994,\n \"acc_norm_stderr\": 0.03927705600787443\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\ \ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \ \ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.6407766990291263,\n \"acc_stderr\": 0.04750458399041695,\n\ \ \"acc_norm\": 0.6407766990291263,\n \"acc_norm_stderr\": 0.04750458399041695\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6923076923076923,\n\ \ \"acc_stderr\": 0.03023638994217309,\n \"acc_norm\": 0.6923076923076923,\n\ \ \"acc_norm_stderr\": 0.03023638994217309\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562427,\n \ \ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562427\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6079182630906769,\n\ \ \"acc_stderr\": 0.017458524050147636,\n \"acc_norm\": 0.6079182630906769,\n\ \ \"acc_norm_stderr\": 0.017458524050147636\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5,\n \"acc_stderr\": 0.026919095102908273,\n \ \ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.026919095102908273\n \ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.28938547486033517,\n\ \ \"acc_stderr\": 0.01516654455049031,\n \"acc_norm\": 0.28938547486033517,\n\ \ \"acc_norm_stderr\": 0.01516654455049031\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5032679738562091,\n \"acc_stderr\": 0.028629305194003543,\n\ \ \"acc_norm\": 0.5032679738562091,\n \"acc_norm_stderr\": 0.028629305194003543\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5369774919614148,\n\ \ \"acc_stderr\": 0.02832032583010591,\n \"acc_norm\": 0.5369774919614148,\n\ \ \"acc_norm_stderr\": 0.02832032583010591\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.4783950617283951,\n \"acc_stderr\": 0.02779476010500874,\n\ \ \"acc_norm\": 0.4783950617283951,\n \"acc_norm_stderr\": 0.02779476010500874\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611324,\n \ \ \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611324\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.32985658409387225,\n\ \ \"acc_stderr\": 0.012008129938540469,\n \"acc_norm\": 0.32985658409387225,\n\ \ \"acc_norm_stderr\": 0.012008129938540469\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.029520095697687758,\n\ \ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.029520095697687758\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4411764705882353,\n \"acc_stderr\": 0.02008736207670286,\n \ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.02008736207670286\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.44545454545454544,\n\ \ \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.44545454545454544,\n\ \ \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5102040816326531,\n \"acc_stderr\": 0.03200255347893782,\n\ \ \"acc_norm\": 0.5102040816326531,\n \"acc_norm_stderr\": 0.03200255347893782\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6368159203980099,\n\ \ \"acc_stderr\": 0.03400598505599014,\n \"acc_norm\": 0.6368159203980099,\n\ \ \"acc_norm_stderr\": 0.03400598505599014\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \ \ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\ \ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\ \ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6432748538011696,\n \"acc_stderr\": 0.03674013002860954,\n\ \ \"acc_norm\": 0.6432748538011696,\n \"acc_norm_stderr\": 0.03674013002860954\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30354957160342716,\n\ \ \"mc1_stderr\": 0.016095884155386844,\n \"mc2\": 0.44894454656581184,\n\ \ \"mc2_stderr\": 0.015890874190577126\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7008681925808997,\n \"acc_stderr\": 0.012868639066091533\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.013646702047005308,\n \ \ \"acc_stderr\": 0.003195747075480772\n }\n}\n```" repo_url: https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0 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_05T09_43_59.013115 path: - '**/details_harness|arc:challenge|25_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-05T09-43-59.013115.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|gsm8k|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hellaswag|10_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-43-59.013115.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-43-59.013115.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-05T09-43-59.013115.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_05T09_43_59.013115 path: - '**/details_harness|winogrande|5_2024-01-05T09-43-59.013115.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-05T09-43-59.013115.parquet' - config_name: results data_files: - split: 2024_01_05T09_43_59.013115 path: - results_2024-01-05T09-43-59.013115.parquet - split: latest path: - results_2024-01-05T09-43-59.013115.parquet --- # Dataset Card for Evaluation run of allbyai/ToRoLaMa-7b-v1.0  Dataset automatically created during the evaluation run of model [allbyai/ToRoLaMa-7b-v1.0](https://huggingface.co/allbyai/ToRoLaMa-7b-v1.0) 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_allbyai__ToRoLaMa-7b-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-05T09:43:59.013115](https://huggingface.co/datasets/open-llm-leaderboard/details_allbyai__ToRoLaMa-7b-v1.0/blob/main/results_2024-01-05T09-43-59.013115.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": 0.45235843004419446, "acc_stderr": 0.03423154255354607, "acc_norm": 0.45929415154501946, "acc_norm_stderr": 0.035110482824261206, "mc1": 0.30354957160342716, "mc1_stderr": 0.016095884155386844, "mc2": 0.44894454656581184, "mc2_stderr": 0.015890874190577126 }, "harness|arc:challenge|25": { "acc": 0.47013651877133106, "acc_stderr": 0.014585305840007104, "acc_norm": 0.5170648464163823, "acc_norm_stderr": 0.0146028783885366 }, "harness|hellaswag|10": { "acc": 0.566122286397132, "acc_stderr": 0.004945956744943814, "acc_norm": 0.7381995618402709, "acc_norm_stderr": 0.00438716120308797 }, "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": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.45394736842105265, "acc_stderr": 0.04051646342874143, "acc_norm": 0.45394736842105265, "acc_norm_stderr": 0.04051646342874143 }, "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": 0.46037735849056605, "acc_stderr": 0.030676096599389188, "acc_norm": 0.46037735849056605, "acc_norm_stderr": 0.030676096599389188 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4444444444444444, "acc_stderr": 0.041553199555931467, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.041553199555931467 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.3699421965317919, "acc_stderr": 0.0368122963339432, "acc_norm": 0.3699421965317919, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.2549019607843137, "acc_stderr": 0.043364327079931785, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.043364327079931785 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4127659574468085, "acc_stderr": 0.03218471141400351, "acc_norm": 0.4127659574468085, "acc_norm_stderr": 0.03218471141400351 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3157894736842105, "acc_stderr": 0.04372748290278006, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.04372748290278006 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.29894179894179895, "acc_stderr": 0.023577604791655826, "acc_norm": 0.29894179894179895, "acc_norm_stderr": 0.023577604791655826 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2777777777777778, "acc_stderr": 0.04006168083848877, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.04006168083848877 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5258064516129032, "acc_stderr": 0.028406095057653326, "acc_norm": 0.5258064516129032, "acc_norm_stderr": 0.028406095057653326 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0317852971064275, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0317852971064275 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6060606060606061, "acc_stderr": 0.0381549430868893, "acc_norm": 0.6060606060606061, "acc_norm_stderr": 0.0381549430868893 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5757575757575758, "acc_stderr": 0.035212249088415845, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.035212249088415845 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6476683937823834, "acc_stderr": 0.03447478286414358, "acc_norm": 0.6476683937823834, "acc_norm_stderr": 0.03447478286414358 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.3769230769230769, "acc_stderr": 0.024570975364225995, "acc_norm": 0.3769230769230769, "acc_norm_stderr": 0.024570975364225995 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2740740740740741, "acc_stderr": 0.027195934804085622, "acc_norm": 0.2740740740740741, "acc_norm_stderr": 0.027195934804085622 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.42436974789915966, "acc_stderr": 0.03210479051015776, "acc_norm": 0.42436974789915966, "acc_norm_stderr": 0.03210479051015776 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.26490066225165565, "acc_stderr": 0.03603038545360384, "acc_norm": 0.26490066225165565, "acc_norm_stderr": 0.03603038545360384 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5486238532110091, "acc_stderr": 0.021335714711268786, "acc_norm": 0.5486238532110091, "acc_norm_stderr": 0.021335714711268786 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2638888888888889, "acc_stderr": 0.030058202704309846, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.030058202704309846 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239172, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239172 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5907172995780591, "acc_stderr": 0.032007041833595914, "acc_norm": 0.5907172995780591, "acc_norm_stderr": 0.032007041833595914 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.547085201793722, "acc_stderr": 0.033408675019233246, "acc_norm": 0.547085201793722, "acc_norm_stderr": 0.033408675019233246 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5190839694656488, "acc_stderr": 0.04382094705550988, "acc_norm": 0.5190839694656488, "acc_norm_stderr": 0.04382094705550988 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6446280991735537, "acc_stderr": 0.0436923632657398, "acc_norm": 0.6446280991735537, "acc_norm_stderr": 0.0436923632657398 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5462962962962963, "acc_stderr": 0.04812917324536824, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.04812917324536824 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.49079754601226994, "acc_stderr": 0.03927705600787443, "acc_norm": 0.49079754601226994, "acc_norm_stderr": 0.03927705600787443 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.375, "acc_stderr": 0.04595091388086298, "acc_norm": 0.375, "acc_norm_stderr": 0.04595091388086298 }, "harness|hendrycksTest-management|5": { "acc": 0.6407766990291263, "acc_stderr": 0.04750458399041695, "acc_norm": 0.6407766990291263, "acc_norm_stderr": 0.04750458399041695 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6923076923076923, "acc_stderr": 0.03023638994217309, "acc_norm": 0.6923076923076923, "acc_norm_stderr": 0.03023638994217309 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.43, "acc_stderr": 0.04975698519562427, "acc_norm": 0.43, "acc_norm_stderr": 0.04975698519562427 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6079182630906769, "acc_stderr": 0.017458524050147636, "acc_norm": 0.6079182630906769, "acc_norm_stderr": 0.017458524050147636 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5, "acc_stderr": 0.026919095102908273, "acc_norm": 0.5, "acc_norm_stderr": 0.026919095102908273 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.28938547486033517, "acc_stderr": 0.01516654455049031, "acc_norm": 0.28938547486033517, "acc_norm_stderr": 0.01516654455049031 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5032679738562091, "acc_stderr": 0.028629305194003543, "acc_norm": 0.5032679738562091, "acc_norm_stderr": 0.028629305194003543 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5369774919614148, "acc_stderr": 0.02832032583010591, "acc_norm": 0.5369774919614148, "acc_norm_stderr": 0.02832032583010591 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4783950617283951, "acc_stderr": 0.02779476010500874, "acc_norm": 0.4783950617283951, "acc_norm_stderr": 0.02779476010500874 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.34397163120567376, "acc_stderr": 0.028338017428611324, "acc_norm": 0.34397163120567376, "acc_norm_stderr": 0.028338017428611324 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.32985658409387225, "acc_stderr": 0.012008129938540469, "acc_norm": 0.32985658409387225, "acc_norm_stderr": 0.012008129938540469 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.38235294117647056, "acc_stderr": 0.029520095697687758, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.029520095697687758 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4411764705882353, "acc_stderr": 0.02008736207670286, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.02008736207670286 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.44545454545454544, "acc_stderr": 0.047605488214603246, "acc_norm": 0.44545454545454544, "acc_norm_stderr": 0.047605488214603246 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5102040816326531, "acc_stderr": 0.03200255347893782, "acc_norm": 0.5102040816326531, "acc_norm_stderr": 0.03200255347893782 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6368159203980099, "acc_stderr": 0.03400598505599014, "acc_norm": 0.6368159203980099, "acc_norm_stderr": 0.03400598505599014 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-virology|5": { "acc": 0.40963855421686746, "acc_stderr": 0.03828401115079022, "acc_norm": 0.40963855421686746, "acc_norm_stderr": 0.03828401115079022 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6432748538011696, "acc_stderr": 0.03674013002860954, "acc_norm": 0.6432748538011696, "acc_norm_stderr": 0.03674013002860954 }, "harness|truthfulqa:mc|0": { "mc1": 0.30354957160342716, "mc1_stderr": 0.016095884155386844, "mc2": 0.44894454656581184, "mc2_stderr": 0.015890874190577126 }, "harness|winogrande|5": { "acc": 0.7008681925808997, "acc_stderr": 0.012868639066091533 }, "harness|gsm8k|5": { "acc": 0.013646702047005308, "acc_stderr": 0.003195747075480772 } } ``` ## 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]

AiresPucrs/CelebA-Smiles

--- license: other dataset_info: features: - name: image dtype: image - name: 5_o_Clock_Shadow dtype: int64 - name: Arched_Eyebrows dtype: int64 - name: Attractive dtype: int64 - name: Bags_Under_Eyes dtype: int64 - name: Bald dtype: int64 - name: Bangs dtype: int64 - name: Big_Lips dtype: int64 - name: Big_Nose dtype: int64 - name: Black_Hair dtype: int64 - name: Blond_Hair dtype: int64 - name: Blurry dtype: int64 - name: Brown_Hair dtype: int64 - name: Bushy_Eyebrows dtype: int64 - name: Chubby dtype: int64 - name: Double_Chin dtype: int64 - name: Eyeglasses dtype: int64 - name: Goatee dtype: int64 - name: Gray_Hair dtype: int64 - name: Heavy_Makeup dtype: int64 - name: High_Cheekbones dtype: int64 - name: Male dtype: int64 - name: Mouth_Slightly_Open dtype: int64 - name: Mustache dtype: int64 - name: Narrow_Eyes dtype: int64 - name: No_Beard dtype: int64 - name: Oval_Face dtype: int64 - name: Pale_Skin dtype: int64 - name: Pointy_Nose dtype: int64 - name: Receding_Hairline dtype: int64 - name: Rosy_Cheeks dtype: int64 - name: Sideburns dtype: int64 - name: Smiling dtype: int64 - name: Straight_Hair dtype: int64 - name: Wavy_Hair dtype: int64 - name: Wearing_Earrings dtype: int64 - name: Wearing_Hat dtype: int64 - name: Wearing_Lipstick dtype: int64 - name: Wearing_Necklace dtype: int64 - name: Wearing_Necktie dtype: int64 - name: Young dtype: int64 splits: - name: train num_bytes: 365293550 num_examples: 50000 download_size: 349853371 dataset_size: 365293550 configs: - config_name: default data_files: - split: train path: data/train-* pretty_name: CelebA-Smiles size_categories: - 10M<n<100M --- # CelebA-Smiles ## Overview This dataset is a subset of the [CelebFaces Attributes Dataset](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset). The dataset can be employed as the training and test sets for computer vision tasks like smile detection. ## Dataset Details The CelebA-Smiles dataset is a smaller version of the original dataset. This data originally came from [CelebFaces Attributes Dataset (CelebA)](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset) The original dataset contains : [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including - 10,177 number of identities - 202,599 face images - 5 landmark locations - 40 binary attribute annotations per image. The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face recognition, face detection, landmark (or facial part) localization, and face editing & synthesis. ```latex @inproceedings{liu2015faceattributes, title = {Deep Learning Face Attributes in the Wild}, author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou}, booktitle = {Proceedings of International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} } ``` - Dataset Name: **CelebA-Smiles** - Language: English - Total Size: 50,000 demonstrations ## Contents The subset dataset consists of images of celebrity people with 40 attributes. The **CelebA-Smile** dataset is balanced with 50% people smiling and 50% people not smiling, it also contains the other 39 attributes like "5_o_Clock_Shadow", "Arched_Eyebrows", "Attractive", "Bags_Under_Eyes", "bald", etc. ## How to use ```python from datasets import load_dataset dataset = load_dataset("AiresPucrs/CelebA-Smiles", split='train') ``` ## License The dataset is licensed under the [Other](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).

afmck/common_voice_13_0_hi_pseudo_labelled

--- dataset_info: config_name: zh-TW features: - name: client_id dtype: string - name: path dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: sentence dtype: string - name: up_votes dtype: int64 - name: down_votes dtype: int64 - name: age dtype: string - name: gender dtype: string - name: accent dtype: string - name: locale dtype: string - name: segment dtype: string - name: variant dtype: string - name: whisper_transcript sequence: int64 splits: - name: train num_bytes: 160498655.447 num_examples: 6799 - name: validation num_bytes: 122880246.375 num_examples: 4825 - name: test num_bytes: 142152848.375 num_examples: 4825 download_size: 398340449 dataset_size: 425531750.197 configs: - config_name: zh-TW data_files: - split: train path: zh-TW/train-* - split: validation path: zh-TW/validation-* - split: test path: zh-TW/test-* ---

ai4bharat/IndicQA-Translated

--- dataset_info: features: - name: id dtype: string - name: context dtype: string - name: question dtype: string - name: answers sequence: - name: text dtype: string - name: answer_start dtype: int32 - name: itv2 hi question dtype: string - name: itv2 hi context dtype: string - name: itv2 hi answers struct: - name: answer_start sequence: int64 - name: text sequence: string splits: - name: test num_bytes: 15063303 num_examples: 1547 download_size: 1378045 dataset_size: 15063303 configs: - config_name: default data_files: - split: test path: data/test-* ---

Mxode/Baike-Astronomy-ZH

--- license: apache-2.0 task_categories: - text-generation language: - zh tags: - astronomy size_categories: - n<1K --- 天文学百科，包含 8 个子目录，约 1000 条词条、110,0000 个字符。数据包含一级目录、二级目录、标题、内容。其中**内容已经处理为单行**，且**文本普遍较长**。一个样例如下： ```json { "top_category": "天文学", "sub_category": "天体力学", "title": "万有引力定律", "content": "万有引力定律（汉语拼音：wàn yǒu yǐn lì zhī dìng lǜ），（universal gravitation，law of），自然界中任何两个质点都相互吸引，这个力同两个质点的质量的乘积成正比，同它们之间的距离的二次方成反比。如用m1、m2表示两质点的质量，r表示两质点间的距离，F表示作用力的值，则F＝Gm1m2／r2，式中的G是比例常量，称万有引力常量或牛顿引力常量，数值因不同单位制而异，在国际单位制中G为6.672×1011牛顿·米2／千克2。这个定律由牛顿于1687年在《原理》上首次发表，它和牛顿运动定律一起，构成了牛顿力学特别是天体力学的基础。\n　　在牛顿公布该定律之前，胡克、惠更斯都曾根据开普勒定律推测行星和太阳间存在和距离二次方成反比的引力，但未能提出数学证明，为此胡克还和牛顿通过信，因此对定律的首创权有过争议。牛顿还曾对晚年的忘年交斯多克雷说过，1666年他在家乡避瘟疫时，曾因见苹果从树上落地而想到地球对苹果的引力是否可延伸到月球。此说传布很广，许多科学家深信不疑，并对牛顿为何推迟20年才发表有种种推测。但也有人根据牛顿晚年的精神状态，认为他对斯多克雷所说的并非真情。\n　　一般物体之间的引力，在物体尺度远小于质心距离时，可视为质点；尺度和间距相近时，须视为质点系，用积分法求引力。但牛顿已算出一个密度均匀的圆球对附近质点的引力同把圆球的质量集中于球心时完全一致。对万有引力的起因，牛顿未作解释，把它视为超距力或以太的作用，系后人所为。爱因斯坦在广义相对论中将引力归之于时空曲率的变化。" } ```

Nart/parallel-ab-ru

--- language_creators: - expert-generated language: - ab - ru license: - cc0-1.0 multilinguality: - translation - multilingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-generation - translation task_ids: [] pretty_name: Abkhazian Russian parallel corpus --- ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Other Known Limitations](#other-known-limitations) ## Dataset Description - **Point of Contact:** [Nart Tlisha](mailto:daniel.abzakh@gmail.com) - **Size of the generated dataset:** 33.5 MB ### Dataset Summary The Abkhaz Russian parallel corpus dataset is a collection of 205,665 sentences/words extracted from different sources; e-books, web scrapping. ## Dataset Creation ### Source Data Here is a link to the source on [github](https://github.com/danielinux7/Multilingual-Parallel-Corpus/blob/master/references.md) ## Considerations for Using the Data ### Other Known Limitations The accuracy of the dataset is around 95% (gramatical, arthographical errors)

open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b

--- pretty_name: Evaluation run of Azazelle/Tippy-Toppy-7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Azazelle/Tippy-Toppy-7b](https://huggingface.co/Azazelle/Tippy-Toppy-7b) on the\ \ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-06T01:20:11.911337](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b/blob/main/results_2024-01-06T01-20-11.911337.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\": 0.6570837201709685,\n\ \ \"acc_stderr\": 0.031992607878974816,\n \"acc_norm\": 0.658599829847844,\n\ \ \"acc_norm_stderr\": 0.03263443134197047,\n \"mc1\": 0.390452876376989,\n\ \ \"mc1_stderr\": 0.017078230743431455,\n \"mc2\": 0.5570225708371419,\n\ \ \"mc2_stderr\": 0.015617917882145785\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6382252559726962,\n \"acc_stderr\": 0.014041957945038075,\n\ \ \"acc_norm\": 0.6689419795221843,\n \"acc_norm_stderr\": 0.013752062419817834\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6790479984066919,\n\ \ \"acc_stderr\": 0.004658882929099517,\n \"acc_norm\": 0.8587930691097391,\n\ \ \"acc_norm_stderr\": 0.003475231889452832\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\ \ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \ \ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700918,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700918\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.034765901043041336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\"\ : 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n\ \ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\ \ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n\ \ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\ \ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\ \ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\ \ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\ \ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\ \ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\ \ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n\ \ \"acc_stderr\": 0.023287665127268542,\n \"acc_norm\": 0.7870967741935484,\n\ \ \"acc_norm_stderr\": 0.023287665127268542\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175008,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175008\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\ acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919436,\n\ \ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919436\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.36666666666666664,\n \"acc_stderr\": 0.029381620726465066,\n \ \ \"acc_norm\": 0.36666666666666664,\n \"acc_norm_stderr\": 0.029381620726465066\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6974789915966386,\n \"acc_stderr\": 0.029837962388291932,\n\ \ \"acc_norm\": 0.6974789915966386,\n \"acc_norm_stderr\": 0.029837962388291932\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\ acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700472,\n \"\ acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700472\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\ acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240647,\n \"\ acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240647\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8059071729957806,\n \"acc_stderr\": 0.02574490253229092,\n \ \ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.02574490253229092\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\ \ \"acc_stderr\": 0.03076935200822914,\n \"acc_norm\": 0.6995515695067265,\n\ \ \"acc_norm_stderr\": 0.03076935200822914\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\ \ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\ \ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\ \ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\ \ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\ \ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\ \ \"acc_stderr\": 0.013428186370608303,\n \"acc_norm\": 0.8301404853128991,\n\ \ \"acc_norm_stderr\": 0.013428186370608303\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.02418242749657761,\n\ \ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.02418242749657761\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3664804469273743,\n\ \ \"acc_stderr\": 0.01611523550486547,\n \"acc_norm\": 0.3664804469273743,\n\ \ \"acc_norm_stderr\": 0.01611523550486547\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.02558306248998481,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.02558306248998481\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7623456790123457,\n \"acc_stderr\": 0.02368359183700856,\n\ \ \"acc_norm\": 0.7623456790123457,\n \"acc_norm_stderr\": 0.02368359183700856\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.46099290780141844,\n \"acc_stderr\": 0.029736592526424438,\n \ \ \"acc_norm\": 0.46099290780141844,\n \"acc_norm_stderr\": 0.029736592526424438\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46479791395045633,\n\ \ \"acc_stderr\": 0.012738547371303957,\n \"acc_norm\": 0.46479791395045633,\n\ \ \"acc_norm_stderr\": 0.012738547371303957\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7242647058823529,\n \"acc_stderr\": 0.027146271936625162,\n\ \ \"acc_norm\": 0.7242647058823529,\n \"acc_norm_stderr\": 0.027146271936625162\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000325,\n \ \ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000325\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\ \ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \ \ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142773,\n\ \ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142773\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\ \ \"acc_stderr\": 0.025538433368578334,\n \"acc_norm\": 0.845771144278607,\n\ \ \"acc_norm_stderr\": 0.025538433368578334\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\ \ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.390452876376989,\n\ \ \"mc1_stderr\": 0.017078230743431455,\n \"mc2\": 0.5570225708371419,\n\ \ \"mc2_stderr\": 0.015617917882145785\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7884767166535123,\n \"acc_stderr\": 0.01147774768422318\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6467020470053071,\n \ \ \"acc_stderr\": 0.013166337192115686\n }\n}\n```" repo_url: https://huggingface.co/Azazelle/Tippy-Toppy-7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|arc:challenge|25_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|arc:challenge|25_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-06T01-20-11.911337.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|gsm8k|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|gsm8k|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hellaswag|10_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hellaswag|10_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T00-38-33.020065.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-20-11.911337.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-06T01-20-11.911337.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-06T01-20-11.911337.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_06T00_38_33.020065 path: - '**/details_harness|winogrande|5_2024-01-06T00-38-33.020065.parquet' - split: 2024_01_06T01_20_11.911337 path: - '**/details_harness|winogrande|5_2024-01-06T01-20-11.911337.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-06T01-20-11.911337.parquet' - config_name: results data_files: - split: 2024_01_06T00_38_33.020065 path: - results_2024-01-06T00-38-33.020065.parquet - split: 2024_01_06T01_20_11.911337 path: - results_2024-01-06T01-20-11.911337.parquet - split: latest path: - results_2024-01-06T01-20-11.911337.parquet --- # Dataset Card for Evaluation run of Azazelle/Tippy-Toppy-7b  Dataset automatically created during the evaluation run of model [Azazelle/Tippy-Toppy-7b](https://huggingface.co/Azazelle/Tippy-Toppy-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-06T01:20:11.911337](https://huggingface.co/datasets/open-llm-leaderboard/details_Azazelle__Tippy-Toppy-7b/blob/main/results_2024-01-06T01-20-11.911337.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": 0.6570837201709685, "acc_stderr": 0.031992607878974816, "acc_norm": 0.658599829847844, "acc_norm_stderr": 0.03263443134197047, "mc1": 0.390452876376989, "mc1_stderr": 0.017078230743431455, "mc2": 0.5570225708371419, "mc2_stderr": 0.015617917882145785 }, "harness|arc:challenge|25": { "acc": 0.6382252559726962, "acc_stderr": 0.014041957945038075, "acc_norm": 0.6689419795221843, "acc_norm_stderr": 0.013752062419817834 }, "harness|hellaswag|10": { "acc": 0.6790479984066919, "acc_stderr": 0.004658882929099517, "acc_norm": 0.8587930691097391, "acc_norm_stderr": 0.003475231889452832 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.6, "acc_stderr": 0.04923659639173309, "acc_norm": 0.6, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700918, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700918 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7777777777777778, "acc_stderr": 0.034765901043041336, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.034765901043041336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.36, "acc_stderr": 0.048241815132442176, "acc_norm": 0.36, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.03533133389323657, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.03533133389323657 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.78, "acc_stderr": 0.04163331998932261, "acc_norm": 0.78, "acc_norm_stderr": 0.04163331998932261 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5914893617021276, "acc_stderr": 0.032134180267015755, "acc_norm": 0.5914893617021276, "acc_norm_stderr": 0.032134180267015755 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.02530590624159063, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.02530590624159063 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4444444444444444, "acc_stderr": 0.044444444444444495, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.044444444444444495 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.023287665127268542, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.023287665127268542 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175008, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175008 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.803030303030303, "acc_stderr": 0.028335609732463362, "acc_norm": 0.803030303030303, "acc_norm_stderr": 0.028335609732463362 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8860103626943006, "acc_stderr": 0.022935144053919436, "acc_norm": 0.8860103626943006, "acc_norm_stderr": 0.022935144053919436 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.36666666666666664, "acc_stderr": 0.029381620726465066, "acc_norm": 0.36666666666666664, "acc_norm_stderr": 0.029381620726465066 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6974789915966386, "acc_stderr": 0.029837962388291932, "acc_norm": 0.6974789915966386, "acc_norm_stderr": 0.029837962388291932 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3443708609271523, "acc_stderr": 0.038796870240733264, "acc_norm": 0.3443708609271523, "acc_norm_stderr": 0.038796870240733264 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8550458715596331, "acc_stderr": 0.015094215699700472, "acc_norm": 0.8550458715596331, "acc_norm_stderr": 0.015094215699700472 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5462962962962963, "acc_stderr": 0.033953227263757976, "acc_norm": 0.5462962962962963, "acc_norm_stderr": 0.033953227263757976 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8284313725490197, "acc_stderr": 0.026460569561240647, "acc_norm": 0.8284313725490197, "acc_norm_stderr": 0.026460569561240647 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8059071729957806, "acc_stderr": 0.02574490253229092, "acc_norm": 0.8059071729957806, "acc_norm_stderr": 0.02574490253229092 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6995515695067265, "acc_stderr": 0.03076935200822914, "acc_norm": 0.6995515695067265, "acc_norm_stderr": 0.03076935200822914 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7852760736196319, "acc_stderr": 0.032262193772867744, "acc_norm": 0.7852760736196319, "acc_norm_stderr": 0.032262193772867744 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.49107142857142855, "acc_stderr": 0.04745033255489123, "acc_norm": 0.49107142857142855, "acc_norm_stderr": 0.04745033255489123 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.02190190511507333, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.02190190511507333 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8301404853128991, "acc_stderr": 0.013428186370608303, "acc_norm": 0.8301404853128991, "acc_norm_stderr": 0.013428186370608303 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7196531791907514, "acc_stderr": 0.02418242749657761, "acc_norm": 0.7196531791907514, "acc_norm_stderr": 0.02418242749657761 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3664804469273743, "acc_stderr": 0.01611523550486547, "acc_norm": 0.3664804469273743, "acc_norm_stderr": 0.01611523550486547 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7483660130718954, "acc_stderr": 0.0248480182638752, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.0248480182638752 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.02558306248998481, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.02558306248998481 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7623456790123457, "acc_stderr": 0.02368359183700856, "acc_norm": 0.7623456790123457, "acc_norm_stderr": 0.02368359183700856 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46099290780141844, "acc_stderr": 0.029736592526424438, "acc_norm": 0.46099290780141844, "acc_norm_stderr": 0.029736592526424438 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46479791395045633, "acc_stderr": 0.012738547371303957, "acc_norm": 0.46479791395045633, "acc_norm_stderr": 0.012738547371303957 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7242647058823529, "acc_stderr": 0.027146271936625162, "acc_norm": 0.7242647058823529, "acc_norm_stderr": 0.027146271936625162 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000325, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000325 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7, "acc_stderr": 0.04389311454644287, "acc_norm": 0.7, "acc_norm_stderr": 0.04389311454644287 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7387755102040816, "acc_stderr": 0.028123429335142773, "acc_norm": 0.7387755102040816, "acc_norm_stderr": 0.028123429335142773 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.025538433368578334, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.025538433368578334 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.87, "acc_stderr": 0.033799766898963086, "acc_norm": 0.87, "acc_norm_stderr": 0.033799766898963086 }, "harness|hendrycksTest-virology|5": { "acc": 0.5180722891566265, "acc_stderr": 0.03889951252827216, "acc_norm": 0.5180722891566265, "acc_norm_stderr": 0.03889951252827216 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8304093567251462, "acc_stderr": 0.02878210810540171, "acc_norm": 0.8304093567251462, "acc_norm_stderr": 0.02878210810540171 }, "harness|truthfulqa:mc|0": { "mc1": 0.390452876376989, "mc1_stderr": 0.017078230743431455, "mc2": 0.5570225708371419, "mc2_stderr": 0.015617917882145785 }, "harness|winogrande|5": { "acc": 0.7884767166535123, "acc_stderr": 0.01147774768422318 }, "harness|gsm8k|5": { "acc": 0.6467020470053071, "acc_stderr": 0.013166337192115686 } } ``` ## 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]

suthawadee/receipt_th_4

--- dataset_info: features: - name: image dtype: image - name: ground_truth dtype: string splits: - name: train num_bytes: 50296489.0 num_examples: 320 - name: validation num_bytes: 6665987.0 num_examples: 40 - name: test num_bytes: 6009751.0 num_examples: 40 download_size: 62106872 dataset_size: 62972227.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

flagship/rice-thermal-new_demo

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': RiceLeafs_BrownSpot '1': RiceLeafs_Healthy '2': RiceLeafs_Hispa '3': RiceLeafs_LeafBlast splits: - name: train num_bytes: 2607108.0 num_examples: 354 - name: test num_bytes: 944624.0 num_examples: 129 download_size: 3511150 dataset_size: 3551732.0 --- # Dataset Card for "rice-thermal-new_demo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

316usman/thematic5e-pw-embed-part4

--- dataset_info: features: - name: text dtype: string - name: document_url dtype: string - name: source_url dtype: string - name: country dtype: string splits: - name: train num_bytes: 265771303 num_examples: 405601 download_size: 102982484 dataset_size: 265771303 configs: - config_name: default data_files: - split: train path: data/train-* ---

arieg/cluster01_large_150

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 004097 '1': '005264' '2': '006674' '3': 009560 '4': '011764' '5': '016334' '6': 019707 '7': '025055' '8': '025601' '9': 026681 '10': 030488 '11': '032756' '12': 036388 '13': 036990 '14': '045516' '15': 047894 '16': '054152' '17': '054156' '18': 058543 '19': 059448 '20': 064093 '21': 064248 '22': '064520' '23': 064992 '24': 065683 '25': 068897 '26': 069781 '27': '071240' '28': '073171' '29': 074945 '30': '075314' '31': '076131' '32': 078841 '33': 081365 '34': 081565 '35': 084139 '36': 084141 '37': 085486 '38': 085492 '39': 087158 '40': 087187 '41': 087966 '42': 088960 '43': 089857 '44': 091900 '45': 093942 '46': 095452 '47': 096694 '48': 098550 '49': 098551 '50': 098552 '51': '101118' '52': '101868' '53': '107181' '54': '107851' '55': '108014' '56': '108303' '57': '108969' '58': '110171' '59': '111372' '60': '111398' '61': '111399' '62': '120178' '63': '121314' '64': '121415' '65': '121738' '66': '125188' '67': '126404' '68': '126489' '69': '126491' '70': '127204' '71': '129185' '72': '129372' '73': '130218' '74': '130950' '75': '130951' '76': '130954' '77': '131792' '78': '132434' '79': '137211' '80': '137900' '81': '141735' '82': '142082' '83': '144545' '84': '146685' '85': '148186' '86': '148211' '87': '148235' '88': '148532' '89': '149369' splits: - name: train num_bytes: 706895816.0 num_examples: 13500 download_size: 706947444 dataset_size: 706895816.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

alex-miller/oecd-dac-crs

--- language: - en - fr - es license: cc size_categories: - 1M<n<10M task_categories: - mask-generation pretty_name: OECD DAC CRS Project titles and descriptions dataset_info: features: - name: text dtype: string - name: Year dtype: int64 - name: ProjectNumber dtype: string - name: RecipientName dtype: string - name: RecipientCode dtype: int64 - name: DonorName dtype: string - name: DonorCode dtype: int64 - name: ProjectTitle dtype: string - name: SectorName dtype: string - name: PurposeName dtype: string - name: FlowName dtype: string - name: ShortDescription dtype: string - name: LongDescription dtype: string splits: - name: train num_bytes: 1643110932 num_examples: 1870757 download_size: 697495115 dataset_size: 1643110932 configs: - config_name: default data_files: - split: train path: data/train-* tags: - finance --- # OECD DAC CRS Project titles and descriptions All unique project titles and descriptions from the OECD DAC Creditor Reporting System (CRS). https://stats.oecd.org/Index.aspx?DataSetCode=crs1 `text` column is the concatenation of Project Title, Short Description, and Long Description, and is also the column on which duplicate projects were removed. Other columns are included for metadata purposes, or if you want to create a new text column as a concatenation of additional data.

Parikshith/grow-1-monolingual-ha-en-1_1m

--- dataset_info: features: - name: ha dtype: string - name: generated_text dtype: string splits: - name: train num_bytes: 265225402 num_examples: 1100000 download_size: 171871099 dataset_size: 265225402 configs: - config_name: default data_files: - split: train path: data/train-* ---

liuyanchen1015/MULTI_VALUE_cola_possessives_belong

--- dataset_info: features: - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 10611 num_examples: 117 - name: test num_bytes: 11076 num_examples: 127 - name: train num_bytes: 85151 num_examples: 983 download_size: 51915 dataset_size: 106838 --- # Dataset Card for "MULTI_VALUE_cola_possessives_belong" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mateusmeladogame/davidmelado

--- license: unknown ---

Jbrcoleman/fake-news

--- license: cc ---

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/eef0e7be

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 182 num_examples: 10 download_size: 1341 dataset_size: 182 --- # Dataset Card for "eef0e7be" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

garcianacho/human_genome_csv

--- license: apache-2.0 task_categories: - token-classification tags: - biology - genome - human genome - bioinformatics --- ## Human Genome Dataset Here is a human genome ready to be used to train LLM.

ShrinivasSK/hi_en_2

--- dataset_info: features: - name: idx dtype: int64 - name: tgt dtype: string - name: src dtype: string splits: - name: train num_bytes: 6376404.6 num_examples: 18000 - name: test num_bytes: 708489.4 num_examples: 2000 download_size: 3796444 dataset_size: 7084894.0 --- # Dataset Card for "hi_en_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo16_2_mix_50_kl_0.1_prm_160m_thr_0.0_seed_1

--- dataset_info: config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: preference dtype: int64 - name: output_1 dtype: string - name: output_2 dtype: string - name: reward_model_prompt_format dtype: string - name: gen_prompt_format dtype: string - name: gen_kwargs struct: - name: do_sample dtype: bool - name: max_new_tokens dtype: int64 - name: pad_token_id dtype: int64 - name: top_k dtype: int64 - name: top_p dtype: float64 - name: reward_1 dtype: float64 - name: reward_2 dtype: float64 - name: n_samples dtype: int64 - name: reject_select dtype: string - name: index dtype: int64 - name: prompt dtype: string - name: chosen dtype: string - name: rejected dtype: string - name: filtered_epoch dtype: int64 - name: gen_reward dtype: float64 - name: gen_response dtype: string splits: - name: epoch_0 num_bytes: 43766110 num_examples: 18928 - name: epoch_1 num_bytes: 44307023 num_examples: 18928 - name: epoch_2 num_bytes: 44359431 num_examples: 18928 - name: epoch_3 num_bytes: 44381054 num_examples: 18928 - name: epoch_4 num_bytes: 44389571 num_examples: 18928 - name: epoch_5 num_bytes: 44380600 num_examples: 18928 - name: epoch_6 num_bytes: 44375580 num_examples: 18928 - name: epoch_7 num_bytes: 44362518 num_examples: 18928 - name: epoch_8 num_bytes: 44361149 num_examples: 18928 - name: epoch_9 num_bytes: 44355512 num_examples: 18928 - name: epoch_10 num_bytes: 44355603 num_examples: 18928 - name: epoch_11 num_bytes: 44353930 num_examples: 18928 - name: epoch_12 num_bytes: 44354440 num_examples: 18928 - name: epoch_13 num_bytes: 44355243 num_examples: 18928 - name: epoch_14 num_bytes: 44353399 num_examples: 18928 - name: epoch_15 num_bytes: 44350140 num_examples: 18928 - name: epoch_16 num_bytes: 44353177 num_examples: 18928 - name: epoch_17 num_bytes: 44352975 num_examples: 18928 - name: epoch_18 num_bytes: 44350345 num_examples: 18928 - name: epoch_19 num_bytes: 44351723 num_examples: 18928 - name: epoch_20 num_bytes: 44349418 num_examples: 18928 - name: epoch_21 num_bytes: 44351470 num_examples: 18928 - name: epoch_22 num_bytes: 44351707 num_examples: 18928 - name: epoch_23 num_bytes: 44351088 num_examples: 18928 - name: epoch_24 num_bytes: 44350591 num_examples: 18928 - name: epoch_25 num_bytes: 44351243 num_examples: 18928 - name: epoch_26 num_bytes: 44350409 num_examples: 18928 - name: epoch_27 num_bytes: 44349426 num_examples: 18928 - name: epoch_28 num_bytes: 44348904 num_examples: 18928 - name: epoch_29 num_bytes: 44349202 num_examples: 18928 download_size: 700096785 dataset_size: 1330072981 configs: - config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1 data_files: - split: epoch_0 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-* - split: epoch_5 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-* - split: epoch_6 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-* - split: epoch_7 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-* - split: epoch_8 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-* - split: epoch_9 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-* - split: epoch_10 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-* - split: epoch_11 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-* - split: epoch_12 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-* - split: epoch_13 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-* - split: epoch_14 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-* - split: epoch_15 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-* - split: epoch_16 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-* - split: epoch_17 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-* - split: epoch_18 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-* - split: epoch_19 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-* - split: epoch_20 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-* - split: epoch_21 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-* - split: epoch_22 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-* - split: epoch_23 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-* - split: epoch_24 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-* - split: epoch_25 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-* - split: epoch_26 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-* - split: epoch_27 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-* - split: epoch_28 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-* - split: epoch_29 path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-* ---

open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora

--- pretty_name: Evaluation run of Aratako/Beyonder-4x7B-random-lora dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Aratako/Beyonder-4x7B-random-lora](https://huggingface.co/Aratako/Beyonder-4x7B-random-lora)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-04-02T20:16:42.836942](https://huggingface.co/datasets/open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora/blob/main/results_2024-04-02T20-16-42.836942.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\": 0.6524116122888561,\n\ \ \"acc_stderr\": 0.03209147540288013,\n \"acc_norm\": 0.6526918811196057,\n\ \ \"acc_norm_stderr\": 0.032750650646658046,\n \"mc1\": 0.5312117503059975,\n\ \ \"mc1_stderr\": 0.01746936487457753,\n \"mc2\": 0.7049250833848263,\n\ \ \"mc2_stderr\": 0.014693924406157995\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6868600682593856,\n \"acc_stderr\": 0.013552671543623496,\n\ \ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.01322671905626613\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6922923720374428,\n\ \ \"acc_stderr\": 0.004606015773125624,\n \"acc_norm\": 0.8740290778729337,\n\ \ \"acc_norm_stderr\": 0.0033113844981586364\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\ \ \"acc_stderr\": 0.04115324610336953,\n \"acc_norm\": 0.6518518518518519,\n\ \ \"acc_norm_stderr\": 0.04115324610336953\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6907894736842105,\n \"acc_stderr\": 0.037610708698674805,\n\ \ \"acc_norm\": 0.6907894736842105,\n \"acc_norm_stderr\": 0.037610708698674805\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n\ \ \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \ \ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337124,\n\ \ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337124\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\ \ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\ \ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\ : 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.630057803468208,\n\ \ \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n\ \ \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.73,\n\ \ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\ \ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\ \ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4417989417989418,\n \"acc_stderr\": 0.025576257061253833,\n \"\ acc_norm\": 0.4417989417989418,\n \"acc_norm_stderr\": 0.025576257061253833\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726855,\n \"\ acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726855\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.5172413793103449,\n \"acc_stderr\": 0.03515895551165698,\n \"\ acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.03515895551165698\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\ : 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\ \ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218974,\n \"\ acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218974\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\ \ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\ \ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.29259259259259257,\n \"acc_stderr\": 0.027738969632176085,\n \ \ \"acc_norm\": 0.29259259259259257,\n \"acc_norm_stderr\": 0.027738969632176085\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\ acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8385321100917431,\n \"acc_stderr\": 0.01577623925616323,\n \"\ acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.01577623925616323\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\ acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568603,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568603\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579654,\n \ \ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579654\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\ \ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\ \ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7520661157024794,\n \"acc_stderr\": 0.039418975265163025,\n \"\ acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.039418975265163025\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\ \ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\ \ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.03351953879521271,\n\ \ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.03351953879521271\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\ \ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\ \ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\ \ \"acc_stderr\": 0.02093019318517933,\n \"acc_norm\": 0.8846153846153846,\n\ \ \"acc_norm_stderr\": 0.02093019318517933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\ \ \"acc_stderr\": 0.013507943909371802,\n \"acc_norm\": 0.8275862068965517,\n\ \ \"acc_norm_stderr\": 0.013507943909371802\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500097,\n\ \ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500097\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.38212290502793295,\n\ \ \"acc_stderr\": 0.016251139711570765,\n \"acc_norm\": 0.38212290502793295,\n\ \ \"acc_norm_stderr\": 0.016251139711570765\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137894,\n\ \ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137894\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\ \ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\ \ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7469135802469136,\n \"acc_stderr\": 0.024191808600713,\n\ \ \"acc_norm\": 0.7469135802469136,\n \"acc_norm_stderr\": 0.024191808600713\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\ \ \"acc_stderr\": 0.01275285834653313,\n \"acc_norm\": 0.47392438070404175,\n\ \ \"acc_norm_stderr\": 0.01275285834653313\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462937,\n\ \ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462937\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6715686274509803,\n \"acc_stderr\": 0.018999707383162673,\n \ \ \"acc_norm\": 0.6715686274509803,\n \"acc_norm_stderr\": 0.018999707383162673\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\ \ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\ \ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128448,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128448\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \ \ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\ \ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5312117503059975,\n\ \ \"mc1_stderr\": 0.01746936487457753,\n \"mc2\": 0.7049250833848263,\n\ \ \"mc2_stderr\": 0.014693924406157995\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.01075935201485593\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6739954510993177,\n \ \ \"acc_stderr\": 0.012911675645682841\n }\n}\n```" repo_url: https://huggingface.co/Aratako/Beyonder-4x7B-random-lora leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|arc:challenge|25_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-04-02T20-16-42.836942.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|gsm8k|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hellaswag|10_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-management|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-04-02T20-16-42.836942.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-management|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-virology|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T20-16-42.836942.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|truthfulqa:mc|0_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-04-02T20-16-42.836942.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_04_02T20_16_42.836942 path: - '**/details_harness|winogrande|5_2024-04-02T20-16-42.836942.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-04-02T20-16-42.836942.parquet' - config_name: results data_files: - split: 2024_04_02T20_16_42.836942 path: - results_2024-04-02T20-16-42.836942.parquet - split: latest path: - results_2024-04-02T20-16-42.836942.parquet --- # Dataset Card for Evaluation run of Aratako/Beyonder-4x7B-random-lora  Dataset automatically created during the evaluation run of model [Aratako/Beyonder-4x7B-random-lora](https://huggingface.co/Aratako/Beyonder-4x7B-random-lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-04-02T20:16:42.836942](https://huggingface.co/datasets/open-llm-leaderboard/details_Aratako__Beyonder-4x7B-random-lora/blob/main/results_2024-04-02T20-16-42.836942.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": 0.6524116122888561, "acc_stderr": 0.03209147540288013, "acc_norm": 0.6526918811196057, "acc_norm_stderr": 0.032750650646658046, "mc1": 0.5312117503059975, "mc1_stderr": 0.01746936487457753, "mc2": 0.7049250833848263, "mc2_stderr": 0.014693924406157995 }, "harness|arc:challenge|25": { "acc": 0.6868600682593856, "acc_stderr": 0.013552671543623496, "acc_norm": 0.712457337883959, "acc_norm_stderr": 0.01322671905626613 }, "harness|hellaswag|10": { "acc": 0.6922923720374428, "acc_stderr": 0.004606015773125624, "acc_norm": 0.8740290778729337, "acc_norm_stderr": 0.0033113844981586364 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6518518518518519, "acc_stderr": 0.04115324610336953, "acc_norm": 0.6518518518518519, "acc_norm_stderr": 0.04115324610336953 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6907894736842105, "acc_stderr": 0.037610708698674805, "acc_norm": 0.6907894736842105, "acc_norm_stderr": 0.037610708698674805 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7094339622641509, "acc_stderr": 0.027943219989337124, "acc_norm": 0.7094339622641509, "acc_norm_stderr": 0.027943219989337124 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7638888888888888, "acc_stderr": 0.03551446610810826, "acc_norm": 0.7638888888888888, "acc_norm_stderr": 0.03551446610810826 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.47, "acc_stderr": 0.050161355804659205, "acc_norm": 0.47, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.630057803468208, "acc_stderr": 0.0368122963339432, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.0368122963339432 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5531914893617021, "acc_stderr": 0.0325005368436584, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.0325005368436584 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5655172413793104, "acc_stderr": 0.04130740879555498, "acc_norm": 0.5655172413793104, "acc_norm_stderr": 0.04130740879555498 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4417989417989418, "acc_stderr": 0.025576257061253833, "acc_norm": 0.4417989417989418, "acc_norm_stderr": 0.025576257061253833 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5, "acc_stderr": 0.04472135954999579, "acc_norm": 0.5, "acc_norm_stderr": 0.04472135954999579 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7870967741935484, "acc_stderr": 0.02328766512726855, "acc_norm": 0.7870967741935484, "acc_norm_stderr": 0.02328766512726855 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5172413793103449, "acc_stderr": 0.03515895551165698, "acc_norm": 0.5172413793103449, "acc_norm_stderr": 0.03515895551165698 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.68, "acc_stderr": 0.04688261722621505, "acc_norm": 0.68, "acc_norm_stderr": 0.04688261722621505 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7818181818181819, "acc_stderr": 0.03225078108306289, "acc_norm": 0.7818181818181819, "acc_norm_stderr": 0.03225078108306289 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8131313131313131, "acc_stderr": 0.027772533334218974, "acc_norm": 0.8131313131313131, "acc_norm_stderr": 0.027772533334218974 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8963730569948186, "acc_stderr": 0.02199531196364424, "acc_norm": 0.8963730569948186, "acc_norm_stderr": 0.02199531196364424 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402538, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402538 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.29259259259259257, "acc_stderr": 0.027738969632176085, "acc_norm": 0.29259259259259257, "acc_norm_stderr": 0.027738969632176085 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.03038835355188679, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.03038835355188679 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8385321100917431, "acc_stderr": 0.01577623925616323, "acc_norm": 0.8385321100917431, "acc_norm_stderr": 0.01577623925616323 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5509259259259259, "acc_stderr": 0.03392238405321617, "acc_norm": 0.5509259259259259, "acc_norm_stderr": 0.03392238405321617 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8578431372549019, "acc_stderr": 0.024509803921568603, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.024509803921568603 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8185654008438819, "acc_stderr": 0.025085961144579654, "acc_norm": 0.8185654008438819, "acc_norm_stderr": 0.025085961144579654 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6860986547085202, "acc_stderr": 0.031146796482972465, "acc_norm": 0.6860986547085202, "acc_norm_stderr": 0.031146796482972465 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7520661157024794, "acc_stderr": 0.039418975265163025, "acc_norm": 0.7520661157024794, "acc_norm_stderr": 0.039418975265163025 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7607361963190185, "acc_stderr": 0.03351953879521271, "acc_norm": 0.7607361963190185, "acc_norm_stderr": 0.03351953879521271 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.48214285714285715, "acc_stderr": 0.047427623612430116, "acc_norm": 0.48214285714285715, "acc_norm_stderr": 0.047427623612430116 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8846153846153846, "acc_stderr": 0.02093019318517933, "acc_norm": 0.8846153846153846, "acc_norm_stderr": 0.02093019318517933 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8275862068965517, "acc_stderr": 0.013507943909371802, "acc_norm": 0.8275862068965517, "acc_norm_stderr": 0.013507943909371802 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7312138728323699, "acc_stderr": 0.023868003262500097, "acc_norm": 0.7312138728323699, "acc_norm_stderr": 0.023868003262500097 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.38212290502793295, "acc_stderr": 0.016251139711570765, "acc_norm": 0.38212290502793295, "acc_norm_stderr": 0.016251139711570765 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7222222222222222, "acc_stderr": 0.025646863097137894, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.025646863097137894 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6977491961414791, "acc_stderr": 0.02608270069539966, "acc_norm": 0.6977491961414791, "acc_norm_stderr": 0.02608270069539966 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7469135802469136, "acc_stderr": 0.024191808600713, "acc_norm": 0.7469135802469136, "acc_norm_stderr": 0.024191808600713 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.49645390070921985, "acc_stderr": 0.02982674915328092, "acc_norm": 0.49645390070921985, "acc_norm_stderr": 0.02982674915328092 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47392438070404175, "acc_stderr": 0.01275285834653313, "acc_norm": 0.47392438070404175, "acc_norm_stderr": 0.01275285834653313 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462937, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462937 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6715686274509803, "acc_stderr": 0.018999707383162673, "acc_norm": 0.6715686274509803, "acc_norm_stderr": 0.018999707383162673 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.028535560337128448, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.028535560337128448 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616913, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616913 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.0348735088019777, "acc_norm": 0.86, "acc_norm_stderr": 0.0348735088019777 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8362573099415205, "acc_stderr": 0.028380919596145866, "acc_norm": 0.8362573099415205, "acc_norm_stderr": 0.028380919596145866 }, "harness|truthfulqa:mc|0": { "mc1": 0.5312117503059975, "mc1_stderr": 0.01746936487457753, "mc2": 0.7049250833848263, "mc2_stderr": 0.014693924406157995 }, "harness|winogrande|5": { "acc": 0.8216258879242304, "acc_stderr": 0.01075935201485593 }, "harness|gsm8k|5": { "acc": 0.6739954510993177, "acc_stderr": 0.012911675645682841 } } ``` ## 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]

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/61f4b25b

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 184 num_examples: 10 download_size: 1340 dataset_size: 184 --- # Dataset Card for "61f4b25b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jmaciejowski/multi_news_tokenized_pegasus_large

--- dataset_info: features: - name: document dtype: string - name: summary dtype: string - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 851951820 num_examples: 44972 - name: validation num_bytes: 104816611 num_examples: 5622 - name: test num_bytes: 106588998 num_examples: 5622 download_size: 537120663 dataset_size: 1063357429 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

Indic-LLM-Labs/CulturaX-Kn

--- language: - kn license: mit size_categories: - 1M<n<10M task_categories: - text-generation configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: text dtype: string - name: timestamp dtype: string - name: url dtype: string - name: source dtype: string splits: - name: train num_bytes: 10347179458 num_examples: 1352142 download_size: 3976072715 dataset_size: 10347179458 --- This is a filtered version of the [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) dataset only containing samples of Kannada language. The dataset contains total of 1352142 samples. ### Dataset Structure: ```python { "text": ..., "timestamp": ..., "url": ..., "source": "mc4" | "OSCAR-xxxx", } ``` ### Data Sample: ```python {'text': "ಭಟ್ಕಳ : ತಂದೆ ತಾಯಿ ಸ್ಮರಣಾರ್ಥ ; ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಣೆ | Vartha Bharati- ವಾರ್ತಾ ಭಾರತಿ\nಮುದರಂಗಡಿ ಬಿಜೆಪಿ ಗ್ರಾಪಂ ಸದಸ್ಯರ ವಿರುದ್ಧ ಪ್ರತಿಭಟನೆ\nಹೋಮ್ ಕ್ವಾರಂಟೈನ್ ನಿಯಮ ಉಲ್ಲಂಘನೆ: ಪ್ರಕರಣ ದಾಖಲು\nಭಟ್ಕಳ : ತಂದೆ ತಾಯಿ ಸ್ಮರಣಾರ್ಥ ; ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಣೆ\nವಾರ್ತಾ ಭಾರತಿ Jun 19, 2019, 10:52 PM IST\nಭಟ್ಕಳ : ತಾಲೂಕಿನ ಹುರುಳಿಸಾಲಿನ ನಿವಾಸಿಗಳಾದ ವೃತ್ತಿಯಲ್ಲಿ ಶಿಕ್ಷಕರಾದ ವೆಂಕಟೇಶ ನಾರಾಯಣ ನಾಯ್ಕ ಪಟೇಲರಮನೆ ಇವರ ತಂದೆ ತಾಯಿಗಳ ಅಕಾಲಿಕ ಮರಣದಿಂದ ಅವರ ಮರಣ ದಿನದ ಸವಿನೆನಪಿಗಾಗಿ ಕಳೆದ 9 ವರ್ಷದಿಂದ ಇಲ್ಲಿನ ಶಾಲಾ ಮಕ್ಕಳಿಗೆ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸುತ್ತಾ ಬಂದಿದ್ದು, ಮಂಗಳವಾರದಂದು ಇಲ್ಲಿನ ಸರಕಾರಿ ಹಿರಿಯ ಪ್ರಾಥಮಿಕ ಶಾಲೆ ಮುಟ್ಟಳ್ಳಿಗೆ ತೆರಳಿ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ನೋಟ್ ವಿತರಿಸಿದರು.\nನೋಟ್ ಬುಕ್ ವಿತರಣೆ ಮಾಡಿ ಮಾತನಾಡಿದ ಶಿಕ್ಷಕ ವೆಂಕಟೇಶ ನಾಯ್ಕ 'ವಿದ್ಯಾರ್ಥಿಗಳ ಭವಿಷ್ಯದ ದಿಸೆಯಿಂದ ಹಾಗೂ ತಂದೆ-ತಾಯಿಗಳ ಸವಿನೆನಪಿಗಾಗಿ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸಲಾಗುತ್ತಿದೆ. ದುಡಿಮೆಯ ಒಂದು ಭಾಗವನ್ನು ಸಮಾಜಮುಖಿ ಕೆಲಕ್ಕೆ ಪ್ರತಿ ವರ್ಷ, ನನ್ನ ಮಡದಿ ಜಯಲಕ್ಷ್ಮೀ ನಾಯ್ಕ ಅವರ ಸಹಕಾರದಿಂದ ಕುಟುಂಬದವರ ಸಹಕಾರದಿಂದ ಈ ಕಾರ್ಯ ಮಾಡುತ್ತಿದ್ದೇನೆ. ಸಮಾಜದಲ್ಲಿ ಎಷ್ಟೇ ಎತ್ತರಕ್ಕೆ ಬೆಳೆದರು ತಂದೆತಾಯಿಗಳ ಹಾಗೂ ಗುರುಗಳ ಋಣ ತೀರಿಸಲು ಸಾಧ್ಯವಿಲ್ಲ. ನಾನು ಮಾಡಿದ ಕಾರ್ಯವನ್ನು ಮುಂದಿನ ದಿನದಲ್ಲಿ ದುಡಿಯುವ ವೇಳೆ ನಿಮ್ಮದಿಂದಾಗುವಷ್ಟು ಸಹಾಯ ಸೇವೆ ಮಾಡಿ ಎಂದು ಕರೆ ನೀಡಿದರು.\nನಂತರ ದಂತ ವೈದ್ಯರಾದ ಡಾ. ರವಿ ಮಾತನಾಡಿ ನಮ್ಮ ಸಮಾಜದಲ್ಲಿ ಇಂತಹ ವ್ಯಕ್ತಿಗಳಿರುವದರಿಂದ ನಮ್ಮ ಸಮಾಜವು ಏಳಿಗೆಯತ್ತ ಮುಖ ಮಾಡುತ್ತದೆ. ಮಕ್ಕಳಾದ ನಾವು ಎಲ್ಲೇ ಇರಿಬಹುದು ಹೇಗೆ ಇರಿಬಹುದ ಆದರೆ ತಂದೆ ತಾಯಿಗಳು ನಮಗೆ ಮಾಡಿರುವ ತ್ಯಾಗಕ್ಕೆ ನಾವು ಋಣ ತೀರಿಸಲು ಸಾಧ್ಯವಾಗದಿದ್ದರು ಇಂತಹ ಕೆಲಸ ಮಾಡಿ ಅವರ ತ್ಯಾಗಕ್ಕೆ ಪ್ರತಿಫಲ ಕೊಟ್ಟಂತೆ ಆಗುತ್ತದೆ ಅಂದು ಕಿವಿ ಮಾತನ್ನು ಮಕ್ಕಳಿಗೆ ಹೇಳಿದರು.\nಈ ಸಂಧರ್ಭದಲ್ಲಿ ಮುಟ್ಟಳ್ಳಿ ಶಾಲಾ ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ ಉಚಿತ ನೋಟ್ ಬುಕ್ ವಿತರಿಸಿದರು.\nಈಗಿನ ಇಲೆಕ್ಟ್ರಾನಿಕ ಜೀವನ ಶೈಲಿಯಲ್ಲಿ ಸಾಕಿದ ತಂದೆ ತಾಯಿಗಳನ್ನು ಅನಾಥಾಶ್ರಾಮಕ್ಕೊ ಅಥವಾ ದಾರಿಯ ಮೇಲೋ ಮನೆಯಿಂದ ಹೊರಗೆ ಹಾಕುವ ಮಕ್ಕಳ ನಡುವೆ ಅವರ ಅಕಾಲಿಕ ಮರಣದಿಂದ ನೊಂದು ಅವರ ಸವಿನೆನಪನ್ನು ಉತ್ತಮ ಕಾರ್ಯ ಮಾಡುವುದರೊಂದಿಗೆ ಸಾರ್ಥಕತೆಯನ್ನು ಮೆರೆದಿದ್ದಾರೆ.\nಈ ಸಂಧರ್ಭದಲ್ಲಿ ಶಾಲೆಯ ಎಸ್.ಡಿ. ಎಂ ಅಧ್ಯಕ್ಷರಾದ ವೆಂಕಟೇಶ ನಾಯ್ಕ, ರಾಜ್ಯ ಸರಕಾರಿ ನೌಕರರ ಸಂಘ ಸದಸ್ಯ ಬಿ.ಕೆ.ನಾಯ್ಕ, ಶಿಕ್ಷಕ ಸಿ.ಡಿ.ಪಡುವಣಿ, ಗಜಾನನ ನಾಯ್ಕ ಮುಖ್ಯ ಶಿಕ್ಷಕರು ವೆಂಕಟೇಶ್ ದೇವಡಿಗ್ ಶಿಕ್ಷಕರು ಉಪಸ್ಥಿತರಿದ್ದರು.", 'timestamp': '2020/07/07 13:00:41', 'url': 'http://www.varthabharati.in/article/karavali/196595', 'source': 'mC4'} ``` ### Use with Datasets ```python from datasets import load_dataset ds = load_dataset("Indic-LLM-Labs/CulturaX-Kn") ```

nmarafo/truthful_qa_TrueFalse

--- license: apache-2.0 task_categories: - table-question-answering language: - en --- # Dataset Card for Dataset Name This is a reduced variation of the truthful_qa dataset (https://huggingface.co/datasets/truthful_qa), modified to associate boolean values with the given answers, with a correct answer as a reference. ## 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] TruthfulQA: @misc{lin2021truthfulqa, title={TruthfulQA: Measuring How Models Mimic Human Falsehoods}, author={Stephanie Lin and Jacob Hilton and Owain Evans}, year={2021}, eprint={2109.07958}, archivePrefix={arXiv}, primaryClass={cs.CL} } **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]

Thundergb/Cesarbr

--- license: openrail ---

CyberHarem/charlotte_genshin

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of charlotte/シャルロット/夏洛蒂 (Genshin Impact) This is the dataset of charlotte/シャルロット/夏洛蒂 (Genshin Impact), containing 276 images and their tags. The core tags of this character are `pink_hair, hat, red_headwear, cabbie_hat, aqua_eyes, medium_hair, breasts, short_hair, 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 | 276 | 560.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_genshin/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 276 | 461.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_genshin/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 710 | 968.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_genshin/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/charlotte_genshin', 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 | 24 | ![](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, monocle, solo, white_gloves, white_shirt, bare_shoulders, detached_sleeves, looking_at_viewer, long_sleeves, open_mouth, red_sleeves, upper_body, holding_camera, hat_feather, suspenders, white_background, :d, sleeveless_shirt, simple_background, bow, puffy_sleeves, upper_teeth_only, gem, blush | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, bare_shoulders, detached_sleeves, long_sleeves, looking_at_viewer, monocle, red_sleeves, simple_background, sleeveless_shirt, solo, white_background, white_shirt, white_gloves, blue_gemstone, hat_feather, skirt, suspenders, puffy_sleeves, white_belt, sideboob, thighs, blush, brooch, open_mouth, :d, thigh_strap, cowboy_shot, upper_body | | 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, bare_shoulders, hat_feather, holding_camera, monocle, simple_background, solo, upper_body, white_background, looking_at_viewer, smile, white_gloves, blush, green_eyes, open_mouth, detached_sleeves, long_sleeves | | 3 | 8 | ![](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, bare_shoulders, blue_gemstone, blue_sky, day, long_sleeves, monocle, outdoors, red_sleeves, sleeveless_shirt, solo, white_gloves, white_shirt, hat_feather, holding_camera, looking_at_viewer, open_mouth, cloud, suspenders, upper_body, blush, brooch, upper_teeth_only, :d, bow, puffy_detached_sleeves, white_belt, blurry_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | monocle | solo | white_gloves | white_shirt | bare_shoulders | detached_sleeves | looking_at_viewer | long_sleeves | open_mouth | red_sleeves | upper_body | holding_camera | hat_feather | suspenders | white_background | :d | sleeveless_shirt | simple_background | bow | puffy_sleeves | upper_teeth_only | gem | blush | blue_gemstone | skirt | white_belt | sideboob | thighs | brooch | thigh_strap | cowboy_shot | smile | green_eyes | blue_sky | day | outdoors | cloud | puffy_detached_sleeves | blurry_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-------|:---------------|:--------------|:-----------------|:-------------------|:--------------------|:---------------|:-------------|:--------------|:-------------|:-----------------|:--------------|:-------------|:-------------------|:-----|:-------------------|:--------------------|:------|:----------------|:-------------------|:------|:--------|:----------------|:--------|:-------------|:-----------|:---------|:---------|:--------------|:--------------|:--------|:-------------|:-----------|:------|:-----------|:--------|:-------------------------|:--------------------| | 0 | 24 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | | X | | | X | X | 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 | | | | | | | | 3 | 8 | ![](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 | X | X | X |

open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1

--- pretty_name: Evaluation run of Sao10K/Franziska-Mixtral-v1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Sao10K/Franziska-Mixtral-v1](https://huggingface.co/Sao10K/Franziska-Mixtral-v1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-31T21:01:34.382668](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1/blob/main/results_2024-03-31T21-01-34.382668.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\": 0.6978344149015431,\n\ \ \"acc_stderr\": 0.030588664141069415,\n \"acc_norm\": 0.7011436233589318,\n\ \ \"acc_norm_stderr\": 0.03118141743792802,\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.7006977726617969,\n\ \ \"mc2_stderr\": 0.014781576553666215\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688065,\n\ \ \"acc_norm\": 0.7175767918088737,\n \"acc_norm_stderr\": 0.013155456884097222\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6922923720374428,\n\ \ \"acc_stderr\": 0.004606015773125624,\n \"acc_norm\": 0.8737303326030671,\n\ \ \"acc_norm_stderr\": 0.0033147420770833183\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\ \ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6888888888888889,\n\ \ \"acc_stderr\": 0.039992628766177214,\n \"acc_norm\": 0.6888888888888889,\n\ \ \"acc_norm_stderr\": 0.039992628766177214\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.033176727875331574,\n\ \ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.033176727875331574\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\ \ \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n \ \ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.769811320754717,\n \"acc_stderr\": 0.02590789712240817,\n\ \ \"acc_norm\": 0.769811320754717,\n \"acc_norm_stderr\": 0.02590789712240817\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\ \ \"acc_stderr\": 0.03216600808802269,\n \"acc_norm\": 0.8194444444444444,\n\ \ \"acc_norm_stderr\": 0.03216600808802269\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7109826589595376,\n\ \ \"acc_stderr\": 0.03456425745086999,\n \"acc_norm\": 0.7109826589595376,\n\ \ \"acc_norm_stderr\": 0.03456425745086999\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\ \ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6510638297872341,\n \"acc_stderr\": 0.031158522131357787,\n\ \ \"acc_norm\": 0.6510638297872341,\n \"acc_norm_stderr\": 0.031158522131357787\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\ \ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\ \ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n\ \ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\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\": 0.49206349206349204,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8516129032258064,\n\ \ \"acc_stderr\": 0.020222737554330378,\n \"acc_norm\": 0.8516129032258064,\n\ \ \"acc_norm_stderr\": 0.020222737554330378\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.03465304488406796,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.03465304488406796\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\ : 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695482995,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695482995\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8585858585858586,\n \"acc_stderr\": 0.02482590979334333,\n \"\ acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.02482590979334333\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9481865284974094,\n \"acc_stderr\": 0.01599622932024412,\n\ \ \"acc_norm\": 0.9481865284974094,\n \"acc_norm_stderr\": 0.01599622932024412\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206865,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206865\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.026265024608275882,\n\ \ \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.026265024608275882\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.44370860927152317,\n \"acc_stderr\": 0.04056527902281732,\n \"\ acc_norm\": 0.44370860927152317,\n \"acc_norm_stderr\": 0.04056527902281732\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8678899082568807,\n \"acc_stderr\": 0.014517801914598238,\n \"\ acc_norm\": 0.8678899082568807,\n \"acc_norm_stderr\": 0.014517801914598238\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n \"\ acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\ acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8649789029535865,\n \"acc_stderr\": 0.022245776632003694,\n \ \ \"acc_norm\": 0.8649789029535865,\n \"acc_norm_stderr\": 0.022245776632003694\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7219730941704036,\n\ \ \"acc_stderr\": 0.030069584874494043,\n \"acc_norm\": 0.7219730941704036,\n\ \ \"acc_norm_stderr\": 0.030069584874494043\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.0349814938546247,\n\ \ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.0349814938546247\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\ \ \"acc_stderr\": 0.03520703990517963,\n \"acc_norm\": 0.8425925925925926,\n\ \ \"acc_norm_stderr\": 0.03520703990517963\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\ \ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\ \ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\ \ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.0349260647662379,\n\ \ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.0349260647662379\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\ \ \"acc_stderr\": 0.019875655027867443,\n \"acc_norm\": 0.8974358974358975,\n\ \ \"acc_norm_stderr\": 0.019875655027867443\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8773946360153256,\n\ \ \"acc_stderr\": 0.011728672144131565,\n \"acc_norm\": 0.8773946360153256,\n\ \ \"acc_norm_stderr\": 0.011728672144131565\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7803468208092486,\n \"acc_stderr\": 0.022289638852617887,\n\ \ \"acc_norm\": 0.7803468208092486,\n \"acc_norm_stderr\": 0.022289638852617887\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.49050279329608937,\n\ \ \"acc_stderr\": 0.016719484643348752,\n \"acc_norm\": 0.49050279329608937,\n\ \ \"acc_norm_stderr\": 0.016719484643348752\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8104575163398693,\n \"acc_stderr\": 0.02244235826333621,\n\ \ \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.02244235826333621\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\ \ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\ \ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.020736358408060006,\n\ \ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.020736358408060006\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \ \ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5417209908735332,\n\ \ \"acc_stderr\": 0.012725701656953642,\n \"acc_norm\": 0.5417209908735332,\n\ \ \"acc_norm_stderr\": 0.012725701656953642\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7720588235294118,\n \"acc_stderr\": 0.025483081468029804,\n\ \ \"acc_norm\": 0.7720588235294118,\n \"acc_norm_stderr\": 0.025483081468029804\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7418300653594772,\n \"acc_stderr\": 0.017704531653250068,\n \ \ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.017704531653250068\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866764,\n\ \ \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866764\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n\ \ \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n\ \ \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \ \ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\ \ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\ \ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276894,\n\ \ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276894\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5471236230110159,\n\ \ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.7006977726617969,\n\ \ \"mc2_stderr\": 0.014781576553666215\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8089976322020521,\n \"acc_stderr\": 0.011047808761510423\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6027293404094011,\n \ \ \"acc_stderr\": 0.013478659652337792\n }\n}\n```" repo_url: https://huggingface.co/Sao10K/Franziska-Mixtral-v1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|arc:challenge|25_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-31T21-01-34.382668.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|gsm8k|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hellaswag|10_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-31T21-01-34.382668.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-management|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-31T21-01-34.382668.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|truthfulqa:mc|0_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-31T21-01-34.382668.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_31T21_01_34.382668 path: - '**/details_harness|winogrande|5_2024-03-31T21-01-34.382668.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-31T21-01-34.382668.parquet' - config_name: results data_files: - split: 2024_03_31T21_01_34.382668 path: - results_2024-03-31T21-01-34.382668.parquet - split: latest path: - results_2024-03-31T21-01-34.382668.parquet --- # Dataset Card for Evaluation run of Sao10K/Franziska-Mixtral-v1  Dataset automatically created during the evaluation run of model [Sao10K/Franziska-Mixtral-v1](https://huggingface.co/Sao10K/Franziska-Mixtral-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-31T21:01:34.382668](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Franziska-Mixtral-v1/blob/main/results_2024-03-31T21-01-34.382668.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": 0.6978344149015431, "acc_stderr": 0.030588664141069415, "acc_norm": 0.7011436233589318, "acc_norm_stderr": 0.03118141743792802, "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.7006977726617969, "mc2_stderr": 0.014781576553666215 }, "harness|arc:challenge|25": { "acc": 0.6877133105802048, "acc_stderr": 0.013542598541688065, "acc_norm": 0.7175767918088737, "acc_norm_stderr": 0.013155456884097222 }, "harness|hellaswag|10": { "acc": 0.6922923720374428, "acc_stderr": 0.004606015773125624, "acc_norm": 0.8737303326030671, "acc_norm_stderr": 0.0033147420770833183 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6888888888888889, "acc_stderr": 0.039992628766177214, "acc_norm": 0.6888888888888889, "acc_norm_stderr": 0.039992628766177214 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7894736842105263, "acc_stderr": 0.033176727875331574, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.033176727875331574 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768078, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768078 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.769811320754717, "acc_stderr": 0.02590789712240817, "acc_norm": 0.769811320754717, "acc_norm_stderr": 0.02590789712240817 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8194444444444444, "acc_stderr": 0.03216600808802269, "acc_norm": 0.8194444444444444, "acc_norm_stderr": 0.03216600808802269 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7109826589595376, "acc_stderr": 0.03456425745086999, "acc_norm": 0.7109826589595376, "acc_norm_stderr": 0.03456425745086999 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.43137254901960786, "acc_stderr": 0.04928099597287534, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.04928099597287534 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6510638297872341, "acc_stderr": 0.031158522131357787, "acc_norm": 0.6510638297872341, "acc_norm_stderr": 0.031158522131357787 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5964912280701754, "acc_stderr": 0.04615186962583707, "acc_norm": 0.5964912280701754, "acc_norm_stderr": 0.04615186962583707 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5724137931034483, "acc_stderr": 0.04122737111370333, "acc_norm": 0.5724137931034483, "acc_norm_stderr": 0.04122737111370333 }, "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": 0.49206349206349204, "acc_stderr": 0.044715725362943486, "acc_norm": 0.49206349206349204, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8516129032258064, "acc_stderr": 0.020222737554330378, "acc_norm": 0.8516129032258064, "acc_norm_stderr": 0.020222737554330378 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5862068965517241, "acc_stderr": 0.03465304488406796, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.03465304488406796 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.031922715695482995, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.031922715695482995 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8585858585858586, "acc_stderr": 0.02482590979334333, "acc_norm": 0.8585858585858586, "acc_norm_stderr": 0.02482590979334333 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9481865284974094, "acc_stderr": 0.01599622932024412, "acc_norm": 0.9481865284974094, "acc_norm_stderr": 0.01599622932024412 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.362962962962963, "acc_stderr": 0.029318203645206865, "acc_norm": 0.362962962962963, "acc_norm_stderr": 0.029318203645206865 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7941176470588235, "acc_stderr": 0.026265024608275882, "acc_norm": 0.7941176470588235, "acc_norm_stderr": 0.026265024608275882 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.44370860927152317, "acc_stderr": 0.04056527902281732, "acc_norm": 0.44370860927152317, "acc_norm_stderr": 0.04056527902281732 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8678899082568807, "acc_stderr": 0.014517801914598238, "acc_norm": 0.8678899082568807, "acc_norm_stderr": 0.014517801914598238 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5879629629629629, "acc_stderr": 0.03356787758160831, "acc_norm": 0.5879629629629629, "acc_norm_stderr": 0.03356787758160831 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8480392156862745, "acc_stderr": 0.025195658428931792, "acc_norm": 0.8480392156862745, "acc_norm_stderr": 0.025195658428931792 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8649789029535865, "acc_stderr": 0.022245776632003694, "acc_norm": 0.8649789029535865, "acc_norm_stderr": 0.022245776632003694 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7219730941704036, "acc_stderr": 0.030069584874494043, "acc_norm": 0.7219730941704036, "acc_norm_stderr": 0.030069584874494043 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8015267175572519, "acc_stderr": 0.0349814938546247, "acc_norm": 0.8015267175572519, "acc_norm_stderr": 0.0349814938546247 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8760330578512396, "acc_stderr": 0.030083098716035202, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 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}, "harness|truthfulqa:mc|0": { "mc1": 0.5471236230110159, "mc1_stderr": 0.01742558984831402, "mc2": 0.7006977726617969, "mc2_stderr": 0.014781576553666215 }, "harness|winogrande|5": { "acc": 0.8089976322020521, "acc_stderr": 0.011047808761510423 }, "harness|gsm8k|5": { "acc": 0.6027293404094011, "acc_stderr": 0.013478659652337792 } } ``` ## 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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mrachilles/NTU60PointsComplete

--- license: mit ---

toilaluan/reward_tuned_prompt_v1

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: model_type dtype: string - name: request_id dtype: int64 - name: topic dtype: string - name: reward dtype: float64 - name: individual_rewards struct: - name: clip_aesthetic_rewarder dtype: float64 - name: pick_rewarder dtype: float64 - name: image_rewarder dtype: float64 - name: hps_v2_rewarder dtype: float64 splits: - name: train num_bytes: 463200 num_examples: 4500 download_size: 160093 dataset_size: 463200 --- # Dataset Card for "reward_tuned_prompt_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)