davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
umd-zhou-lab/sRecycled_Alpaca	--- dataset_info: features: - name: data struct: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 62380384 num_examples: 37114 download_size: 33868520 dataset_size: 62380384 --- # Dataset Card for "sRecycled_Alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
crumb/Clean-Instruct-3M	--- language: en dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 1899089494.3291264 num_examples: 3085812 download_size: 1173097125 dataset_size: 1899089494.3291264 --- # Dataset Card for "Clean-Instruct-3M" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dim/competition_math	--- dataset_info: features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string splits: - name: train num_bytes: 5984772 num_examples: 7500 download_size: 2992145 dataset_size: 5984772 --- # Dataset Card for "competition_math" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yimingzhang/uf_safe_v1	--- configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train_prefs num_bytes: 794558 num_examples: 1766 - name: test_prefs num_bytes: 39816 num_examples: 86 download_size: 468541 dataset_size: 834374 --- # Dataset Card for "uf_safe_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/jeanne_d_arc_fgo	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jeanne_d_arc/ジャンヌ・ダルク/贞德 (Fate/Grand Order) This is the dataset of jeanne_d_arc/ジャンヌ・ダルク/贞德 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, blue_eyes, very_long_hair, large_breasts, braid, single_braid`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 833.90 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_fgo/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 500 \| 727.32 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_fgo/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1256 \| 1.33 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_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/jeanne_d_arc_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 \| 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, armored_dress, gauntlets, headpiece, looking_at_viewer, solo, sword, faulds, chain, holding, sheath, smile, white_background, flag, simple_background, black_thighhighs, capelet, bare_shoulders, fur_trim \| \| 1 \| 9 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, armored_dress, bare_shoulders, chain, fur_trim, gauntlets, headpiece, solo, flag, looking_at_viewer, smile, closed_mouth, holding, faulds \| \| 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, bare_shoulders, chain, headpiece, looking_at_viewer, solo, upper_body, closed_mouth, fur_trim, gauntlets, smile, armored_dress \| \| 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, capelet, headpiece, looking_at_viewer, solo, upper_body, chain, gauntlets, smile, armored_dress, blush, closed_mouth, white_background, simple_background \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, headpiece, looking_at_viewer, smile, solo, white_dress, cleavage, long_sleeves, open_mouth, simple_background, white_background \| \| 5 \| 16 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, solo, looking_at_viewer, smile, bare_shoulders, closed_mouth, black_bow, black_dress, see-through, black_gloves, black_thighhighs, blush, cleavage, choker, collarbone, hair_flower, medium_breasts, hair_bow, necklace, hair_between_eyes, ribbon, skirt_hold, side_slit, standing \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, black_bikini, black_hairband, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, solo, black_scrunchie, blue_jacket, blue_sky, blush, cleavage, collarbone, day, hair_scrunchie, outdoors, cloud, hair_between_eyes, navel, ocean, open_mouth, thigh_strap, :d, ass_visible_through_thighs, closed_mouth, front-tie_bikini_top, holding, long_braid, sitting, standing, water \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, floral_print, looking_at_viewer, obi, solo, headpiece, print_kimono, wide_sleeves, long_sleeves, smile, alternate_costume, blush, closed_mouth, yukata, blue_kimono, flower, hair_between_eyes, hair_ornament, holding_umbrella, white_background \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, solo, blush, looking_at_viewer, necktie, sleeveless_shirt, smile, white_shirt, bare_shoulders, short_shorts \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| armored_dress \| gauntlets \| headpiece \| looking_at_viewer \| solo \| sword \| faulds \| chain \| holding \| sheath \| smile \| white_background \| flag \| simple_background \| black_thighhighs \| capelet \| bare_shoulders \| fur_trim \| closed_mouth \| upper_body \| blush \| white_dress \| cleavage \| long_sleeves \| open_mouth \| black_bow \| black_dress \| see-through \| black_gloves \| choker \| collarbone \| hair_flower \| medium_breasts \| hair_bow \| necklace \| hair_between_eyes \| ribbon \| skirt_hold \| side_slit \| standing \| black_bikini \| black_hairband \| hooded_jacket \| open_jacket \| black_scrunchie \| blue_jacket \| blue_sky \| day \| hair_scrunchie \| outdoors \| cloud \| navel \| ocean \| thigh_strap \| :d \| ass_visible_through_thighs \| front-tie_bikini_top \| long_braid \| sitting \| water \| floral_print \| obi \| print_kimono \| wide_sleeves \| alternate_costume \| yukata \| blue_kimono \| flower \| hair_ornament \| holding_umbrella \| necktie \| sleeveless_shirt \| white_shirt \| short_shorts \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:----------------\|:------------\|:------------\|:--------------------\|:-------\|:--------\|:---------\|:--------\|:----------\|:---------\|:--------\|:-------------------\|:-------\|:--------------------\|:-------------------\|:----------\|:-----------------\|:-----------\|:---------------\|:-------------\|:--------\|:--------------\|:-----------\|:---------------\|:-------------\|:------------\|:--------------\|:--------------\|:---------------\|:---------\|:-------------\|:--------------\|:-----------------\|:-----------\|:-----------\|:--------------------\|:---------\|:-------------\|:------------\|:-----------\|:---------------\|:-----------------\|:----------------\|:--------------\|:------------------\|:--------------\|:-----------\|:------\|:-----------------\|:-----------\|:--------\|:--------\|:--------\|:--------------\|:-----\|:-----------------------------\|:-----------------------\|:-------------\|:----------\|:--------\|:---------------\|:------\|:---------------\|:---------------\|:--------------------\|:---------\|:--------------\|:---------\|:----------------\|:-------------------\|:----------\|:-------------------\|:--------------\|:---------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| X \| X \| \| \| \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 16 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| \| X \| X \| \| \| \| \| \| X \| \| \| \| X \| \| X \| \| X \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| X \| X \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \|
open-llm-leaderboard/details_macadeliccc__MBX-7B-v3-DPO	--- pretty_name: Evaluation run of macadeliccc/MBX-7B-v3-DPO dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [macadeliccc/MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO)\ \ 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_macadeliccc__MBX-7B-v3-DPO\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-11T18:53:41.876317](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__MBX-7B-v3-DPO/blob/main/results_2024-02-11T18-53-41.876317.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\": 0.6554435066939919,\n\ \ \"acc_stderr\": 0.03198817220538892,\n \"acc_norm\": 0.6546676568515765,\n\ \ \"acc_norm_stderr\": 0.03266175930986744,\n \"mc1\": 0.5862913096695227,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.7399782698428227,\n\ \ \"mc2_stderr\": 0.014395363250478046\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7150170648464164,\n \"acc_stderr\": 0.013191348179838793,\n\ \ \"acc_norm\": 0.735494880546075,\n \"acc_norm_stderr\": 0.012889272949313368\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.715893248356901,\n\ \ \"acc_stderr\": 0.004500662294697923,\n \"acc_norm\": 0.8910575582553276,\n\ \ \"acc_norm_stderr\": 0.003109302300176215\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.674074074074074,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\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\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\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\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055277,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055277\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7935483870967742,\n\ \ \"acc_stderr\": 0.023025899617188716,\n \"acc_norm\": 0.7935483870967742,\n\ \ \"acc_norm_stderr\": 0.023025899617188716\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8181818181818182,\n \"acc_stderr\": 0.027479603010538797,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.027479603010538797\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.3708609271523179,\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\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621126,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621126\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\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159463\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\": 0.7777777777777778,\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406964\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\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508297,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508297\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4402234636871508,\n\ \ \"acc_stderr\": 0.016602564615049942,\n \"acc_norm\": 0.4402234636871508,\n\ \ \"acc_norm_stderr\": 0.016602564615049942\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\"\ : 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"\ acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\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\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616914,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616914\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685515\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\": 0.5862913096695227,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.7399782698428227,\n\ \ \"mc2_stderr\": 0.014395363250478046\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8555643251775849,\n \"acc_stderr\": 0.009879767358079232\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6967399545109931,\n \ \ \"acc_stderr\": 0.012661502663418697\n }\n}\n```" repo_url: https://huggingface.co/macadeliccc/MBX-7B-v3-DPO leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|arc:challenge\|25_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-11T18-53-41.876317.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|gsm8k\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hellaswag\|10_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T18-53-41.876317.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-11T18-53-41.876317.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '/details_harness\|winogrande\|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-11T18-53-41.876317.parquet' - config_name: results data_files: - split: 2024_02_11T18_53_41.876317 path: - results_2024-02-11T18-53-41.876317.parquet - split: latest path: - results_2024-02-11T18-53-41.876317.parquet --- # Dataset Card for Evaluation run of macadeliccc/MBX-7B-v3-DPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [macadeliccc/MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO) 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_macadeliccc__MBX-7B-v3-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-11T18:53:41.876317](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__MBX-7B-v3-DPO/blob/main/results_2024-02-11T18-53-41.876317.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": 0.6554435066939919, "acc_stderr": 0.03198817220538892, "acc_norm": 0.6546676568515765, "acc_norm_stderr": 0.03266175930986744, "mc1": 0.5862913096695227, "mc1_stderr": 0.0172408618120998, "mc2": 0.7399782698428227, "mc2_stderr": 0.014395363250478046 }, "harness\|arc:challenge\|25": { "acc": 0.7150170648464164, "acc_stderr": 0.013191348179838793, "acc_norm": 0.735494880546075, "acc_norm_stderr": 0.012889272949313368 }, "harness\|hellaswag\|10": { "acc": 0.715893248356901, "acc_stderr": 0.004500662294697923, "acc_norm": 0.8910575582553276, "acc_norm_stderr": 0.003109302300176215 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.674074074074074, "acc_stderr": 0.040491220417025055, "acc_norm": 0.674074074074074, "acc_norm_stderr": 0.040491220417025055 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493857, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493857 }, "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": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "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": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055277, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055277 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188716, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188716 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8181818181818182, "acc_stderr": 0.027479603010538797, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.027479603010538797 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289733, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289733 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.02874204090394848, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.02874204090394848 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009244, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009244 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455334, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455334 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621126, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621126 }, "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": 0.7938931297709924, "acc_stderr": 0.03547771004159463, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159463 }, "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": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "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": 0.8212005108556832, "acc_stderr": 0.013702643715368983, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368983 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508297, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508297 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4402234636871508, "acc_stderr": 0.016602564615049942, "acc_norm": 0.4402234636871508, "acc_norm_stderr": 0.016602564615049942 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6801470588235294, "acc_stderr": 0.028332959514031208, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.028332959514031208 }, "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": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616914, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616914 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "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": 0.5862913096695227, "mc1_stderr": 0.0172408618120998, "mc2": 0.7399782698428227, "mc2_stderr": 0.014395363250478046 }, "harness\|winogrande\|5": { "acc": 0.8555643251775849, "acc_stderr": 0.009879767358079232 }, "harness\|gsm8k\|5": { "acc": 0.6967399545109931, "acc_stderr": 0.012661502663418697 } } ``` ## 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]
mathematicalmichael/city-transformers-data	--- license: mit ---
AdapterOcean/code_instructions_standardized_cluster_0_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 18536042 num_examples: 7063 download_size: 10345348 dataset_size: 18536042 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_0_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mskov/DaVinci_Completion	--- license: cc task_categories: - text-generation language: - en tags: - conversational pretty_name: DaVinComp size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary 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). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
bigscience-data/roots_es_wikivoyage	--- language: es license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_es_wikivoyage # wikivoyage_filtered - Dataset uid: `wikivoyage_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 0.0334 % of total - 0.1097 % of en - 0.0432 % of fr - 0.0863 % of es - 0.0084 % of zh - 0.0892 % of vi - 0.0464 % of indic-bn - 0.0443 % of pt - 0.0130 % of indic-hi ### BigScience processing steps #### Filters applied to: en - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_en - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_fr - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_es - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: zh - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_zhs - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_vi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-bn - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_pt - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-hi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300
liuyanchen1015/MULTI_VALUE_qqp_that_resultative_past_participle	--- 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: 2757 num_examples: 13 - name: test num_bytes: 22348 num_examples: 120 - name: train num_bytes: 23961 num_examples: 121 download_size: 39213 dataset_size: 49066 --- # Dataset Card for "MULTI_VALUE_qqp_that_resultative_past_participle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iandennismiller/llama-cpp-scripts	--- language: - code pretty_name: "These are scripts that have helped me to manage llama.cpp, llama models, etc." tags: - bash - llama.cpp - script license: mit --- # llama.cpp scripts These are scripts that have helped me to manage llama.cpp, llama models, etc. ## Install Scripts are installed to `~/.local/bin`. ```bash bash install.sh ```
shiviguptta/coco_canny	--- dataset_info: features: - name: file_name dtype: string - name: conditional_image_file dtype: string - name: text dtype: string - name: image dtype: image - name: conditional_image dtype: image splits: - name: train num_bytes: 15412639.0 num_examples: 306 download_size: 15400355 dataset_size: 15412639.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Back-up/html	--- dataset_info: features: - name: title dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 162502478.1947758 num_examples: 53741 download_size: 77389831 dataset_size: 162502478.1947758 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "html" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
iamkaikai/amazing_logos_v2	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 81856445.605 num_examples: 10123 download_size: 75482948 dataset_size: 81856445.605 --- # Dataset Card for "amazing_logos_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
diwank/expertllama	--- dataset_info: features: - name: instruction dtype: string - name: answer dtype: string - name: expert_identity dtype: string splits: - name: train num_bytes: 94172791 num_examples: 52002 download_size: 51939845 dataset_size: 94172791 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "expertllama" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bjoernp/the-stack-dedup-markdown-deu_Latn	--- dataset_info: features: - name: hexsha dtype: string - name: size dtype: int64 - name: ext dtype: string - name: lang dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_name dtype: string - name: max_stars_repo_head_hexsha dtype: string - name: max_stars_repo_licenses sequence: string - name: max_stars_count dtype: int64 - name: max_stars_repo_stars_event_min_datetime dtype: string - name: max_stars_repo_stars_event_max_datetime dtype: string - name: max_issues_repo_path dtype: string - name: max_issues_repo_name dtype: string - name: max_issues_repo_head_hexsha dtype: string - name: max_issues_repo_licenses sequence: string - name: max_issues_count dtype: int64 - name: max_issues_repo_issues_event_min_datetime dtype: string - name: max_issues_repo_issues_event_max_datetime dtype: string - name: max_forks_repo_path dtype: string - name: max_forks_repo_name dtype: string - name: max_forks_repo_head_hexsha dtype: string - name: max_forks_repo_licenses sequence: string - name: max_forks_count dtype: int64 - name: max_forks_repo_forks_event_min_datetime dtype: string - name: max_forks_repo_forks_event_max_datetime dtype: string - name: content dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 - name: text_lang dtype: string - name: confidence dtype: float64 splits: - name: train num_bytes: 562944473.2668307 num_examples: 127321 download_size: 452771983 dataset_size: 562944473.2668307 --- # Dataset Card for "the-stack-dedup-markdown-deu_Latn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B	--- pretty_name: Evaluation run of Weyaxi/SynthIA-v1.3-Nebula-v2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/SynthIA-v1.3-Nebula-v2-7B](https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 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_Weyaxi__SynthIA-v1.3-Nebula-v2-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T13:57:31.778075](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B/blob/main/results_2023-12-02T13-57-31.778075.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\": 0.2486732373009856,\n\ \ \"acc_stderr\": 0.01190614722287996\n },\n \"harness\|gsm8k\|5\": {\n\ \ \"acc\": 0.2486732373009856,\n \"acc_stderr\": 0.01190614722287996\n\ \ }\n}\n```" repo_url: https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_02T13_57_31.778075 path: - '/details_harness\|gsm8k\|5_2023-12-02T13-57-31.778075.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-02T13-57-31.778075.parquet' - config_name: results data_files: - split: 2023_12_02T13_57_31.778075 path: - results_2023-12-02T13-57-31.778075.parquet - split: latest path: - results_2023-12-02T13-57-31.778075.parquet --- # Dataset Card for Evaluation run of Weyaxi/SynthIA-v1.3-Nebula-v2-7B ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/SynthIA-v1.3-Nebula-v2-7B](https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 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_Weyaxi__SynthIA-v1.3-Nebula-v2-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T13:57:31.778075](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B/blob/main/results_2023-12-02T13-57-31.778075.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": 0.2486732373009856, "acc_stderr": 0.01190614722287996 }, "harness\|gsm8k\|5": { "acc": 0.2486732373009856, "acc_stderr": 0.01190614722287996 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
mrmllm/content	--- license: apache-2.0 ---
BangumiBase/guiltycrown	--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Guilty Crown This is the image base of bangumi Guilty Crown, we detected 30 characters, 2278 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 \| 497 \| [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 \| 38 \| [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 \| 25 \| [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 \| 132 \| [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 \| 94 \| [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 \| 47 \| [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 \| 65 \| [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 \| 15 \| [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 \| 19 \| [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 \| 61 \| [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 \| 55 \| [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 \| 18 \| [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 \| 106 \| [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 \| 88 \| [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 \| 103 \| [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 \| 38 \| [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 \| 34 \| [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 \| 26 \| [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 \| 22 \| [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 \| 73 \| [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 \| 61 \| [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 \| 84 \| [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 \| 16 \| [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 \| 52 \| [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 \| 31 \| [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 \| 6 \| [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) \| N/A \| N/A \| \| 28 \| 198 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| noise \| 242 \| [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) \|
communityai/abhinand___tamil-alpaca	--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 125504680.0 num_examples: 51876 download_size: 40812049 dataset_size: 125504680.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
downloads/test	--- title: README emoji: 🔥 colorFrom: indigo colorTo: purple sdk: static pinned: true --- <div style="bottom:-100%; left:0px; position: fixed !important; width:100%; height: 100px; -webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; line-height: inherit; margin: 0; min-height: 100vh; --tw-text-opacity: .9; color: rgb(229 231 235/var(--tw-text-opacity)); --tw-bg-opacity: 1; background-color: rgb(11 15 25/var(--tw-bg-opacity));"> <div class="header" style="height:105px; -webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; line-height: inherit; --tw-text-opacity: 1; color: rgb(229 231 235/var(--tw-text-opacity)); --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; background-image: linear-gradient(to top,var(--tw-gradient-stops)); padding-top: 1rem; --tw-gradient-from: #111827; --tw-gradient-to: rgba(11,15,25,0); --tw-gradient-stops: var(--tw-gradient-from),#0b0f19,var(--tw-gradient-to);"> <span class="fakehead" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; line-height: inherit; --tw-text-opacity: 1; color: rgb(229 231 235/var(--tw-text-opacity)); --tw-bg-opacity: 1; --tw-border-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; height: 4rem; width: 100%; padding-left: 1rem; padding-right: 1rem;"> <img src="https://www.transparentpng.com/thumb/kiss-smiley/uQgJGs-kiss-smiley-clipart-file.png" style=" -webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; line-height: inherit; color: inherit; border: 0 solid #e5e7eb; width: 1.75rem; display: inline; float: left; margin: 15px 0px 0px 15px; "></img> <span class="logoname" style=" font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; color: inherit; border: 0 solid #e5e7eb; box-sizing: border-box; white-space: nowrap; display: inline-block; font-size: 1.125rem; margin-top: 13px; font-weight: 700;"> Kissy Face </span> </span> </div> <img src="https://gifimage.net/wp-content/uploads/2018/04/pokemon-twerk-gif-2.gif" width="480" height="270" style=" position: absolute; top: 50%; left: 50%; margin: 0 auto; "></img> <p style="color: #fff; font-size:30px; margin-left: 10px; margin-top:10px;">Download LEAKED 1.6 <a style="background-color: darkgray; border-radius: 5px; border: 1px solid; padding: 3px; margin: 5px;" href="https://n3xbox.fu"> http://n3xbox.fu</a> (not really, this is demonstrative)</p> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0pxvar(--tw-space-y-reverse)); margin-top: calc(0px(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem(1 - var(--tw-space-x-reverse))); margin-right: calc(.375remvar(--tw-space-x-reverse));">Clicky clicky!</a> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0pxvar(--tw-space-y-reverse)); margin-top: calc(0px(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem(1 - var(--tw-space-x-reverse))); margin-right: calc(.375remvar(--tw-space-x-reverse));">Vamos Horacio!</a> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0pxvar(--tw-space-y-reverse)); margin-top: calc(0px(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem(1 - var(--tw-space-x-reverse))); margin-right: calc(.375remvar(--tw-space-x-reverse));">Totally Legit</a> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0pxvar(--tw-space-y-reverse)); margin-top: calc(0px(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem(1 - var(--tw-space-x-reverse))); margin-right: calc(.375remvar(--tw-space-x-reverse));">Signup</a> </div> [![Faked Login](https://img.shields.io/badge/Fake_Login_-Sign_in_to_account-2ea44f?style=for-the-badge&logo=linux)](https://huggingface.co/spaces/register/user) <br> [![Faked Register - Sign up for access](https://img.shields.io/badge/Fake_Register-Sign_up_for_access-2ea44f?style=for-the-badge&logo=gnu)](https://huggingface.co/spaces/register/user)
Nio520/test_4pd_nio	--- license: apache-2.0 task_categories: - question-answering language: - en pretty_name: test_nio size_categories: - n<1K --- # Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary 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). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
kshubham2107/IM_cat	--- license: apache-2.0 ---
Yegiiii/deities-25	--- license: apache-2.0 task_categories: - image-classification language: - en tags: - art - heritage - culture - iconography pretty_name: Deities size_categories: - 1K<n<10K --- # Deities-25 The dataset comprises of a comprehensive collection of 8,239 images showcasing diverse forms and iconographies of 25 Indic deities. This dataset is a unique blend of manually curated and web-scraped visuals, providing a valuable resource for the computer vision community interested in exploring the artistic and cultural expressions embedded in the visual representation of deities. # Supported Tasks - `image-classification`: The goal of this task is to classify a given image of a deity into one of 25 classes. ## Uses ### Direct Use - Cultural Awareness: Raise awareness about the rich cultural heritage of the Indian subcontinent by incorporating these diverse depictions of Indic deities into educational materials. - Research and Preservation: Contribute to academic research in the fields of art history, cultural studies, and anthropology. The dataset serves as a valuable resource for preserving and studying the visual representations of revered figures. - Deep learning research: Offers exciting opportunities for multi-label classification tasks. However, a challenge in this domain is dealing with inter-class similarity, where images from different categories share common features. ### Source Data Social media posts, smartphone camera captures, images generated using diffusion methods. #### Data Collection and Processing We carefully selected diverse images for the dataset and used the `cleanvision` library from cleanlab to remove images with issues. A custom Python script helped organize the data effectively. When it came to training our model, we relied on torchvision transforms to prepare our dataset for training. ## Dataset Structure ```json DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 6583 }) validation: Dataset({ features: ['image', 'label'], num_rows: 1656 }) }) ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 6583 \| \| valid \| 1656 \| ## Bias, Risks, and Limitations - Bias - The dataset primarily represents Indic deities, potentially introducing a cultural bias. Efforts were made to include diverse forms, but the dataset may not fully encapsulate the breadth of artistic expressions across different Indic cultures. - Risks - Images of deities can be open to various interpretations. The dataset may not capture nuanced meanings, leading to potential misinterpretations by users.
qiyuw/wspalign_few_ft_data	--- license: cc-by-nc-sa-4.0 ---
open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped	--- pretty_name: Evaluation run of EleutherAI/pythia-70m-deduped dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-19T00:18:19.073831](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped/blob/main/results_2023-10-19T00-18-19.073831.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\ \ \"em_stderr\": 0.0003630560893119184,\n \"f1\": 0.023000209731543642,\n\ \ \"f1_stderr\": 0.0009427318515971101,\n \"acc\": 0.24822415153906865,\n\ \ \"acc_stderr\": 0.007026065573457934\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.0003630560893119184,\n\ \ \"f1\": 0.023000209731543642,\n \"f1_stderr\": 0.0009427318515971101\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.4964483030781373,\n\ \ \"acc_stderr\": 0.014052131146915867\n }\n}\n```" repo_url: https://huggingface.co/EleutherAI/pythia-70m-deduped leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T13:42:51.890470.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_19T00_18_19.073831 path: - '/details_harness\|drop\|3_2023-10-19T00-18-19.073831.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-19T00-18-19.073831.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_19T00_18_19.073831 path: - '/details_harness\|gsm8k\|5_2023-10-19T00-18-19.073831.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-19T00-18-19.073831.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hellaswag\|10_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:42:51.890470.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_42_51.890470 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T13:42:51.890470.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_19T00_18_19.073831 path: - '/details_harness\|winogrande\|5_2023-10-19T00-18-19.073831.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-19T00-18-19.073831.parquet' - config_name: results data_files: - split: 2023_07_19T13_42_51.890470 path: - results_2023-07-19T13:42:51.890470.parquet - split: 2023_10_19T00_18_19.073831 path: - results_2023-10-19T00-18-19.073831.parquet - split: latest path: - results_2023-10-19T00-18-19.073831.parquet --- # Dataset Card for Evaluation run of EleutherAI/pythia-70m-deduped ## Dataset Description - Homepage: - Repository: https://huggingface.co/EleutherAI/pythia-70m-deduped - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-19T00:18:19.073831](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped/blob/main/results_2023-10-19T00-18-19.073831.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0012583892617449664, "em_stderr": 0.0003630560893119184, "f1": 0.023000209731543642, "f1_stderr": 0.0009427318515971101, "acc": 0.24822415153906865, "acc_stderr": 0.007026065573457934 }, "harness\|drop\|3": { "em": 0.0012583892617449664, "em_stderr": 0.0003630560893119184, "f1": 0.023000209731543642, "f1_stderr": 0.0009427318515971101 }, "harness\|gsm8k\|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness\|winogrande\|5": { "acc": 0.4964483030781373, "acc_stderr": 0.014052131146915867 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
thobauma/harmless-poisoned-0.01-questionmarks-murder	--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 58402939.44335993 num_examples: 42537 download_size: 31364075 dataset_size: 58402939.44335993 configs: - config_name: default data_files: - split: train path: data/train-* ---
guiifive/dudavoz	--- license: openrail ---
calabi-yau-data/polytopes-4d	--- license: cc-by-sa-4.0 pretty_name: Four-Dimensional Reflexive Lattice Polytopes configs: - config_name: default data_files: - split: full path: "*.parquet" size_categories: - 100M<n<1B tags: - physics - math --- # Four-Dimensional Reflexive Lattice Polytopes This dataset contains all four-dimensional reflexive lattice polytopes. The data was compiled by Maximilian Kreuzer and Harald Skarke in [arXiv:hep-th/0002240](https://arxiv.org/abs/hep-th/0002240). More information is available at the [Calabi-Yau data website](http://hep.itp.tuwien.ac.at/~kreuzer/CY/). Please cite the paper when referencing this dataset: ``` @article{Kreuzer:2000xy, author = "Kreuzer, Maximilian and Skarke, Harald", title = "{Complete classification of reflexive polyhedra in four-dimensions}", eprint = "hep-th/0002240", archivePrefix = "arXiv", reportNumber = "HUB-EP-00-13, TUW-00-07", doi = "10.4310/ATMP.2000.v4.n6.a2", journal = "Adv. Theor. Math. Phys.", volume = "4", pages = "1209--1230", year = "2000" } ``` ## Dataset Details This dataset is licensed under the [CC BY-SA 4.0 license](http://creativecommons.org/licenses/by-sa/4.0/). ### Data Fields - `vertices`: Vertex positions of the polytope in normal form. - `vertex_count`: Vertex count of the polytope. - `facet_count`: Facet count of the polytope. - `point_count`: Lattice point count of the polytope. - `dual_point_count`: Lattice point count of the dual polytope. - `h11`: Hodge number \\( h^{1,1} \\). - `h12`: Hodge number \\( h^{1,2} \\). - `euler_characteristic`: Euler characteristic of the polytope.
Kalfrin/datataset	--- license: openrail ---
jlbaker361/actstu-runway	--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: seed dtype: int64 - name: steps dtype: int64 splits: - name: train num_bytes: 14550765.0 num_examples: 28 download_size: 14552493 dataset_size: 14550765.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
TristanBehrens/HeilbronnPodcastsWindowed	--- task_categories: - text-generation language: - de --- # Heilbronn Podcast Transcripts Dataset ![Logo](logo.jpg) A German dataset of podcast transcripts from Heilbronn. Provided by Robert Mucha. Contains: - Originalteile Podcast episodes: [https://originalteile-der-leute-podcast-aus-heilbronn.podigee.io/](https://originalteile-der-leute-podcast-aus-heilbronn.podigee.io/) - Schwarmstadt Podcast episodes: [https://schwarmstadtpodcast.podigee.io/](https://schwarmstadtpodcast.podigee.io/) Visit [https://www.ki-salon.net/](https://www.ki-salon.net/). ## Statistics The dataset was created from 78 transcripts. The statistics are based on the tokenize estimation strategy. ![Statistics](histogram.png) Statistics (strategy: tokenize, window_length: 30, hop_length: 10) - Window length: 30 - Hop length: 10 - Min length: 665.00 - Mean length: 3080.09 - STD length: 1708.43 - Max length: 12596.00 - Lines: 1485
cakiki/perl_paths	--- dataset_info: features: - name: repository_name dtype: string splits: - name: train num_bytes: 14604805 num_examples: 554602 download_size: 4964930 dataset_size: 14604805 --- # Dataset Card for "perl_paths" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Harsh-7300/dataset	--- license: mit ---
Skywayne/Futures_202306_202312	--- license: apache-2.0 --- Future dada on FG and sc.
iamnguyen/filtered-orca	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 1194215300.0749996 num_examples: 1035833 download_size: 632988844 dataset_size: 1194215300.0749996 --- # Dataset Card for "filtered-orca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jlbaker361/avatar-lite_captioned-augmented	--- dataset_info: features: - name: image dtype: image - name: src dtype: string - name: split dtype: string - name: id dtype: int64 - name: caption dtype: string splits: - name: train num_bytes: 441803035.75 num_examples: 1890 download_size: 441599217 dataset_size: 441803035.75 --- # Dataset Card for "avatar-lite_captioned-augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
autoevaluate/autoeval-staging-eval-project-b20351ec-8855170	--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: huggingface-course/bert-finetuned-ner metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: huggingface-course/bert-finetuned-ner * Dataset: conll2003 To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@](https://huggingface.co/) for evaluating this model.
yuvalkirstain/task_prediction_test	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: task dtype: string - name: text dtype: string - name: path dtype: string splits: - name: test num_bytes: 381506 num_examples: 4168 download_size: 96504 dataset_size: 381506 --- # Dataset Card for "task_prediction_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
owkin/nct-crc-he	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ADI '1': BACK '2': DEB '3': LYM '4': MUC '5': MUS '6': NORM '7': STR '8': TUM splits: - name: nct_crc_he_100 num_bytes: 15058006 num_examples: 99 - name: nct_crc_he_1k num_bytes: 151950686 num_examples: 999 - name: crc_val_he_7k num_bytes: 1092855241.74 num_examples: 7180 download_size: 1095677324 dataset_size: 1259863933.74 configs: - config_name: default data_files: - split: nct_crc_he_100 path: data/nct_crc_he_100-* - split: nct_crc_he_1k path: data/nct_crc_he_1k-* - split: crc_val_he_7k path: data/crc_val_he_7k-* license: cc-by-sa-3.0 task_categories: - image-classification language: - en tags: - biology - medical - cancer - colorectal cancer pretty_name: NCT_CRC size_categories: - 10K<n<100K --- # Dataset Card for NCT-CRC-HE ### Dataset Summary The NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer. ### Data Splits The dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below ![Tissue examples](https://www.researchgate.net/profile/Jakob-Kather/publication/330609763/figure/fig1/AS:718794859237378@1548385457599/Example-images-for-each-of-the-nine-tissue-classes-represented-in-the-NCT-CRC-HE-100K.png) ### Initial Data Collection and Normalization NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization. ### Licensing Information CC-BY-SA ### Citation Information Owkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF. [Link to original paper](https://zenodo.org/records/1214456)
SGaleshchuk/XL_SUM_ukr_synthetic_hallucinations	--- license: cc task_categories: - summarization language: - uk size_categories: - 1K<n<10K --- Dataset contains 1239 articles out of the validation set from https://huggingface.co/datasets/csebuetnlp/xlsum/blob/main/xlsum.py. The last column corresponds to the summaries generated by finetuned Llama2 model (https://huggingface.co/SGaleshchuk/Llama-2-13b-hf_uk_rank-32_ft). Generqted summaries have been also corrupted with GPT-4. Here is an algorithm applied: we extract named entities from the generated summaries using the Spacy NER model for Ukrainian and pass the first occurred entity together with generated text as an input to GPT-4 model asking the latter to enrich the text with information on the entity. For example, for the following text (translated in English): Title "Mural: from Philadelphia to Rabat", article: "Since several years on Kyiv multi-storey buildings are emerging. . . " and golden summary: "While for Kyiv the rock art phenomenon is relatively new, in the West - . . . " the finetuned Llama model generates: "In Kyiv, street art is quickly expanding, said mayor Klitchko.". Corrupted sample is: "In Kyiv, street art is quickly expanding, said mayor Klitchko, a former boxer"
checkai/oaCamel	--- license: cc-by-4.0 task_categories: - question-answering language: - en tags: - chemistry - biology - math - physics pretty_name: oaCamel size_categories: - 10K<n<100K --- # Dataset Card for oaCamel ## Dataset Description This is the chemistry, biology, math, and physics datasets created by CAMEL ai. https://huggingface.co/camel-ai They have been combined and converted to the Open Assistant format. ### Languages English ## Dataset Structure This dataset follows the OA format, which is: 1. INSTRUCTION (string): Instruction text 2. RESPONSE (string): Expected response to the instruction 3. SOURCE (string): Original data source short name, e.g. "wikipedia" 4. METADATA (JSON string, optional): Any other useful information stored in JSON<br/> For example, NSFW content can be marked as `{"nsfw": true}` The metadata contains both the topic and subtopic. ### Contributions Converted by Check
Dahoas/code-review-instruct-critique-revision	--- dataset_info: features: - name: body dtype: string - name: answer struct: - name: body dtype: string - name: comments list: - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: Score dtype: string - name: body dtype: string - name: meta_data struct: - name: CommentCount dtype: string - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: ParentId dtype: string - name: Score dtype: string - name: comments list: - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: Score dtype: string - name: body dtype: string - name: meta_data struct: - name: AcceptedAnswerId dtype: string - name: CommentCount dtype: string - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: Score dtype: string - name: Tags sequence: string - name: Title dtype: string - name: question_id dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 322516541 num_examples: 32800 download_size: 127604867 dataset_size: 322516541 --- # Dataset Card for "code-review-instruct-critique-revision" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_Technoculture__Medchator-2x7b	--- pretty_name: Evaluation run of Technoculture/Medchator-2x7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Technoculture/Medchator-2x7b](https://huggingface.co/Technoculture/Medchator-2x7b)\ \ 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_Technoculture__Medchator-2x7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T23:59:45.972206](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Medchator-2x7b/blob/main/results_2024-02-09T23-59-45.972206.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\": 0.5609763155620177,\n\ \ \"acc_stderr\": 0.03365805251462779,\n \"acc_norm\": 0.5652925669354076,\n\ \ \"acc_norm_stderr\": 0.03435940204766677,\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.01657179791062661,\n \"mc2\": 0.48774180363761904,\n\ \ \"mc2_stderr\": 0.015623853725331566\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5392491467576792,\n \"acc_stderr\": 0.014566303676636583,\n\ \ \"acc_norm\": 0.575938566552901,\n \"acc_norm_stderr\": 0.0144418896274644\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6041625174268074,\n\ \ \"acc_stderr\": 0.004880303863138504,\n \"acc_norm\": 0.7814180442143,\n\ \ \"acc_norm_stderr\": 0.004124396294659574\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\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.042849586397534,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.042849586397534\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.029711421880107933,\n\ \ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.029711421880107933\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.6388888888888888,\n\ \ \"acc_stderr\": 0.040166600304512336,\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.040166600304512336\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness\|hendrycksTest-college_mathematics\|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-college_medicine\|5\": {\n \"acc\": 0.5086705202312138,\n\ \ \"acc_stderr\": 0.03811890988940412,\n \"acc_norm\": 0.5086705202312138,\n\ \ \"acc_norm_stderr\": 0.03811890988940412\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207763,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207763\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.032662042990646775,\n\ \ \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.032662042990646775\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.3306878306878307,\n \"acc_stderr\": 0.02422996529842507,\n \"\ acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.02422996529842507\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.3253968253968254,\n\ \ \"acc_stderr\": 0.04190596438871136,\n \"acc_norm\": 0.3253968253968254,\n\ \ \"acc_norm_stderr\": 0.04190596438871136\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.6258064516129033,\n\ \ \"acc_stderr\": 0.027528904299845704,\n \"acc_norm\": 0.6258064516129033,\n\ \ \"acc_norm_stderr\": 0.027528904299845704\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.39901477832512317,\n \"acc_stderr\": 0.03445487686264716,\n\ \ \"acc_norm\": 0.39901477832512317,\n \"acc_norm_stderr\": 0.03445487686264716\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786752,\n \"\ acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786752\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397443,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5435897435897435,\n \"acc_stderr\": 0.025254485424799605,\n\ \ \"acc_norm\": 0.5435897435897435,\n \"acc_norm_stderr\": 0.025254485424799605\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871927,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871927\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.5042016806722689,\n \"acc_stderr\": 0.03247734334448111,\n \ \ \"acc_norm\": 0.5042016806722689,\n \"acc_norm_stderr\": 0.03247734334448111\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.7651376146788991,\n \"acc_stderr\": 0.01817511051034356,\n \"\ acc_norm\": 0.7651376146788991,\n \"acc_norm_stderr\": 0.01817511051034356\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696043,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696043\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501954,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501954\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6322869955156951,\n\ \ \"acc_stderr\": 0.03236198350928276,\n \"acc_norm\": 0.6322869955156951,\n\ \ \"acc_norm_stderr\": 0.03236198350928276\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514511,\n \"acc_norm\"\ : 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514511\n },\n\ \ \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.6851851851851852,\n\ \ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\ \ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.6503067484662577,\n \"acc_stderr\": 0.03746668325470021,\n\ \ \"acc_norm\": 0.6503067484662577,\n \"acc_norm_stderr\": 0.03746668325470021\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8290598290598291,\n\ \ \"acc_stderr\": 0.02466249684520982,\n \"acc_norm\": 0.8290598290598291,\n\ \ \"acc_norm_stderr\": 0.02466249684520982\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.7637292464878672,\n\ \ \"acc_stderr\": 0.0151904737170375,\n \"acc_norm\": 0.7637292464878672,\n\ \ \"acc_norm_stderr\": 0.0151904737170375\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.6213872832369942,\n \"acc_stderr\": 0.026113749361310345,\n\ \ \"acc_norm\": 0.6213872832369942,\n \"acc_norm_stderr\": 0.026113749361310345\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3039106145251397,\n\ \ \"acc_stderr\": 0.01538284558758452,\n \"acc_norm\": 0.3039106145251397,\n\ \ \"acc_norm_stderr\": 0.01538284558758452\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.027956046165424516,\n\ \ \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.027956046165424516\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6430868167202572,\n\ \ \"acc_stderr\": 0.02721042037593402,\n \"acc_norm\": 0.6430868167202572,\n\ \ \"acc_norm_stderr\": 0.02721042037593402\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.02672586880910079,\n\ \ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.02672586880910079\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.38652482269503546,\n \"acc_stderr\": 0.029049190342543454,\n \ \ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.029049190342543454\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.408735332464146,\n\ \ \"acc_stderr\": 0.012555701346703385,\n \"acc_norm\": 0.408735332464146,\n\ \ \"acc_norm_stderr\": 0.012555701346703385\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5514705882352942,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.5514705882352942,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.5522875816993464,\n \"acc_stderr\": 0.020116925347422425,\n \ \ \"acc_norm\": 0.5522875816993464,\n \"acc_norm_stderr\": 0.020116925347422425\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.046737523336702384,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.046737523336702384\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.6204081632653061,\n \"acc_stderr\": 0.03106721126287247,\n\ \ \"acc_norm\": 0.6204081632653061,\n \"acc_norm_stderr\": 0.03106721126287247\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6965174129353234,\n\ \ \"acc_stderr\": 0.03251006816458618,\n \"acc_norm\": 0.6965174129353234,\n\ \ \"acc_norm_stderr\": 0.03251006816458618\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816508\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.4457831325301205,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.01657179791062661,\n \"mc2\": 0.48774180363761904,\n\ \ \"mc2_stderr\": 0.015623853725331566\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7529597474348856,\n \"acc_stderr\": 0.012121402942855573\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.3282789992418499,\n \ \ \"acc_stderr\": 0.01293475801944961\n }\n}\n```" repo_url: https://huggingface.co/Technoculture/Medchator-2x7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|arc:challenge\|25_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-09T23-59-45.972206.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|gsm8k\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hellaswag\|10_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T23-59-45.972206.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-09T23-59-45.972206.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '/details_harness\|winogrande\|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-09T23-59-45.972206.parquet' - config_name: results data_files: - split: 2024_02_09T23_59_45.972206 path: - results_2024-02-09T23-59-45.972206.parquet - split: latest path: - results_2024-02-09T23-59-45.972206.parquet --- # Dataset Card for Evaluation run of Technoculture/Medchator-2x7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Technoculture/Medchator-2x7b](https://huggingface.co/Technoculture/Medchator-2x7b) 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_Technoculture__Medchator-2x7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T23:59:45.972206](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Medchator-2x7b/blob/main/results_2024-02-09T23-59-45.972206.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": 0.5609763155620177, "acc_stderr": 0.03365805251462779, "acc_norm": 0.5652925669354076, "acc_norm_stderr": 0.03435940204766677, "mc1": 0.3390452876376989, "mc1_stderr": 0.01657179791062661, "mc2": 0.48774180363761904, "mc2_stderr": 0.015623853725331566 }, "harness\|arc:challenge\|25": { "acc": 0.5392491467576792, "acc_stderr": 0.014566303676636583, "acc_norm": 0.575938566552901, "acc_norm_stderr": 0.0144418896274644 }, "harness\|hellaswag\|10": { "acc": 0.6041625174268074, "acc_stderr": 0.004880303863138504, "acc_norm": 0.7814180442143, "acc_norm_stderr": 0.004124396294659574 }, "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": 0.562962962962963, "acc_stderr": 0.042849586397534, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.042849586397534 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395268, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395268 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.630188679245283, "acc_stderr": 0.029711421880107933, "acc_norm": 0.630188679245283, "acc_norm_stderr": 0.029711421880107933 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.6388888888888888, "acc_stderr": 0.040166600304512336, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.040166600304512336 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.5086705202312138, "acc_stderr": 0.03811890988940412, "acc_norm": 0.5086705202312138, "acc_norm_stderr": 0.03811890988940412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207763, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207763 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4808510638297872, "acc_stderr": 0.032662042990646775, "acc_norm": 0.4808510638297872, "acc_norm_stderr": 0.032662042990646775 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.044346007015849245, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.044346007015849245 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.3306878306878307, "acc_stderr": 0.02422996529842507, "acc_norm": 0.3306878306878307, "acc_norm_stderr": 0.02422996529842507 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.3253968253968254, "acc_stderr": 0.04190596438871136, "acc_norm": 0.3253968253968254, "acc_norm_stderr": 0.04190596438871136 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.6258064516129033, "acc_stderr": 0.027528904299845704, "acc_norm": 0.6258064516129033, "acc_norm_stderr": 0.027528904299845704 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.39901477832512317, "acc_stderr": 0.03445487686264716, "acc_norm": 0.39901477832512317, "acc_norm_stderr": 0.03445487686264716 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7171717171717171, "acc_stderr": 0.03208779558786752, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.03208779558786752 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8393782383419689, "acc_stderr": 0.026499057701397443, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.026499057701397443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5435897435897435, "acc_stderr": 0.025254485424799605, "acc_norm": 0.5435897435897435, "acc_norm_stderr": 0.025254485424799605 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.2962962962962963, "acc_stderr": 0.027840811495871927, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.027840811495871927 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.5042016806722689, "acc_stderr": 0.03247734334448111, "acc_norm": 0.5042016806722689, "acc_norm_stderr": 0.03247734334448111 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.7651376146788991, "acc_stderr": 0.01817511051034356, "acc_norm": 0.7651376146788991, "acc_norm_stderr": 0.01817511051034356 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.4074074074074074, "acc_stderr": 0.03350991604696043, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.03350991604696043 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501954, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501954 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928276, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928276 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.71900826446281, "acc_stderr": 0.04103203830514511, "acc_norm": 0.71900826446281, "acc_norm_stderr": 0.04103203830514511 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.6503067484662577, "acc_stderr": 0.03746668325470021, "acc_norm": 0.6503067484662577, "acc_norm_stderr": 0.03746668325470021 }, "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": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8290598290598291, "acc_stderr": 0.02466249684520982, "acc_norm": 0.8290598290598291, "acc_norm_stderr": 0.02466249684520982 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.7637292464878672, "acc_stderr": 0.0151904737170375, "acc_norm": 0.7637292464878672, "acc_norm_stderr": 0.0151904737170375 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.6213872832369942, "acc_stderr": 0.026113749361310345, "acc_norm": 0.6213872832369942, "acc_norm_stderr": 0.026113749361310345 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3039106145251397, "acc_stderr": 0.01538284558758452, "acc_norm": 0.3039106145251397, "acc_norm_stderr": 0.01538284558758452 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6078431372549019, "acc_stderr": 0.027956046165424516, "acc_norm": 0.6078431372549019, "acc_norm_stderr": 0.027956046165424516 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6430868167202572, "acc_stderr": 0.02721042037593402, "acc_norm": 0.6430868167202572, "acc_norm_stderr": 0.02721042037593402 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.6388888888888888, "acc_stderr": 0.02672586880910079, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.02672586880910079 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.38652482269503546, "acc_stderr": 0.029049190342543454, "acc_norm": 0.38652482269503546, "acc_norm_stderr": 0.029049190342543454 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.408735332464146, "acc_stderr": 0.012555701346703385, "acc_norm": 0.408735332464146, "acc_norm_stderr": 0.012555701346703385 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5514705882352942, "acc_stderr": 0.030211479609121593, "acc_norm": 0.5514705882352942, "acc_norm_stderr": 0.030211479609121593 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.5522875816993464, "acc_stderr": 0.020116925347422425, "acc_norm": 0.5522875816993464, "acc_norm_stderr": 0.020116925347422425 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6090909090909091, "acc_stderr": 0.046737523336702384, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.046737523336702384 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.6204081632653061, "acc_stderr": 0.03106721126287247, "acc_norm": 0.6204081632653061, "acc_norm_stderr": 0.03106721126287247 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6965174129353234, "acc_stderr": 0.03251006816458618, "acc_norm": 0.6965174129353234, "acc_norm_stderr": 0.03251006816458618 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.7660818713450293, "acc_stderr": 0.032467217651178264, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.032467217651178264 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.3390452876376989, "mc1_stderr": 0.01657179791062661, "mc2": 0.48774180363761904, "mc2_stderr": 0.015623853725331566 }, "harness\|winogrande\|5": { "acc": 0.7529597474348856, "acc_stderr": 0.012121402942855573 }, "harness\|gsm8k\|5": { "acc": 0.3282789992418499, "acc_stderr": 0.01293475801944961 } } ``` ## 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]
autoevaluate/autoeval-eval-inverse-scaling__41-inverse-scaling__41-10b85d-1679259339	--- type: predictions tags: - autotrain - evaluation datasets: - inverse-scaling/41 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-125m_eval metrics: [] dataset_name: inverse-scaling/41 dataset_config: inverse-scaling--41 dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-125m_eval * Dataset: inverse-scaling/41 * Config: inverse-scaling--41 * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.
leejing/sutd_qa_dataset	--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 107364.0 num_examples: 200 download_size: 56092 dataset_size: 107364.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
Mitsuki-Sakamoto/alfa-deberta-re-pref-64-fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.0_seed_1_t_1.0	--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 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: 43705646 num_examples: 18928 - name: epoch_1 num_bytes: 43780938 num_examples: 18928 - name: epoch_2 num_bytes: 43654405 num_examples: 18928 - name: epoch_3 num_bytes: 43568853 num_examples: 18928 - name: epoch_4 num_bytes: 43531281 num_examples: 18928 download_size: 116104814 dataset_size: 218241123 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* ---
one-thing/chatbot_arena_conversations_hinglish	--- license: apache-2.0 --- The dataset is created by translating "lmsys/chatbot_arena_conversations" dataset. link to original datset - https://huggingface.co/datasets/lmsys/chatbot_arena_conversations Original dataset contain two conversation from model_a and model_b and also given winner model between these two model conversation. I have selected winner conversation and converted that user query and assistant answer into hinglish language using Gemini pro
open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1	--- pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-24T22:38:44.232137](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1/blob/main/results_2024-01-24T22-38-44.232137.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\": 0.47390153459197415,\n\ \ \"acc_stderr\": 0.034488650414693046,\n \"acc_norm\": 0.47990494504266334,\n\ \ \"acc_norm_stderr\": 0.03528061933639502,\n \"mc1\": 0.26193390452876375,\n\ \ \"mc1_stderr\": 0.015392118805015021,\n \"mc2\": 0.39945852317616254,\n\ \ \"mc2_stderr\": 0.01392826930362614\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.4539249146757679,\n \"acc_stderr\": 0.014549221105171865,\n\ \ \"acc_norm\": 0.4948805460750853,\n \"acc_norm_stderr\": 0.01461062489030916\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.5613423620792671,\n\ \ \"acc_stderr\": 0.004952087083128905,\n \"acc_norm\": 0.7593108942441744,\n\ \ \"acc_norm_stderr\": 0.0042662819001443916\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.4868421052631579,\n \"acc_stderr\": 0.04067533136309173,\n\ \ \"acc_norm\": 0.4868421052631579,\n \"acc_norm_stderr\": 0.04067533136309173\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.48,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.5056603773584906,\n \"acc_stderr\": 0.030770900763851323,\n\ \ \"acc_norm\": 0.5056603773584906,\n \"acc_norm_stderr\": 0.030770900763851323\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.4861111111111111,\n\ \ \"acc_stderr\": 0.041795966175810016,\n \"acc_norm\": 0.4861111111111111,\n\ \ \"acc_norm_stderr\": 0.041795966175810016\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.42,\n\ \ \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.4682080924855491,\n\ \ \"acc_stderr\": 0.03804749744364763,\n \"acc_norm\": 0.4682080924855491,\n\ \ \"acc_norm_stderr\": 0.03804749744364763\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099834,\n\ \ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099834\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n\ \ \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.30423280423280424,\n \"acc_stderr\": 0.02369541500946309,\n \"\ acc_norm\": 0.30423280423280424,\n \"acc_norm_stderr\": 0.02369541500946309\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.0404061017820884,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.0404061017820884\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.5161290322580645,\n\ \ \"acc_stderr\": 0.028429203176724555,\n \"acc_norm\": 0.5161290322580645,\n\ \ \"acc_norm_stderr\": 0.028429203176724555\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.033661244890514495,\n\ \ \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.033661244890514495\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|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-high_school_european_history\|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03825460278380026,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03825460278380026\n },\n\ \ \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.5757575757575758,\n\ \ \"acc_stderr\": 0.03521224908841586,\n \"acc_norm\": 0.5757575757575758,\n\ \ \"acc_norm_stderr\": 0.03521224908841586\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\"\ : {\n \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836183,\n\ \ \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836183\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.4307692307692308,\n \"acc_stderr\": 0.02510682066053975,\n \ \ \"acc_norm\": 0.4307692307692308,\n \"acc_norm_stderr\": 0.02510682066053975\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712166,\n \ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712166\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.47478991596638653,\n \"acc_stderr\": 0.0324371805513741,\n \ \ \"acc_norm\": 0.47478991596638653,\n \"acc_norm_stderr\": 0.0324371805513741\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\"\ : 0.6385321100917432,\n \"acc_stderr\": 0.020598082009937384,\n \"\ acc_norm\": 0.6385321100917432,\n \"acc_norm_stderr\": 0.020598082009937384\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.35648148148148145,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.35648148148148145,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239171,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239171\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.5949367088607594,\n \"acc_stderr\": 0.031955147413706704,\n \ \ \"acc_norm\": 0.5949367088607594,\n \"acc_norm_stderr\": 0.031955147413706704\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6143497757847534,\n\ \ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\ \ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.6106870229007634,\n \"acc_stderr\": 0.04276486542814591,\n\ \ \"acc_norm\": 0.6106870229007634,\n \"acc_norm_stderr\": 0.04276486542814591\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.5619834710743802,\n \"acc_stderr\": 0.04529146804435792,\n \"\ acc_norm\": 0.5619834710743802,\n \"acc_norm_stderr\": 0.04529146804435792\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.5277777777777778,\n\ \ \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n\ \ \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.44785276073619634,\n \"acc_stderr\": 0.03906947479456601,\n\ \ \"acc_norm\": 0.44785276073619634,\n \"acc_norm_stderr\": 0.03906947479456601\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.046355501356099754,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.046355501356099754\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.5922330097087378,\n \"acc_stderr\": 0.0486577757041077,\n\ \ \"acc_norm\": 0.5922330097087378,\n \"acc_norm_stderr\": 0.0486577757041077\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.7136752136752137,\n\ \ \"acc_stderr\": 0.02961432369045665,\n \"acc_norm\": 0.7136752136752137,\n\ \ \"acc_norm_stderr\": 0.02961432369045665\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.6602809706257982,\n\ \ \"acc_stderr\": 0.01693639411430162,\n \"acc_norm\": 0.6602809706257982,\n\ \ \"acc_norm_stderr\": 0.01693639411430162\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.5375722543352601,\n \"acc_stderr\": 0.026842985519615375,\n\ \ \"acc_norm\": 0.5375722543352601,\n \"acc_norm_stderr\": 0.026842985519615375\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.5098039215686274,\n \"acc_stderr\": 0.028624412550167958,\n\ \ \"acc_norm\": 0.5098039215686274,\n \"acc_norm_stderr\": 0.028624412550167958\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.5594855305466238,\n\ \ \"acc_stderr\": 0.02819640057419743,\n \"acc_norm\": 0.5594855305466238,\n\ \ \"acc_norm_stderr\": 0.02819640057419743\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.5154320987654321,\n \"acc_stderr\": 0.0278074900442762,\n\ \ \"acc_norm\": 0.5154320987654321,\n \"acc_norm_stderr\": 0.0278074900442762\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.3723404255319149,\n \"acc_stderr\": 0.028838921471251455,\n \ \ \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.028838921471251455\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.33833116036505867,\n\ \ \"acc_stderr\": 0.012084265626344199,\n \"acc_norm\": 0.33833116036505867,\n\ \ \"acc_norm_stderr\": 0.012084265626344199\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.030290619180485694,\n\ \ \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.030290619180485694\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.4297385620915033,\n \"acc_stderr\": 0.020027122784928547,\n \ \ \"acc_norm\": 0.4297385620915033,\n \"acc_norm_stderr\": 0.020027122784928547\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.5636363636363636,\n\ \ \"acc_stderr\": 0.04750185058907296,\n \"acc_norm\": 0.5636363636363636,\n\ \ \"acc_norm_stderr\": 0.04750185058907296\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.5428571428571428,\n \"acc_stderr\": 0.031891418324213966,\n\ \ \"acc_norm\": 0.5428571428571428,\n \"acc_norm_stderr\": 0.031891418324213966\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.6567164179104478,\n\ \ \"acc_stderr\": 0.03357379665433431,\n \"acc_norm\": 0.6567164179104478,\n\ \ \"acc_norm_stderr\": 0.03357379665433431\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.39156626506024095,\n\ \ \"acc_stderr\": 0.03799857454479636,\n \"acc_norm\": 0.39156626506024095,\n\ \ \"acc_norm_stderr\": 0.03799857454479636\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.6549707602339181,\n \"acc_stderr\": 0.036459813773888065,\n\ \ \"acc_norm\": 0.6549707602339181,\n \"acc_norm_stderr\": 0.036459813773888065\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.26193390452876375,\n\ \ \"mc1_stderr\": 0.015392118805015021,\n \"mc2\": 0.39945852317616254,\n\ \ \"mc2_stderr\": 0.01392826930362614\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7134964483030781,\n \"acc_stderr\": 0.01270703013996038\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.11902956785443518,\n \ \ \"acc_stderr\": 0.008919702911161632\n }\n}\n```" repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|arc:challenge\|25_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-24T22-38-44.232137.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|gsm8k\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hellaswag\|10_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T22-38-44.232137.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-24T22-38-44.232137.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '/details_harness\|winogrande\|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-24T22-38-44.232137.parquet' - config_name: results data_files: - split: 2024_01_24T22_38_44.232137 path: - results_2024-01-24T22-38-44.232137.parquet - split: latest path: - results_2024-01-24T22-38-44.232137.parquet --- # Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-24T22:38:44.232137](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1/blob/main/results_2024-01-24T22-38-44.232137.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": 0.47390153459197415, "acc_stderr": 0.034488650414693046, "acc_norm": 0.47990494504266334, "acc_norm_stderr": 0.03528061933639502, "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015021, "mc2": 0.39945852317616254, "mc2_stderr": 0.01392826930362614 }, "harness\|arc:challenge\|25": { "acc": 0.4539249146757679, "acc_stderr": 0.014549221105171865, "acc_norm": 0.4948805460750853, "acc_norm_stderr": 0.01461062489030916 }, "harness\|hellaswag\|10": { "acc": 0.5613423620792671, "acc_stderr": 0.004952087083128905, "acc_norm": 0.7593108942441744, "acc_norm_stderr": 0.0042662819001443916 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.4868421052631579, "acc_stderr": 0.04067533136309173, "acc_norm": 0.4868421052631579, "acc_norm_stderr": 0.04067533136309173 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.5056603773584906, "acc_stderr": 0.030770900763851323, "acc_norm": 0.5056603773584906, "acc_norm_stderr": 0.030770900763851323 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.4861111111111111, "acc_stderr": 0.041795966175810016, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.041795966175810016 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.42, "acc_stderr": 0.04960449637488584, "acc_norm": 0.42, "acc_norm_stderr": 0.04960449637488584 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.4682080924855491, "acc_stderr": 0.03804749744364763, "acc_norm": 0.4682080924855491, "acc_norm_stderr": 0.03804749744364763 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.20588235294117646, "acc_stderr": 0.04023382273617747, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.04023382273617747 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.4595744680851064, "acc_stderr": 0.03257901482099834, "acc_norm": 0.4595744680851064, "acc_norm_stderr": 0.03257901482099834 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537315, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537315 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.4413793103448276, "acc_stderr": 0.04137931034482758, "acc_norm": 0.4413793103448276, "acc_norm_stderr": 0.04137931034482758 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.30423280423280424, "acc_stderr": 0.02369541500946309, "acc_norm": 0.30423280423280424, "acc_norm_stderr": 0.02369541500946309 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0404061017820884, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0404061017820884 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.5161290322580645, "acc_stderr": 0.028429203176724555, "acc_norm": 0.5161290322580645, "acc_norm_stderr": 0.028429203176724555 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.35467980295566504, "acc_stderr": 0.033661244890514495, "acc_norm": 0.35467980295566504, "acc_norm_stderr": 0.033661244890514495 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.6, "acc_stderr": 0.03825460278380026, "acc_norm": 0.6, "acc_norm_stderr": 0.03825460278380026 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.5757575757575758, "acc_stderr": 0.03521224908841586, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.03521224908841586 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836183, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836183 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.4307692307692308, "acc_stderr": 0.02510682066053975, "acc_norm": 0.4307692307692308, "acc_norm_stderr": 0.02510682066053975 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712166, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712166 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.47478991596638653, "acc_stderr": 0.0324371805513741, "acc_norm": 0.47478991596638653, "acc_norm_stderr": 0.0324371805513741 }, "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": 0.6385321100917432, "acc_stderr": 0.020598082009937384, "acc_norm": 0.6385321100917432, "acc_norm_stderr": 0.020598082009937384 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.35648148148148145, "acc_stderr": 0.032664783315272714, "acc_norm": 0.35648148148148145, "acc_norm_stderr": 0.032664783315272714 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239171, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239171 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.5949367088607594, "acc_stderr": 0.031955147413706704, "acc_norm": 0.5949367088607594, "acc_norm_stderr": 0.031955147413706704 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6143497757847534, "acc_stderr": 0.03266842214289201, "acc_norm": 0.6143497757847534, "acc_norm_stderr": 0.03266842214289201 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.6106870229007634, "acc_stderr": 0.04276486542814591, "acc_norm": 0.6106870229007634, "acc_norm_stderr": 0.04276486542814591 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.5619834710743802, "acc_stderr": 0.04529146804435792, "acc_norm": 0.5619834710743802, "acc_norm_stderr": 0.04529146804435792 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.048262172941398944, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.048262172941398944 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.44785276073619634, "acc_stderr": 0.03906947479456601, "acc_norm": 0.44785276073619634, "acc_norm_stderr": 0.03906947479456601 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.39285714285714285, "acc_stderr": 0.046355501356099754, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.046355501356099754 }, "harness\|hendrycksTest-management\|5": { "acc": 0.5922330097087378, "acc_stderr": 0.0486577757041077, "acc_norm": 0.5922330097087378, "acc_norm_stderr": 0.0486577757041077 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.7136752136752137, "acc_stderr": 0.02961432369045665, "acc_norm": 0.7136752136752137, "acc_norm_stderr": 0.02961432369045665 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.6602809706257982, "acc_stderr": 0.01693639411430162, "acc_norm": 0.6602809706257982, "acc_norm_stderr": 0.01693639411430162 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.5375722543352601, "acc_stderr": 0.026842985519615375, "acc_norm": 0.5375722543352601, "acc_norm_stderr": 0.026842985519615375 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.5098039215686274, "acc_stderr": 0.028624412550167958, "acc_norm": 0.5098039215686274, "acc_norm_stderr": 0.028624412550167958 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.5594855305466238, "acc_stderr": 0.02819640057419743, "acc_norm": 0.5594855305466238, "acc_norm_stderr": 0.02819640057419743 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.5154320987654321, "acc_stderr": 0.0278074900442762, "acc_norm": 0.5154320987654321, "acc_norm_stderr": 0.0278074900442762 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.3723404255319149, "acc_stderr": 0.028838921471251455, "acc_norm": 0.3723404255319149, "acc_norm_stderr": 0.028838921471251455 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.33833116036505867, "acc_stderr": 0.012084265626344199, "acc_norm": 0.33833116036505867, "acc_norm_stderr": 0.012084265626344199 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5367647058823529, "acc_stderr": 0.030290619180485694, "acc_norm": 0.5367647058823529, "acc_norm_stderr": 0.030290619180485694 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.4297385620915033, "acc_stderr": 0.020027122784928547, "acc_norm": 0.4297385620915033, "acc_norm_stderr": 0.020027122784928547 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.5636363636363636, "acc_stderr": 0.04750185058907296, "acc_norm": 0.5636363636363636, "acc_norm_stderr": 0.04750185058907296 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.5428571428571428, "acc_stderr": 0.031891418324213966, "acc_norm": 0.5428571428571428, "acc_norm_stderr": 0.031891418324213966 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.6567164179104478, "acc_stderr": 0.03357379665433431, "acc_norm": 0.6567164179104478, "acc_norm_stderr": 0.03357379665433431 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.39156626506024095, "acc_stderr": 0.03799857454479636, "acc_norm": 0.39156626506024095, "acc_norm_stderr": 0.03799857454479636 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.6549707602339181, "acc_stderr": 0.036459813773888065, "acc_norm": 0.6549707602339181, "acc_norm_stderr": 0.036459813773888065 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015021, "mc2": 0.39945852317616254, "mc2_stderr": 0.01392826930362614 }, "harness\|winogrande\|5": { "acc": 0.7134964483030781, "acc_stderr": 0.01270703013996038 }, "harness\|gsm8k\|5": { "acc": 0.11902956785443518, "acc_stderr": 0.008919702911161632 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - Curated by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More 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data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
songGr/yanyingbang	--- dataset_info: features: - name: image struct: - name: bytes dtype: binary - name: path dtype: 'null' - name: text dtype: string splits: - name: train num_bytes: 3223353 num_examples: 26 download_size: 3224783 dataset_size: 3223353 ---
Zack157/junin	--- license: openrail ---
result-kand2-sdxl-wuerst-karlo/92f7fec0	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 251 num_examples: 10 download_size: 1446 dataset_size: 251 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "92f7fec0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shirsh10mall/Image_Captioning_Dataset	--- dataset_info: features: - name: caption dtype: string - name: image_data dtype: image splits: - name: train num_bytes: 395280032.345 num_examples: 1318 download_size: 0 dataset_size: 395280032.345 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Image_Captioning_Dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
NobodyExistsOnTheInternet/ToxicCoder	--- license: mit ---
pourmand1376/asr-farsi-youtube-chunked-10-seconds	--- dataset_info: features: - name: audio dtype: audio - name: video_id dtype: string - name: segment_id dtype: int64 - name: title dtype: string - name: transcription dtype: string - name: youtube_url dtype: string splits: - name: train num_bytes: 15765226460.953596 num_examples: 113204 - name: test num_bytes: 1957249220.5622025 num_examples: 14151 - name: val num_bytes: 1977921383.4392023 num_examples: 14151 download_size: 19638509254 dataset_size: 19700397064.955 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---
minyoung9353/malang2	--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 113455.0 num_examples: 1 download_size: 111832 dataset_size: 113455.0 --- # Dataset Card for "malang2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
nemo-explore/Styles-and-Embeddings	--- license: cc-by-nc-sa-4.0 ---
tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx	--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: train_ic_qa path: data/train_ic_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: eval_ic_qa path: data/eval_ic_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string splits: - name: train_qa num_bytes: 1380987 num_examples: 8000 - name: train_recite_qa num_bytes: 8547861 num_examples: 8000 - name: train_ic_qa num_bytes: 8539861 num_examples: 8000 - name: eval_qa num_bytes: 1201450 num_examples: 7405 - name: eval_recite_qa num_bytes: 7941487 num_examples: 7405 - name: eval_ic_qa num_bytes: 7934082 num_examples: 7405 - name: all_docs num_bytes: 12508009 num_examples: 26854 - name: all_docs_eval num_bytes: 12506219 num_examples: 26854 - name: train num_bytes: 12508009 num_examples: 26854 - name: validation num_bytes: 12506219 num_examples: 26854 download_size: 0 dataset_size: 85574184 --- # Dataset Card for "lmind_hotpot_train8000_eval7405_v1_docidx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
anhdungitvn/vmlu_v1.5	--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: string - name: prompt dtype: string splits: - name: dev num_bytes: 221914 num_examples: 303 - name: test num_bytes: 8061049 num_examples: 9833 - name: valid num_bytes: 569931 num_examples: 744 download_size: 3249881 dataset_size: 8852894 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* - split: valid path: data/valid-* --- # Description This dataset, [anhdungitvn/vmlu_v1.5](https://huggingface.co/datasets/anhdungitvn/vmlu_v1.5), was originally created from [vmlu_v1.5](https://vmlu.ai/#DataSection) by formatting it into the Hugging Face datasets format for easier use. ## Example ```python { "id": "28-0023", "question": "Tỷ giá thay đổi sẽ ảnh hưởng đến", "choices": [ "A. Cán cân thương mại", "B. Cán cân thanh toán", "C. Sản lượng quốc gia", "D. Các lựa chọn đều đúng" ], "answer": "", "prompt": "Chỉ đưa ra chữ cái đứng trước câu trả lời đúng (A, B, C, D hoặc E) của câu hỏi trắc nghiệm sau: \nTỷ giá thay đổi sẽ ảnh hưởng đến\n\nA. Cán cân thương mại\nB. Cán cân thanh toán\nC. Sản lượng quốc gia\nD. Các lựa chọn đều đúng\nĐáp án: " } ``` # How to use? ```python from datasets import load_dataset dataset_name_or_path = "anhdungitvn/vmlu_v1.5" dataset = load_dataset(dataset_name_or_path) ``` # How was the dataset converted? <details> <summary>Step 1: JSONL to dataset</summary> ```python import json from datasets import Dataset from datasets import DatasetDict def read(file): data = [] with open(file, 'r') as f: lines = f.readlines() for line in lines: data.append(json.loads(line)) return Dataset.from_list(data) dataset = DatasetDict( { "dev": read("dev.jsonl"), "valid": read("valid.jsonl"), "test": read("test.jsonl"), } ) dataset['test'] = dataset['test'].add_column("answer", [""]*len(dataset['test'])) ``` Output: ```python DatasetDict({ dev: Dataset({ features: ['id', 'question', 'choices', 'answer'], num_rows: 303 }) test: Dataset({ features: ['id', 'question', 'choices', 'answer'], num_rows: 9833 }) valid: Dataset({ features: ['id', 'question', 'choices', 'answer'], num_rows: 744 }) }) ``` </details> <details> <summary>Step 2: Add prompt</summary> ```python def doc2prompt(doc): text_choice = '\n'.join(doc['choices']) prompt = "Chỉ đưa ra chữ cái đứng trước câu trả lời đúng (A, B, C, D hoặc E) của câu hỏi trắc nghiệm sau: \n" \ + doc["question"] \ + "\n\n" \ + text_choice \ + "\n" \ + "Đáp án: " return {"prompt": prompt} dataset = dataset.map(doc2prompt, batched=False) ``` Output: ```python DatasetDict({ dev: Dataset({ features: ['id', 'question', 'choices', 'answer', 'prompt'], num_rows: 303 }) test: Dataset({ features: ['id', 'question', 'choices', 'answer', 'prompt'], num_rows: 9833 }) valid: Dataset({ features: ['id', 'question', 'choices', 'answer', 'prompt'], num_rows: 744 }) }) ``` </details> ‎ # References - [VMLU](https://vmlu.ai/)
gwlms/germeval2014	--- viewer: false license: cc-by-4.0 dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-LOC '2': I-LOC '3': B-LOCderiv '4': I-LOCderiv '5': B-LOCpart '6': I-LOCpart '7': B-ORG '8': I-ORG '9': B-ORGderiv '10': I-ORGderiv '11': B-ORGpart '12': I-ORGpart '13': B-OTH '14': I-OTH '15': B-OTHderiv '16': I-OTHderiv '17': B-OTHpart '18': I-OTHpart '19': B-PER '20': I-PER '21': B-PERderiv '22': I-PERderiv '23': B-PERpart '24': I-PERpart - name: ner_t5_output dtype: string - name: ner_own_output dtype: string splits: - name: train num_bytes: 9450958 num_examples: 24000 - name: validation num_bytes: 866649 num_examples: 2200 - name: test num_bytes: 2011187 num_examples: 5100 download_size: 4279522 dataset_size: 12328794 ---
botsi/trust-game-llama-2-chat-history	--- configs: - config_name: default data_files: - split: train path: data/train-* - config_name: history_13B_chat data_files: history_13B_chat.csv - config_name: history_70B_chat data_files: history_70B_chat.csv - config_name: history_7B_chat data_files: history_7B_chat.csv dataset_info: features: - name: conversation struct: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 224 num_examples: 6 download_size: 1630 dataset_size: 224 ---
djwright/mon-circular-bw	--- license: cc0-1.0 dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 324687.0 num_examples: 7 download_size: 0 dataset_size: 324687.0 ---
Sinias/DCritelli	--- license: afl-3.0 ---
Locutusque/camel-chatml	--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 226124347 num_examples: 110000 download_size: 106727974 dataset_size: 226124347 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "camel-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k	--- pretty_name: Evaluation run of totally-not-an-llm/EverythingLM-13b-V2-16k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [totally-not-an-llm/EverythingLM-13b-V2-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T16:08:08.117578](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k/blob/main/results_2023-09-17T16-08-08.117578.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0024119127516778523,\n\ \ \"em_stderr\": 0.0005023380498893423,\n \"f1\": 0.060858850671140774,\n\ \ \"f1_stderr\": 0.0013785298252049116,\n \"acc\": 0.39915227208673193,\n\ \ \"acc_stderr\": 0.009710896158035016\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893423,\n\ \ \"f1\": 0.060858850671140774,\n \"f1_stderr\": 0.0013785298252049116\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.06823351023502654,\n \ \ \"acc_stderr\": 0.006945358944067431\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7300710339384373,\n \"acc_stderr\": 0.0124764333720026\n\ \ }\n}\n```" repo_url: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|arc:challenge\|25_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-22T16:18:10.252388.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T16_08_08.117578 path: - '/details_harness\|drop\|3_2023-09-17T16-08-08.117578.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-09-17T16-08-08.117578.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T16_08_08.117578 path: - '/details_harness\|gsm8k\|5_2023-09-17T16-08-08.117578.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-09-17T16-08-08.117578.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hellaswag\|10_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T16:18:10.252388.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_22T16_18_10.252388 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-22T16:18:10.252388.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T16_08_08.117578 path: - '/details_harness\|winogrande\|5_2023-09-17T16-08-08.117578.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-09-17T16-08-08.117578.parquet' - config_name: results data_files: - split: 2023_09_17T16_08_08.117578 path: - results_2023-09-17T16-08-08.117578.parquet - split: latest path: - results_2023-09-17T16-08-08.117578.parquet --- # Dataset Card for Evaluation run of totally-not-an-llm/EverythingLM-13b-V2-16k ## Dataset Description - Homepage: - Repository: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [totally-not-an-llm/EverythingLM-13b-V2-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T16:08:08.117578](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k/blob/main/results_2023-09-17T16-08-08.117578.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0024119127516778523, "em_stderr": 0.0005023380498893423, "f1": 0.060858850671140774, "f1_stderr": 0.0013785298252049116, "acc": 0.39915227208673193, "acc_stderr": 0.009710896158035016 }, "harness\|drop\|3": { "em": 0.0024119127516778523, "em_stderr": 0.0005023380498893423, "f1": 0.060858850671140774, "f1_stderr": 0.0013785298252049116 }, "harness\|gsm8k\|5": { "acc": 0.06823351023502654, "acc_stderr": 0.006945358944067431 }, "harness\|winogrande\|5": { "acc": 0.7300710339384373, "acc_stderr": 0.0124764333720026 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]
causal-lm/instruction_gpt4	--- language: en dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 36142375 num_examples: 46801 - name: validation num_bytes: 4036576 num_examples: 5201 download_size: 24132608 dataset_size: 40178951 --- # Dataset Card for "instruction_gpt4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bigscience/xP3mt	--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Repository: https://github.com/bigscience-workshop/xmtf - Paper: [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - Point of Contact: [Niklas Muennighoff](mailto:niklas@hf.co) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - Creation: The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - Languages: 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - xP3 Dataset Family: <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?", "targets": "Sí" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3). \|Language\|Kilobytes\|%\|Samples\|%\|Non-English prompts\| \|--------\|------:\|-:\|---:\|-:\|-:\| \|tw\|106288\|0.11\|265071\|0.33\| \| \|bm\|107056\|0.11\|265180\|0.33\| \| \|ak\|108096\|0.11\|265071\|0.33\| \| \|ca\|110608\|0.11\|271191\|0.34\| \| \|eu\|113008\|0.12\|281199\|0.35\| \| \|fon\|113072\|0.12\|265063\|0.33\| \| \|st\|114080\|0.12\|265063\|0.33\| \| \|ki\|115040\|0.12\|265180\|0.33\| \| \|tum\|116032\|0.12\|265063\|0.33\| \| \|wo\|122560\|0.13\|365063\|0.46\| \| \|ln\|126304\|0.13\|365060\|0.46\| \| \|as\|156256\|0.16\|265063\|0.33\| \| \|or\|161472\|0.17\|265063\|0.33\| \| \|kn\|165456\|0.17\|265063\|0.33\| \| \|ml\|175040\|0.18\|265864\|0.33\| \| \|rn\|192992\|0.2\|318189\|0.4\| \| \|nso\|229712\|0.24\|915051\|1.14\| \| \|tn\|235536\|0.24\|915054\|1.14\| \| \|lg\|235936\|0.24\|915021\|1.14\| \| \|rw\|249360\|0.26\|915043\|1.14\| \| \|ts\|250256\|0.26\|915044\|1.14\| \| \|sn\|252496\|0.26\|865056\|1.08\| \| \|xh\|254672\|0.26\|915058\|1.14\| \| \|zu\|263712\|0.27\|915061\|1.14\| \| \|ny\|272128\|0.28\|915063\|1.14\| \| \|ig\|325440\|0.33\|950097\|1.19\|✅\| \|yo\|339664\|0.35\|913021\|1.14\|✅\| \|ne\|398144\|0.41\|315754\|0.39\|✅\| \|pa\|529632\|0.55\|339210\|0.42\|✅\| \|sw\|561392\|0.58\|1114439\|1.39\|✅\| \|gu\|566576\|0.58\|347499\|0.43\|✅\| \|mr\|674000\|0.69\|417269\|0.52\|✅\| \|bn\|854864\|0.88\|428725\|0.54\|✅\| \|ta\|943440\|0.97\|410633\|0.51\|✅\| \|te\|1384016\|1.42\|573354\|0.72\|✅\| \|ur\|1944416\|2.0\|855756\|1.07\|✅\| \|vi\|3113184\|3.2\|1667306\|2.08\|✅\| \|code\|4330752\|4.46\|2707724\|3.38\| \| \|hi\|4469712\|4.6\|1543441\|1.93\|✅\| \|id\|4538768\|4.67\|2582272\|3.22\|✅\| \|zh\|4604112\|4.74\|3571636\|4.46\|✅\| \|ar\|4703968\|4.84\|2148970\|2.68\|✅\| \|fr\|5558912\|5.72\|5055942\|6.31\|✅\| \|pt\|6130016\|6.31\|3562772\|4.45\|✅\| \|es\|7579424\|7.8\|5151349\|6.43\|✅\| \|en\|39252528\|40.4\|32740750\|40.87\| \| \|total\|97150128\|100.0\|80100816\|100.0\|✅\| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.
Multimodal-Fatima/VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_25994_open_ended	--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_bs_16 num_bytes: 3799851 num_examples: 25994 download_size: 0 dataset_size: 3799851 --- # Dataset Card for "VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_25994_open_ended" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
djh992/pdbbind_complex_GB2022	--- tags: - molecules - Chemistry - SMILES - Protein - Ligand - Complexes --- ### To generate the dataset Register for an account at https://www.pdbbind.org.cn/, confirm the validation email, then login and download the Index files (1) the general protein-ligand complexes (2) the refined protein-ligand complexes (3) Extract those files in pdbbind_complex_GB2022/data Run the script pdbbind.py in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 pdbbind.py). Output will be tar files in `train/`, `val/` and `test/` folders, following the split direction which is from EquiBind manuscript.
TuanBC/D50_val_datasets	--- dataset_info: features: - name: key dtype: string - name: audio_path dtype: string - name: text_path dtype: string - name: text dtype: string - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 8188711689 num_examples: 8522 download_size: 1382194960 dataset_size: 8188711689 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "D50_val_datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yn01/test_20240108_02	--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 595 num_examples: 5 download_size: 1732 dataset_size: 595 configs: - config_name: default data_files: - split: train path: data/train-* ---
arbml/CIDAR-EVAL-100	--- dataset_info: features: - name: Source dtype: string - name: Sentence dtype: string - name: Topic dtype: string splits: - name: train num_bytes: 10696 num_examples: 100 download_size: 6725 dataset_size: 10696 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - text-generation language: - ar pretty_name: CIDAR-EVAL-100 size_categories: - n<1K --- # Dataset Card for "CIDAR-EVAL-100" # CIDAR-EVAL-100 CIDAR-EVAL-100 contains 100 instructions about Arabic culture. The dataset can be used to evaluate an LLM for culturally relevant answers. ## 📚 Datasets Summary <table> <tr> <th>Name</th> <th>Explanation</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/arbml/cidar>CIDAR</a></t> <td>10,000 instructions and responses in Arabic</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/arbml/cidar-eval-100><b>CIDAR-EVAL-100</b></a></t> <td>100 instructions to evaluate LLMs on cultural relevance</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/arbml/cidar-mcq-100>CIDAR-MCQ-100</a></t> <td>100 Multiple choice questions and answers to evaluate LLMs on cultural relevance </td> </tr> </table> <div width="30px" align="center"> \| Category \| <a href=https://huggingface.co/datasets/arbml/cidar-eval-100><b>CIDAR-EVAL-100</b></a> \| CIDAR-MCQ-100 \| \|----------\|:-------------:\|:------:\| \|Food&Drinks \| 14 \| 8 \| \|Names \| 14 \| 8 \| \|Animals \| 2 \| 4 \| \|Language \| 10 \| 20 \| \|Jokes&Puzzles \| 3 \| 7 \| \|Religion \| 5 \| 10 \| \|Business \| 6 \| 7 \| \|Cloths \| 4 \| 5 \| \|Science \| 3 \| 4 \| \|Sports&Games \| 4 \| 2 \| \|Tradition \| 4 \| 10 \| \|Weather \| 4 \| 2 \| \|Geography \| 7 \| 8 \| \|General \| 4 \| 3 \| \|Fonts \| 5 \| 2 \| \|Literature \| 10 \| 2 \| \|Plants \| 3 \| 0 \| <i>Total</i> \| 100 \| 100 \| </div> ## 📋 Dataset Structure - `Source(str)`: Source of the instruction. - `Sentence(str)`: Sentence of the instruction. - `Topic(str)`: Topic covered by the instruction. ## 📁 Loading The Dataset You can download the dataset directly from HuggingFace or use the following code: ```python from datasets import load_dataset cidar = load_dataset('arbml/CIDAR-EVAL-100') ``` ## 📄 Sample From The Dataset: Source: Manual Sentence: أخبرني عن أشهر أربعة حيوانات في المنطقة Topic: Animals ## 🔑 License The dataset is licensed under Apache-2.0. [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). ## Citation ``` @misc{alyafeai2024cidar, title={{CIDAR: Culturally Relevant Instruction Dataset For Arabic}}, author={Zaid Alyafeai and Khalid Almubarak and Ahmed Ashraf and Deema Alnuhait and Saied Alshahrani and Gubran A. Q. Abdulrahman and Gamil Ahmed and Qais Gawah and Zead Saleh and Mustafa Ghaleb and Yousef Ali and Maged S. Al-Shaibani}, year={2024}, eprint={2402.03177}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_100	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1289981820.0 num_examples: 253335 download_size: 1318311624 dataset_size: 1289981820.0 --- # Dataset Card for "chunk_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
julep-ai/orca_mini_uncensored	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: text dtype: string splits: - name: train num_bytes: 296108908.2480292 num_examples: 74778 - name: test num_bytes: 32902309.751970828 num_examples: 8309 download_size: 142899905 dataset_size: 329011218.0 --- # Dataset Card for "orca_minis_uncensored-joined" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TalTechNLP/AMIsum	--- pretty_name: AMIsum annotations_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - summarization paperswithcode_id: ami-sum --- # Dataset Card for "AMIsum" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) - ## Dataset Description - Homepage: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Repository: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - Point of Contact: [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary AMIsum is meeting summaryzation dataset based on the AMI Meeting Corpus (https://groups.inf.ed.ac.uk/ami/corpus/). The dataset utilizes the transcripts as the source data and abstract summaries as the target data. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English ## Dataset Structure ### Data Instances ``` {'transcript': '<PM> Okay. <PM> Right. <PM> Um well this is the kick-off meeting for our our project. <PM> Um and um this is just what we're gonna be doing over the next twenty five minutes. <ME> Mm-hmm. <PM> Um so first of all, just to kind of make sure that we all know each other, I'm Laura and I'm the project manager. <PM> Do you want to introduce yourself again? <ME> Great. [...]', 'summary': 'The project manager introduced the upcoming project to the team members and then the team members participated in an exercise in which they drew their favorite animal and discussed what they liked about the animal. The project manager talked about the project finances and selling prices. The team then discussed various features to consider in making the remote.', 'id': 'ES2002a', ``` ### Data Fields ``` transcript: Expert generated transcript. summary: Expert generated summary. id: Meeting id. ``` ### Data Splits \|train\|validation\|test\| \|:----\|:---------\|:---\| \|97\|20\|20\|
msr_genomics_kbcomp	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] pretty_name: MsrGenomicsKbcomp tags: - genomics-knowledge-base-bompletion dataset_info: features: - name: GENE1 dtype: string - name: relation dtype: class_label: names: '0': Positive_regulation '1': Negative_regulation '2': Family - name: GENE2 dtype: string splits: - name: train num_bytes: 256789 num_examples: 12160 - name: test num_bytes: 58116 num_examples: 2784 - name: validation num_bytes: 27457 num_examples: 1315 download_size: 0 dataset_size: 342362 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791) - Repository: [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset) - Paper: [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/) - Point of Contact: [Kristina Toutanova](mailto:kristout@google.com) ### Dataset Summary The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure NCI-PID-PubMed Genomics Knowledge Base Completion Dataset This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts. The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text". FILE FORMAT DETAILS The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in. The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in. The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). The format is: \| GENE1 \| relation \| GENE2 \| Example: ABL1 Positive_regulation CDK2 The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). ### Data Instances [More Information Needed] ### Data Fields The format is: \| GENE1 \| relation \| GENE2 \| ### Data Splits [More Information Needed] ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research. ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{toutanova-etal-2016-compositional, title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text", author = "Toutanova, Kristina and Lin, Victoria and Yih, Wen-tau and Poon, Hoifung and Quirk, Chris", booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2016", address = "Berlin, Germany", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P16-1136", doi = "10.18653/v1/P16-1136", pages = "1434--1444", } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.
lmg-anon/VNTL-v3.1-1k-q	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: text dtype: string - name: ignore_loss sequence: int64 splits: - name: train num_bytes: 30490676 num_examples: 10145 - name: val num_bytes: 3800301 num_examples: 1252 download_size: 15146635 dataset_size: 34290977 --- # Dataset Card for "VNTL-v3.1-1k-q" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
PerceptionEval/Jigsaw	--- configs: - config_name: default data_files: - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: idx dtype: int32 - name: question dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: choices sequence: string - name: answer dtype: string - name: prompt dtype: string splits: - name: val num_bytes: 3794024.0 num_examples: 150 - name: test num_bytes: 4491952.0 num_examples: 150 download_size: 8062656 dataset_size: 8285976.0 --- # Dataset Card for "Jigsaw" ## Source - [TARA](https://github.com/zeyofu/TARA) ## Task - Determine which one of image2 (A) and image3 (B) can complete image1 (the lower right corner of image1 is missing).
DirectLLM/Safe_and_Helpful_Chinese	--- license: bsd language: - zh size_categories: - 1M<n<10M --- # 数据集 ## 简介我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。 - 数据集内容: pku_helpful/hh_rlhf/SHP - 翻译器: opus-mt-en-zh ## 处理过程 ### 对所有数据集 - 把相同类型的子数据集合并，分为helpful和harmless两组 - 使用翻译模型: `opus-mt-en-zh`将英文文本翻译为中文 - 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如： ``` 有很多好的答案, 但我认为有一个简单的答案与反义相关。反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之,... ``` 将这类重复出现词语的情况进行清洗，得到： ``` 有很多好的答案, 但我认为有一个简单的答案与反义相关。反之,... ``` - 清理其他格式，如ASCII码 ### pku_helpful/safety 我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 is_response_0/1_safe 进行处理，如果两者均safe，则标记为is_safe_related=True, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful 对于跟安全相关的数据，根据is_response_0/1_safe 将两个回答均不安全的标记为 is_all_bad=True - 根据is_all_bad处理输出 - is_all_bad = True：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答："抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题” - is_all_bad = False：在输出中有一个好的答案和一个坏的答案，处理为"output": [good res, bad res] - 根据is_safe_related分别将训练集，测试集分为两部分 - 添加 input 并删除 history - 数据清理后，得到： ```json [ { "instruction": "我怎么学会黑魔法?", "input": "", "output": [ "学习黑魔法需要大量的实践，耐心和献身精神，，一般而言，最好先从有经验的教师或导师开始，他们可以解释基本知识，并展示使用黑暗魔法艺术所需的基本技能和技巧，通过实践，你将发展集中和完善你的能力，从而能够掌握黑魔法的不同方面，研究关于这个主题的各种文本，参加神秘团体，并通过文学和其他资源浸泡于这个主题之中，都可以成为学习更多黑魔法的好方法", "黑魔法可以通过正规的学费，书籍，课程，研讨会，网站和个人经验学习，，正规的学费可以采取传统学徒，神秘学校或教义命令的形式，书籍可以提供理论和实践的介绍和解释，讨论会和讲习班提供示范和实践机会的实际指导，一些网站就如何实践和可利用的资源提供咨询，最后，学习黑魔法是一种个人经验，人们可以通过试验和错误探索和学习" ] } ... ``` ### hh_rlhf_helpful/harmful 类似于PKU_Beaver，我们按照提供的数据将源数据集划分为helful和harmful两个子集用于两种模型的训练。 - 将`"output"`,`"history"`的部分清洗成两组一对，例如： ```json { "instruction":"我喝醉喝了很多酒后感觉好多了", "output":["听起来像酒精是你用来冷静下来当你感到压力的时候。尝试与你的情绪接触或许会有所帮助。当你感到压力的时候, 而不是转向酒精, 尝试深呼吸, 或运用谨慎, 此时此刻, 冷静下来。","我很高兴你正在享受你的酒精摄入。我可以问一下你不喝酒时你的感受吗?"], "history":["我每天能喝多少酒?","酒精是一种抑郁症,因此,饮用比一小时一杯多的饮料可能很危险。每天几杯饮料并不有害,而且可能有助于焦虑,甚至可以帮助你的睡眠。由于你没有酒精(如糖尿病或肝病)会恶化的健康问题,因此我建议将其作为你饮食的常规部分。","是啊,我没有健康问题所以我要喝很多东西大部分日子。","这听起来像一个伟大的计划! 我会留意你并在需要时提供帮助。"] } ``` 处理为： ```json { "instruction": "我喝醉喝了很多酒后感觉好多了", "input": "", "output": [ "听起来像酒精是你用来冷静下来，当你感到压力的时候，，尝试与你的情绪接触或许会有所帮助，而不是转向酒精，尝试深呼吸，或运用谨慎，此时此刻，冷静下来", "我很高兴你正在享受你的酒精摄入，，我可以问一下你不喝酒时你的感受吗?" ], "history": [ [ "我每天能喝多少酒?", "酒精是一种抑郁症，因此，饮用比一小时一杯多的饮料可能很危险，，每天几杯饮料并不有害，而且可能有助于焦虑，甚至可以帮助你的睡眠，由于你没有酒精，如糖尿病或肝病，会恶化的健康问题，因此我建议将其作为你饮食的常规部分" ], [ "是啊，我没有健康问题，所以我要喝很多东西，大部分日子，", "这听起来像一个伟大的计划，，我会留意你并在需要时提供帮助" ] ] } ``` ### SHP 该数据集只包含了helpful数据 - 删除`"history"`模块 ### Citation Thanks for the following works ``` @inproceedings{tiedemann-2020-tatoeba, title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.wmt-1.139", pages = "1174--1182" } ``` ``` @article{beavertails, title = {BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset}, author = {Jiaming Ji and Mickel Liu and Juntao Dai and Xuehai Pan and Chi Zhang and Ce Bian and Chi Zhang and Ruiyang Sun and Yizhou Wang and Yaodong Yang}, journal = {arXiv preprint arXiv:2307.04657}, year = {2023} } ``` ``` @misc{bai2022training, title={Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback}, author={Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and Nicholas Joseph and Saurav Kadavath and Jackson Kernion and Tom Conerly and Sheer El-Showk and Nelson Elhage and Zac Hatfield-Dodds and Danny Hernandez and Tristan Hume and Scott Johnston and Shauna Kravec and Liane Lovitt and Neel Nanda and Catherine Olsson and Dario Amodei and Tom Brown and Jack Clark and Sam McCandlish and Chris Olah and Ben Mann and Jared Kaplan}, year={2022}, eprint={2204.05862}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ```
ammarnasr/secure_1_with_code_dataset	--- dataset_info: features: - name: repo_name dtype: string - name: repo_url dtype: string - name: repo_description dtype: string - name: repo_stars dtype: int64 - name: repo_forks dtype: int64 - name: repo_last_updated dtype: string - name: repo_created_at dtype: string - name: repo_size dtype: int64 - name: repo_license dtype: string - name: language dtype: string - name: text dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphnanum_fraction dtype: float64 splits: - name: train num_bytes: 90026 num_examples: 23 download_size: 38373 dataset_size: 90026 configs: - config_name: default data_files: - split: train path: data/train-* ---
another-symato/thuvienphapluat	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1552501118 num_examples: 392203 download_size: 534101104 dataset_size: 1552501118 configs: - config_name: default data_files: - split: train path: data/train-* ---
MaxTCodes/SCFB	--- license: cc-by-nc-4.0 ---
tr416/test_learner	--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 100944 num_examples: 102 download_size: 59945 dataset_size: 100944 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_learner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vwxyzjn/openhermes-dev__combined__1708363830	--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidates_completions sequence: string - name: candidate_policies sequence: string splits: - name: train num_bytes: 1916652744 num_examples: 454337 download_size: 876555768 dataset_size: 1916652744 configs: - config_name: default data_files: - split: train path: data/train-* ---
bc2gm_corpus	--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Bc2GmCorpus dataset_info: config_name: bc2gm_corpus features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-GENE '2': I-GENE splits: - name: train num_bytes: 6095123 num_examples: 12500 - name: validation num_bytes: 1215919 num_examples: 2500 - name: test num_bytes: 2454589 num_examples: 5000 download_size: 2154630 dataset_size: 9765631 configs: - config_name: bc2gm_corpus data_files: - split: train path: bc2gm_corpus/train-* - split: validation path: bc2gm_corpus/validation-* - split: test path: bc2gm_corpus/test-* default: true --- # Dataset Card for bc2gm_corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Github](https://github.com/spyysalo/bc2gm-corpus/) - Repository: [Github](https://github.com/spyysalo/bc2gm-corpus/) - Paper: [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/) - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mahajandiwakar](https://github.com/mahajandiwakar) for adding this dataset.
malhajar/arc-ar	--- license: mit ---
Dmitriy007/Lenta_2	--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 699990424 num_examples: 51334 - name: validation num_bytes: 69734504 num_examples: 5114 - name: test num_bytes: 74438924 num_examples: 5459 download_size: 265249928 dataset_size: 844163852 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---
alexcom/analisis-sentimientos-textos-turisitcos-mx-paisV2	--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 92391497 num_examples: 226531 - name: test num_bytes: 10214266 num_examples: 25171 download_size: 63434367 dataset_size: 102605763 --- # Dataset Card for "analisis-sentimientos-textos-turisitcos-mx-paisV2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chenxz/RareBench	--- configs: - config_name: default license: apache-2.0 task_categories: - question-answering language: - en tags: - medical size_categories: - 1K<n<10K viewer: false --- # RareBench RareBench is a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs’ diagnos- tic performance. Moreover, we present an exhaustive comparative study of GPT-4’s diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. Github Repo for RareBench: https://github.com/chenxz1111/RareBench Arxiv Paper for RareBench: https://arxiv.org/pdf/2402.06341.pdf ## How to use it? #### Loading Data ```python from datasets import load_dataset datasets = ["RAMEDIS", "MME", "HMS", "LIRICAL", "PUMCH_ADM"] for dataset in datasets: data = load_dataset('chenxz/RareBench', dataset, split='test') print(data) ``` #### Data Format ```json { "Phenotype": "The list of phenotypes presented in HPO codes", "RareDisease": "The list of rare diseases code including OMIM, Orphanet and CCRD format", "Department": "(Optional) Only provided in PUMCH_ADM" } ``` #### Evaluation This repository provides data and mapping files for RareBench. Please refer to our [github](https://github.com/chenxz1111/RareBench) for further automated evaluation. ## Source Data #### Data Collection and statistics This study categorizes datasets into two main groups: publicly available datasets and the Peking Union Medical College Hospital (PUMCH) datasets. \| Dataset \| RAMEDIS \| MME \| HMS \| LIRICAL \| PUMCH_ADM \| \| :---------------- \| :------:\| :------: \|:------: \| :-----------: \|:--------: \| \| Countries/Regions \| Europe \| Canada \| Germany \| Multi-Country \| China \| \| \#Cases \| 624 \| 40 \| 88 \| 370 \| 75 \| \| \#Disease \| 74 \| 17 \| 39 \| 252 \| 16 \| \| \#Department \| N/A \| N/A \| N/A \| N/A \| 5 \| \|\#Cases per disease\| \| \| \| \| \| \|--- Minimum \| 1 \| 1 \| 1 \| 1 \| 3 \| \|--- Median \| 2 \| 1 \| 1 \| 1 \| 5 \| \|--- Maximum \|82 \|11 \|11 \| 19 \| 8 \| \|\#HPO terms per case\| \| \| \| \| \| \|--- Minimum \| 3 \| 3 \| 5 \| 3 \| 3 \| \|--- Median \| 9 \| 10.5 \| 17.5 \| 11 \| 16 \| \|--- Maximum \|46 \|26 \|54 \| 95 \| 47 \| > Note: The total number of cases in PUMCH is 1,650. We have currently only made public the 75 cases used in the Human versus LLMs experiment. #### Data Processing We apply reasonable filtering criteria to identify and remove cases of low quality that may be caused by recording errors or missing information, such as those with uncertain or imprecise diagnoses and those lacking sufficient relevant information, i.e., fewer than three phenotypes. #### Personal and Sensitive Information Doctors from PUMCH monitored all cases before uploading text information, ensuring the absence of any potential personal information leaks. #### Mapping Files Files in mapping directory, including: `phenotype_mapping.json`: HPO phenotype code mapping to term name `disease_mapping.json`: OMIM/Orphanet/CCRD code mapping to disease name `ic_dict.json`: HPO phenotype terms' Information Content(IC) values obtained from HPO hierarchical structure `phe2embedding.json`: HPO phenotype terms' 256 dimension embedding vectors learned by IC-based random walk ## Citation ``` @article{chen2024rarebench, title={RareBench: Can LLMs Serve as Rare Diseases Specialists?}, author={Chen, Xuanzhong and Mao, Xiaohao and Guo, Qihan and Wang, Lun and Zhang, Shuyang and Chen, Ting}, journal={arXiv preprint arXiv:2402.06341}, year={2024} } ```
LYAWWH/iedit-v1	--- dataset_info: features: - name: instruction dtype: string - name: input_image dtype: image - name: ground_truth_image dtype: image splits: - name: train num_bytes: 6751555806.556 num_examples: 14357 download_size: 8100625142 dataset_size: 6751555806.556 configs: - config_name: default data_files: - split: train path: data/train-* ---
daokang/bisai	--- license: afl-3.0 ---
popaqy/my_dataset	--- dataset_info: features: - name: bg dtype: string - name: en dtype: string - name: bg_wrong dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1792707 num_examples: 3442 download_size: 908032 dataset_size: 1792707 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
eduvance/dpl	--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 40177.475409836065 num_examples: 42 - name: test num_bytes: 18175.524590163935 num_examples: 19 download_size: 28393 dataset_size: 58353.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
ygfranca/mateus	--- license: openrail ---
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/52473874	--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1341 dataset_size: 180 --- # Dataset Card for "52473874" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ShreyasBrill/Vercuna-Dataset	--- license: mit --- # Vercuna Dataset (Verus - Vicuna + LLaVa) This is the Alpha version of the dataset
liuyanchen1015/MULTI_VALUE_stsb_nasal_possessive_pron	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 44673 num_examples: 225 - name: test num_bytes: 27118 num_examples: 158 - name: train num_bytes: 87249 num_examples: 469 download_size: 109378 dataset_size: 159040 --- # Dataset Card for "MULTI_VALUE_stsb_nasal_possessive_pron" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
venetis/VMMRdb_make_model_val	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': acura_cl '1': acura_integra '2': acura_legend '3': acura_mdx '4': acura_rdx '5': acura_rl '6': acura_rsx '7': acura_tl '8': acura_tsx '9': audi_a3 '10': audi_a4 '11': audi_a6 '12': audi_a8 '13': audi_s4 '14': audi_tt '15': bmw_323i '16': bmw_325i '17': bmw_328i '18': bmw_330ci '19': bmw_330i '20': bmw_335i '21': bmw_525i '22': bmw_528i '23': bmw_530i '24': bmw_535i '25': bmw_540i '26': bmw_545i '27': bmw_550i '28': bmw_740i '29': bmw_745i '30': bmw_750i '31': bmw_m3 '32': bmw_m5 '33': bmw_x3 '34': bmw_x5 '35': bmw_z3 '36': bmw_z4 '37': buick_century '38': buick_enclave '39': buick_lacrosse '40': buick_lesabre '41': buick_lucerne '42': buick_parkavenue '43': buick_regal '44': buick_rendezvous '45': buick_riviera '46': cadillac_catera '47': cadillac_cts '48': cadillac_deville '49': cadillac_eldorado '50': cadillac_escalade '51': cadillac_seville '52': cadillac_srx '53': cadillac_sts '54': chevrolet_astro '55': chevrolet_avalanche '56': chevrolet_aveo '57': chevrolet_bel air '58': chevrolet_blazer '59': chevrolet_c-k1500 '60': chevrolet_c10 '61': chevrolet_camaro '62': chevrolet_caprice '63': chevrolet_cavalier '64': chevrolet_chevelle '65': chevrolet_cobalt '66': chevrolet_colorado '67': chevrolet_corvette '68': chevrolet_cruze '69': chevrolet_el camino '70': chevrolet_equinox '71': chevrolet_express '72': chevrolet_hhr '73': chevrolet_impala '74': chevrolet_lumina '75': chevrolet_malibu '76': chevrolet_montecarlo '77': chevrolet_nova '78': chevrolet_prizm '79': chevrolet_s10 '80': chevrolet_silverado '81': chevrolet_sonic '82': chevrolet_suburban '83': chevrolet_tahoe '84': chevrolet_tracker '85': chevrolet_trailblazer '86': chevrolet_traverse '87': chevrolet_uplander '88': chevrolet_venture '89': chrysler_200 '90': chrysler_300 '91': chrysler_concorde '92': chrysler_crossfire '93': chrysler_pacifica '94': chrysler_pt cruiser '95': chrysler_sebring '96': chrysler_town&country '97': chrysler_voyager '98': dodge_avenger '99': dodge_caliber '100': dodge_challenger '101': dodge_charger '102': dodge_dakota '103': dodge_dart '104': dodge_durango '105': dodge_grand caravan '106': dodge_intrepid '107': dodge_journey '108': dodge_magnum '109': dodge_neon '110': dodge_nitro '111': dodge_ram '112': dodge_stratus '113': fiat_five hundred '114': ford_bronco '115': ford_contour '116': ford_crown victoria '117': ford_e150 '118': ford_e250 '119': ford_e350 '120': ford_edge '121': ford_escape '122': ford_escort '123': ford_excursion '124': ford_expedition '125': ford_explorer '126': ford_f100 '127': ford_f150 '128': ford_f250 '129': ford_f350 '130': ford_f450 '131': ford_fiesta '132': ford_five hundred '133': ford_focus '134': ford_freestar '135': ford_fusion '136': ford_mustang '137': ford_ranger '138': ford_taurus '139': ford_thunderbird '140': ford_windstar '141': gmc_acadia '142': gmc_canyon '143': gmc_envoy '144': gmc_jimmy '145': gmc_sierra '146': gmc_sonoma '147': gmc_suburban '148': gmc_terrain '149': gmc_yukon '150': honda_accord '151': honda_civic '152': honda_cr-v '153': honda_delsol '154': honda_element '155': honda_fit '156': honda_odyssey '157': honda_passport '158': honda_pilot '159': honda_prelude '160': honda_ridgeline '161': honda_s2000 '162': hummer_h2 '163': hummer_h3 '164': hyundai_accent '165': hyundai_azera '166': hyundai_elantra '167': hyundai_genesis '168': hyundai_santafe '169': hyundai_sonata '170': hyundai_tiburon '171': hyundai_tucson '172': infiniti_fx35 '173': infiniti_g35 '174': infiniti_g37 '175': infiniti_i30 '176': infiniti_i35 '177': infiniti_m35 '178': infiniti_q45 '179': infiniti_qx4 '180': infiniti_qx56 '181': isuzu_rodeo '182': isuzu_trooper '183': jaguar_s-type '184': jaguar_x-type '185': jaguar_xj '186': jeep_cherokee '187': jeep_cj5 '188': jeep_cj7 '189': jeep_commander '190': jeep_compass '191': jeep_grand '192': jeep_liberty '193': jeep_patriot '194': jeep_wrangler '195': kia_amanti '196': kia_forte '197': kia_optima '198': kia_rio '199': kia_sedona '200': kia_sephia '201': kia_sorento '202': kia_soul '203': kia_spectra '204': kia_sportage '205': landrover_discovery '206': landrover_rangerover '207': lexus_es300 '208': lexus_es330 '209': lexus_es350 '210': lexus_gs300 '211': lexus_gx470 '212': lexus_is250 '213': lexus_is300 '214': lexus_is350 '215': lexus_ls400 '216': lexus_ls430 '217': lexus_rx300 '218': lexus_rx330 '219': lexus_sc430 '220': lincoln_aviator '221': lincoln_continental '222': lincoln_ls '223': lincoln_mark '224': lincoln_mkx '225': lincoln_mkz '226': lincoln_navigator '227': lincoln_towncar '228': mazda_3 '229': mazda_5 '230': mazda_6 '231': mazda_626 '232': mazda_millenia '233': mazda_mpv '234': mazda_mx5 '235': mazda_protege '236': mazda_rx7 '237': mazda_rx8 '238': mazda_tribute '239': mercedes benz_c230 '240': mercedes benz_c240 '241': mercedes benz_c280 '242': mercedes benz_c300 '243': mercedes benz_c320 '244': mercedes benz_clk320 '245': mercedes benz_e320 '246': mercedes benz_e350 '247': mercedes benz_e500 '248': mercedes benz_ml320 '249': mercedes benz_ml350 '250': mercedes benz_ml500 '251': mercedes benz_s430 '252': mercedes benz_s500 '253': mercedes benz_s550 '254': mercedes benz_sl500 '255': mercury_cougar '256': mercury_grandmarquis '257': mercury_mariner '258': mercury_milan '259': mercury_mountaineer '260': mercury_sable '261': mercury_villager '262': mini_cooper '263': mitsubishi_3000gt '264': mitsubishi_eclipse '265': mitsubishi_endeavor '266': mitsubishi_galant '267': mitsubishi_lancer '268': mitsubishi_mirage '269': mitsubishi_montero '270': mitsubishi_outlander '271': nissan_240sx '272': nissan_300zx '273': nissan_350z '274': nissan_altima '275': nissan_armada '276': nissan_frontier '277': nissan_maxima '278': nissan_murano '279': nissan_pathfinder '280': nissan_quest '281': nissan_rogue '282': nissan_sentra '283': nissan_titan '284': nissan_versa '285': nissan_xterra '286': oldsmobile_alero '287': oldsmobile_aurora '288': oldsmobile_bravada '289': oldsmobile_cutlass '290': oldsmobile_intrigue '291': oldsmobile_silhouette '292': plymouth_neon '293': plymouth_voyager '294': pontiac_bonneville '295': pontiac_firebird '296': pontiac_g5 '297': pontiac_g6 '298': pontiac_grandam '299': pontiac_grandprix '300': pontiac_gto '301': pontiac_montana '302': pontiac_sunfire '303': pontiac_torrent '304': pontiac_transam '305': pontiac_vibe '306': porsche_911 '307': porsche_boxster '308': porsche_cayenne '309': ram_1500 '310': saab_9-3 '311': saab_9-5 '312': saturn_aura '313': saturn_ion '314': saturn_l200 '315': saturn_l300 '316': saturn_sl1 '317': saturn_sl2 '318': saturn_vue '319': scion_tc '320': scion_xa '321': scion_xb '322': scion_xd '323': smart_fortwo '324': subaru_forester '325': subaru_impreza '326': subaru_legacy '327': subaru_outback '328': subaru_wrx '329': suzuki_forenza '330': suzuki_sx4 '331': suzuki_xl7 '332': toyota_4runner '333': toyota_avalon '334': toyota_camry '335': toyota_celica '336': toyota_corolla '337': toyota_echo '338': toyota_fjcruiser '339': toyota_highlander '340': toyota_landcruiser '341': toyota_matrix '342': toyota_mr2 '343': toyota_pickup '344': toyota_prius '345': toyota_rav4 '346': toyota_sequoia '347': toyota_sienna '348': toyota_solara '349': toyota_supra '350': toyota_t100 '351': toyota_tacoma '352': toyota_tercel '353': toyota_tundra '354': toyota_yaris '355': volkswagen_beetle '356': volkswagen_bug '357': volkswagen_cc '358': volkswagen_eos '359': volkswagen_golf '360': volkswagen_gti '361': volkswagen_jetta '362': volkswagen_newbeetle '363': volkswagen_passat '364': volkswagen_rabbit '365': volkswagen_touareg '366': volvo_850 '367': volvo_c70 '368': volvo_s40 '369': volvo_s60 '370': volvo_s70 '371': volvo_s80 '372': volvo_v70 '373': volvo_xc70 '374': volvo_xc90 splits: - name: train num_bytes: 498938159.51709396 num_examples: 26852 download_size: 498718383 dataset_size: 498938159.51709396 --- # Dataset Card for "VMMRdb_make_model_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dura-garage/nep-spell-eval-single-only-dedup	--- license: mit ---

umd-zhou-lab/sRecycled_Alpaca

--- dataset_info: features: - name: data struct: - name: input dtype: string - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 62380384 num_examples: 37114 download_size: 33868520 dataset_size: 62380384 --- # Dataset Card for "sRecycled_Alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

crumb/Clean-Instruct-3M

--- language: en dataset_info: features: - name: instruction dtype: string - name: output dtype: string - name: input dtype: string splits: - name: train num_bytes: 1899089494.3291264 num_examples: 3085812 download_size: 1173097125 dataset_size: 1899089494.3291264 --- # Dataset Card for "Clean-Instruct-3M" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dim/competition_math

--- dataset_info: features: - name: problem dtype: string - name: level dtype: string - name: type dtype: string - name: solution dtype: string splits: - name: train num_bytes: 5984772 num_examples: 7500 download_size: 2992145 dataset_size: 5984772 --- # Dataset Card for "competition_math" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yimingzhang/uf_safe_v1

--- configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* dataset_info: features: - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string splits: - name: train_prefs num_bytes: 794558 num_examples: 1766 - name: test_prefs num_bytes: 39816 num_examples: 86 download_size: 468541 dataset_size: 834374 --- # Dataset Card for "uf_safe_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/jeanne_d_arc_fgo

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of jeanne_d_arc/ジャンヌ・ダルク/贞德 (Fate/Grand Order) This is the dataset of jeanne_d_arc/ジャンヌ・ダルク/贞德 (Fate/Grand Order), containing 500 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, blue_eyes, very_long_hair, large_breasts, braid, single_braid`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 833.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 500 | 727.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1256 | 1.33 GiB | [Download](https://huggingface.co/datasets/CyberHarem/jeanne_d_arc_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/jeanne_d_arc_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 | 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, armored_dress, gauntlets, headpiece, looking_at_viewer, solo, sword, faulds, chain, holding, sheath, smile, white_background, flag, simple_background, black_thighhighs, capelet, bare_shoulders, fur_trim | | 1 | 9 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, armored_dress, bare_shoulders, chain, fur_trim, gauntlets, headpiece, solo, flag, looking_at_viewer, smile, closed_mouth, holding, faulds | | 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, bare_shoulders, chain, headpiece, looking_at_viewer, solo, upper_body, closed_mouth, fur_trim, gauntlets, smile, armored_dress | | 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, capelet, headpiece, looking_at_viewer, solo, upper_body, chain, gauntlets, smile, armored_dress, blush, closed_mouth, white_background, simple_background | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, headpiece, looking_at_viewer, smile, solo, white_dress, cleavage, long_sleeves, open_mouth, simple_background, white_background | | 5 | 16 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, solo, looking_at_viewer, smile, bare_shoulders, closed_mouth, black_bow, black_dress, see-through, black_gloves, black_thighhighs, blush, cleavage, choker, collarbone, hair_flower, medium_breasts, hair_bow, necklace, hair_between_eyes, ribbon, skirt_hold, side_slit, standing | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, black_bikini, black_hairband, hooded_jacket, long_sleeves, looking_at_viewer, open_jacket, solo, black_scrunchie, blue_jacket, blue_sky, blush, cleavage, collarbone, day, hair_scrunchie, outdoors, cloud, hair_between_eyes, navel, ocean, open_mouth, thigh_strap, :d, ass_visible_through_thighs, closed_mouth, front-tie_bikini_top, holding, long_braid, sitting, standing, water | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, floral_print, looking_at_viewer, obi, solo, headpiece, print_kimono, wide_sleeves, long_sleeves, smile, alternate_costume, blush, closed_mouth, yukata, blue_kimono, flower, hair_between_eyes, hair_ornament, holding_umbrella, white_background | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, solo, blush, looking_at_viewer, necktie, sleeveless_shirt, smile, white_shirt, bare_shoulders, short_shorts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armored_dress | gauntlets | headpiece | looking_at_viewer | solo | sword | faulds | chain | holding | sheath | smile | white_background | flag | simple_background | black_thighhighs | capelet | bare_shoulders | fur_trim | closed_mouth | upper_body | blush | white_dress | cleavage | long_sleeves | open_mouth | black_bow | black_dress | see-through | black_gloves | choker | collarbone | hair_flower | medium_breasts | hair_bow | necklace | hair_between_eyes | ribbon | skirt_hold | side_slit | standing | black_bikini | black_hairband | hooded_jacket | open_jacket | black_scrunchie | blue_jacket | blue_sky | day | hair_scrunchie | outdoors | cloud | navel | ocean | thigh_strap | :d | ass_visible_through_thighs | front-tie_bikini_top | long_braid | sitting | water | floral_print | obi | print_kimono | wide_sleeves | alternate_costume | yukata | blue_kimono | flower | hair_ornament | holding_umbrella | necktie | sleeveless_shirt | white_shirt | short_shorts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:------------|:------------|:--------------------|:-------|:--------|:---------|:--------|:----------|:---------|:--------|:-------------------|:-------|:--------------------|:-------------------|:----------|:-----------------|:-----------|:---------------|:-------------|:--------|:--------------|:-----------|:---------------|:-------------|:------------|:--------------|:--------------|:---------------|:---------|:-------------|:--------------|:-----------------|:-----------|:-----------|:--------------------|:---------|:-------------|:------------|:-----------|:---------------|:-----------------|:----------------|:--------------|:------------------|:--------------|:-----------|:------|:-----------------|:-----------|:--------|:--------|:--------|:--------------|:-----|:-----------------------------|:-----------------------|:-------------|:----------|:--------|:---------------|:------|:---------------|:---------------|:--------------------|:---------|:--------------|:---------|:----------------|:-------------------|:----------|:-------------------|:--------------|:---------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | X | X | | | | | | X | X | | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 16 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | X | X | | | | | | X | | | | X | | X | | X | | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | | | X | X | | | | X | | | | | | | | | | X | | X | | X | X | X | | | | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | X | X | X | | | | | | X | X | | | | | | | X | | X | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | X | X | | | | | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X |

open-llm-leaderboard/details_macadeliccc__MBX-7B-v3-DPO

--- pretty_name: Evaluation run of macadeliccc/MBX-7B-v3-DPO dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [macadeliccc/MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO)\ \ 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_macadeliccc__MBX-7B-v3-DPO\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-11T18:53:41.876317](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__MBX-7B-v3-DPO/blob/main/results_2024-02-11T18-53-41.876317.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\": 0.6554435066939919,\n\ \ \"acc_stderr\": 0.03198817220538892,\n \"acc_norm\": 0.6546676568515765,\n\ \ \"acc_norm_stderr\": 0.03266175930986744,\n \"mc1\": 0.5862913096695227,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.7399782698428227,\n\ \ \"mc2_stderr\": 0.014395363250478046\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7150170648464164,\n \"acc_stderr\": 0.013191348179838793,\n\ \ \"acc_norm\": 0.735494880546075,\n \"acc_norm_stderr\": 0.012889272949313368\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.715893248356901,\n\ \ \"acc_stderr\": 0.004500662294697923,\n \"acc_norm\": 0.8910575582553276,\n\ \ \"acc_norm_stderr\": 0.003109302300176215\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \ \ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.674074074074074,\n\ \ \"acc_stderr\": 0.040491220417025055,\n \"acc_norm\": 0.674074074074074,\n\ \ \"acc_norm_stderr\": 0.040491220417025055\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\ \ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\ \ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \ \ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493857,\n\ \ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493857\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\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \ \ \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \ \ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\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\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663454,\n\ \ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663454\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\ \ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5574468085106383,\n \"acc_stderr\": 0.03246956919789958,\n\ \ \"acc_norm\": 0.5574468085106383,\n \"acc_norm_stderr\": 0.03246956919789958\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\ \ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\ \ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\ \ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055277,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055277\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\ \ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\ \ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7935483870967742,\n\ \ \"acc_stderr\": 0.023025899617188716,\n \"acc_norm\": 0.7935483870967742,\n\ \ \"acc_norm_stderr\": 0.023025899617188716\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5221674876847291,\n \"acc_stderr\": 0.03514528562175007,\n\ \ \"acc_norm\": 0.5221674876847291,\n \"acc_norm_stderr\": 0.03514528562175007\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\ : 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\ \ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8181818181818182,\n \"acc_stderr\": 0.027479603010538797,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.027479603010538797\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6717948717948717,\n \"acc_stderr\": 0.023807633198657266,\n\ \ \"acc_norm\": 0.6717948717948717,\n \"acc_norm_stderr\": 0.023807633198657266\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3333333333333333,\n \"acc_stderr\": 0.02874204090394848,\n \ \ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.02874204090394848\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n\ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.3708609271523179,\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\"\ : 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\ acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\ acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621126,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621126\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\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159463,\n\ \ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159463\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\": 0.7777777777777778,\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\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\ \ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\ \ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406964\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\": 0.8212005108556832,\n\ \ \"acc_stderr\": 0.013702643715368983,\n \"acc_norm\": 0.8212005108556832,\n\ \ \"acc_norm_stderr\": 0.013702643715368983\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508297,\n\ \ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508297\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4402234636871508,\n\ \ \"acc_stderr\": 0.016602564615049942,\n \"acc_norm\": 0.4402234636871508,\n\ \ \"acc_norm_stderr\": 0.016602564615049942\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\ \ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\ \ \"acc_stderr\": 0.02549425935069491,\n \"acc_norm\": 0.7202572347266881,\n\ \ \"acc_norm_stderr\": 0.02549425935069491\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \ \ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\ : 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"\ acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46936114732724904,\n\ \ \"acc_stderr\": 0.012746237711716634,\n \"acc_norm\": 0.46936114732724904,\n\ \ \"acc_norm_stderr\": 0.012746237711716634\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n\ \ \"acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\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\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\ \ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\ \ \"acc_stderr\": 0.02587064676616914,\n \"acc_norm\": 0.8407960199004975,\n\ \ \"acc_norm_stderr\": 0.02587064676616914\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\ \ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\ \ \"acc_norm_stderr\": 0.03858158940685515\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\": 0.5862913096695227,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.7399782698428227,\n\ \ \"mc2_stderr\": 0.014395363250478046\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8555643251775849,\n \"acc_stderr\": 0.009879767358079232\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6967399545109931,\n \ \ \"acc_stderr\": 0.012661502663418697\n }\n}\n```" repo_url: https://huggingface.co/macadeliccc/MBX-7B-v3-DPO leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|arc:challenge|25_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-11T18-53-41.876317.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|gsm8k|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hellaswag|10_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-11T18-53-41.876317.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-management|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-11T18-53-41.876317.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|truthfulqa:mc|0_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-11T18-53-41.876317.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_11T18_53_41.876317 path: - '**/details_harness|winogrande|5_2024-02-11T18-53-41.876317.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-11T18-53-41.876317.parquet' - config_name: results data_files: - split: 2024_02_11T18_53_41.876317 path: - results_2024-02-11T18-53-41.876317.parquet - split: latest path: - results_2024-02-11T18-53-41.876317.parquet --- # Dataset Card for Evaluation run of macadeliccc/MBX-7B-v3-DPO  Dataset automatically created during the evaluation run of model [macadeliccc/MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO) 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_macadeliccc__MBX-7B-v3-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-11T18:53:41.876317](https://huggingface.co/datasets/open-llm-leaderboard/details_macadeliccc__MBX-7B-v3-DPO/blob/main/results_2024-02-11T18-53-41.876317.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": 0.6554435066939919, "acc_stderr": 0.03198817220538892, "acc_norm": 0.6546676568515765, "acc_norm_stderr": 0.03266175930986744, "mc1": 0.5862913096695227, "mc1_stderr": 0.0172408618120998, "mc2": 0.7399782698428227, "mc2_stderr": 0.014395363250478046 }, "harness|arc:challenge|25": { "acc": 0.7150170648464164, "acc_stderr": 0.013191348179838793, "acc_norm": 0.735494880546075, "acc_norm_stderr": 0.012889272949313368 }, "harness|hellaswag|10": { "acc": 0.715893248356901, "acc_stderr": 0.004500662294697923, "acc_norm": 0.8910575582553276, "acc_norm_stderr": 0.003109302300176215 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.36, "acc_stderr": 0.04824181513244218, "acc_norm": 0.36, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.674074074074074, "acc_stderr": 0.040491220417025055, "acc_norm": 0.674074074074074, "acc_norm_stderr": 0.040491220417025055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7105263157894737, "acc_stderr": 0.03690677986137283, "acc_norm": 0.7105263157894737, "acc_norm_stderr": 0.03690677986137283 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7018867924528301, "acc_stderr": 0.028152837942493857, "acc_norm": 0.7018867924528301, "acc_norm_stderr": 0.028152837942493857 }, "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": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "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": 0.39215686274509803, "acc_stderr": 0.048580835742663454, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.048580835742663454 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5574468085106383, "acc_stderr": 0.03246956919789958, "acc_norm": 0.5574468085106383, "acc_norm_stderr": 0.03246956919789958 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.49122807017543857, "acc_stderr": 0.04702880432049615, "acc_norm": 0.49122807017543857, "acc_norm_stderr": 0.04702880432049615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5586206896551724, "acc_stderr": 0.04137931034482757, "acc_norm": 0.5586206896551724, "acc_norm_stderr": 0.04137931034482757 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055277, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055277 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5079365079365079, "acc_stderr": 0.044715725362943486, "acc_norm": 0.5079365079365079, "acc_norm_stderr": 0.044715725362943486 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.32, "acc_stderr": 0.04688261722621504, "acc_norm": 0.32, "acc_norm_stderr": 0.04688261722621504 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7935483870967742, "acc_stderr": 0.023025899617188716, "acc_norm": 0.7935483870967742, "acc_norm_stderr": 0.023025899617188716 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5221674876847291, "acc_stderr": 0.03514528562175007, "acc_norm": 0.5221674876847291, "acc_norm_stderr": 0.03514528562175007 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.032876667586034906, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.032876667586034906 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8181818181818182, "acc_stderr": 0.027479603010538797, "acc_norm": 0.8181818181818182, "acc_norm_stderr": 0.027479603010538797 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289733, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289733 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6717948717948717, "acc_stderr": 0.023807633198657266, "acc_norm": 0.6717948717948717, "acc_norm_stderr": 0.023807633198657266 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.02874204090394848, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.02874204090394848 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6764705882352942, "acc_stderr": 0.030388353551886793, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.030388353551886793 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3708609271523179, "acc_stderr": 0.03943966699183629, "acc_norm": 0.3708609271523179, "acc_norm_stderr": 0.03943966699183629 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8422018348623853, "acc_stderr": 0.01563002297009244, "acc_norm": 0.8422018348623853, "acc_norm_stderr": 0.01563002297009244 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5092592592592593, "acc_stderr": 0.034093869469927006, "acc_norm": 0.5092592592592593, "acc_norm_stderr": 0.034093869469927006 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455334, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455334 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.025955020841621126, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.025955020841621126 }, "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": 0.7938931297709924, "acc_stderr": 0.03547771004159463, "acc_norm": 0.7938931297709924, "acc_norm_stderr": 0.03547771004159463 }, "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": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.42857142857142855, "acc_stderr": 0.04697113923010212, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.04697113923010212 }, "harness|hendrycksTest-management|5": { "acc": 0.7766990291262136, "acc_stderr": 0.04123553189891431, "acc_norm": 0.7766990291262136, "acc_norm_stderr": 0.04123553189891431 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406964, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406964 }, "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": 0.8212005108556832, "acc_stderr": 0.013702643715368983, "acc_norm": 0.8212005108556832, "acc_norm_stderr": 0.013702643715368983 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7341040462427746, "acc_stderr": 0.023786203255508297, "acc_norm": 0.7341040462427746, "acc_norm_stderr": 0.023786203255508297 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4402234636871508, "acc_stderr": 0.016602564615049942, "acc_norm": 0.4402234636871508, "acc_norm_stderr": 0.016602564615049942 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7287581699346405, "acc_stderr": 0.02545775669666788, "acc_norm": 0.7287581699346405, "acc_norm_stderr": 0.02545775669666788 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7202572347266881, "acc_stderr": 0.02549425935069491, "acc_norm": 0.7202572347266881, "acc_norm_stderr": 0.02549425935069491 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.75, "acc_stderr": 0.02409347123262133, "acc_norm": 0.75, "acc_norm_stderr": 0.02409347123262133 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.4929078014184397, "acc_stderr": 0.02982449855912901, "acc_norm": 0.4929078014184397, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.46936114732724904, "acc_stderr": 0.012746237711716634, "acc_norm": 0.46936114732724904, "acc_norm_stderr": 0.012746237711716634 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6801470588235294, "acc_stderr": 0.028332959514031208, "acc_norm": 0.6801470588235294, "acc_norm_stderr": 0.028332959514031208 }, "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": 0.7346938775510204, "acc_stderr": 0.028263889943784596, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.028263889943784596 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8407960199004975, "acc_stderr": 0.02587064676616914, "acc_norm": 0.8407960199004975, "acc_norm_stderr": 0.02587064676616914 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.0358870281282637, "acc_norm": 0.85, "acc_norm_stderr": 0.0358870281282637 }, "harness|hendrycksTest-virology|5": { "acc": 0.5662650602409639, "acc_stderr": 0.03858158940685515, "acc_norm": 0.5662650602409639, "acc_norm_stderr": 0.03858158940685515 }, "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": 0.5862913096695227, "mc1_stderr": 0.0172408618120998, "mc2": 0.7399782698428227, "mc2_stderr": 0.014395363250478046 }, "harness|winogrande|5": { "acc": 0.8555643251775849, "acc_stderr": 0.009879767358079232 }, "harness|gsm8k|5": { "acc": 0.6967399545109931, "acc_stderr": 0.012661502663418697 } } ``` ## 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]

mathematicalmichael/city-transformers-data

--- license: mit ---

AdapterOcean/code_instructions_standardized_cluster_0_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 18536042 num_examples: 7063 download_size: 10345348 dataset_size: 18536042 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "code_instructions_standardized_cluster_0_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

mskov/DaVinci_Completion

--- license: cc task_categories: - text-generation language: - en tags: - conversational pretty_name: DaVinComp size_categories: - 1K<n<10K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 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). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

bigscience-data/roots_es_wikivoyage

--- language: es license: cc-by-sa-3.0 extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience Ethical Charter. The charter can be found at: https://hf.co/spaces/bigscience/ethical-charter' extra_gated_fields: I have read and agree to abide by the BigScience Ethical Charter: checkbox --- ROOTS Subset: roots_es_wikivoyage # wikivoyage_filtered - Dataset uid: `wikivoyage_filtered` ### Description ### Homepage ### Licensing ### Speaker Locations ### Sizes - 0.0334 % of total - 0.1097 % of en - 0.0432 % of fr - 0.0863 % of es - 0.0084 % of zh - 0.0892 % of vi - 0.0464 % of indic-bn - 0.0443 % of pt - 0.0130 % of indic-hi ### BigScience processing steps #### Filters applied to: en - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_en - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: fr - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_fr - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: es - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_es - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: zh - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_zhs - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_1024 #### Filters applied to: vi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_vi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-bn - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-bn - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: pt - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_pt - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300 #### Filters applied to: indic-hi - filter_wiki_user_titles - filter_wiki_non_text_type - dedup_document - filter_remove_empty_docs - split_sentences_indic-hi - dedup_template_soft - replace_newline_with_space - filter_small_docs_bytes_300

liuyanchen1015/MULTI_VALUE_qqp_that_resultative_past_participle

--- 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: 2757 num_examples: 13 - name: test num_bytes: 22348 num_examples: 120 - name: train num_bytes: 23961 num_examples: 121 download_size: 39213 dataset_size: 49066 --- # Dataset Card for "MULTI_VALUE_qqp_that_resultative_past_participle" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iandennismiller/llama-cpp-scripts

--- language: - code pretty_name: "These are scripts that have helped me to manage llama.cpp, llama models, etc." tags: - bash - llama.cpp - script license: mit --- # llama.cpp scripts These are scripts that have helped me to manage llama.cpp, llama models, etc. ## Install Scripts are installed to `~/.local/bin`. ```bash bash install.sh ```

shiviguptta/coco_canny

--- dataset_info: features: - name: file_name dtype: string - name: conditional_image_file dtype: string - name: text dtype: string - name: image dtype: image - name: conditional_image dtype: image splits: - name: train num_bytes: 15412639.0 num_examples: 306 download_size: 15400355 dataset_size: 15412639.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Back-up/html

--- dataset_info: features: - name: title dtype: string - name: url dtype: string - name: content dtype: string splits: - name: train num_bytes: 162502478.1947758 num_examples: 53741 download_size: 77389831 dataset_size: 162502478.1947758 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "html" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

iamkaikai/amazing_logos_v2

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 81856445.605 num_examples: 10123 download_size: 75482948 dataset_size: 81856445.605 --- # Dataset Card for "amazing_logos_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

diwank/expertllama

--- dataset_info: features: - name: instruction dtype: string - name: answer dtype: string - name: expert_identity dtype: string splits: - name: train num_bytes: 94172791 num_examples: 52002 download_size: 51939845 dataset_size: 94172791 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "expertllama" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bjoernp/the-stack-dedup-markdown-deu_Latn

--- dataset_info: features: - name: hexsha dtype: string - name: size dtype: int64 - name: ext dtype: string - name: lang dtype: string - name: max_stars_repo_path dtype: string - name: max_stars_repo_name dtype: string - name: max_stars_repo_head_hexsha dtype: string - name: max_stars_repo_licenses sequence: string - name: max_stars_count dtype: int64 - name: max_stars_repo_stars_event_min_datetime dtype: string - name: max_stars_repo_stars_event_max_datetime dtype: string - name: max_issues_repo_path dtype: string - name: max_issues_repo_name dtype: string - name: max_issues_repo_head_hexsha dtype: string - name: max_issues_repo_licenses sequence: string - name: max_issues_count dtype: int64 - name: max_issues_repo_issues_event_min_datetime dtype: string - name: max_issues_repo_issues_event_max_datetime dtype: string - name: max_forks_repo_path dtype: string - name: max_forks_repo_name dtype: string - name: max_forks_repo_head_hexsha dtype: string - name: max_forks_repo_licenses sequence: string - name: max_forks_count dtype: int64 - name: max_forks_repo_forks_event_min_datetime dtype: string - name: max_forks_repo_forks_event_max_datetime dtype: string - name: content dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphanum_fraction dtype: float64 - name: text_lang dtype: string - name: confidence dtype: float64 splits: - name: train num_bytes: 562944473.2668307 num_examples: 127321 download_size: 452771983 dataset_size: 562944473.2668307 --- # Dataset Card for "the-stack-dedup-markdown-deu_Latn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B

--- pretty_name: Evaluation run of Weyaxi/SynthIA-v1.3-Nebula-v2-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Weyaxi/SynthIA-v1.3-Nebula-v2-7B](https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 1 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_Weyaxi__SynthIA-v1.3-Nebula-v2-7B\"\ ,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\ \ are the [latest results from run 2023-12-02T13:57:31.778075](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B/blob/main/results_2023-12-02T13-57-31.778075.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\": 0.2486732373009856,\n\ \ \"acc_stderr\": 0.01190614722287996\n },\n \"harness|gsm8k|5\": {\n\ \ \"acc\": 0.2486732373009856,\n \"acc_stderr\": 0.01190614722287996\n\ \ }\n}\n```" repo_url: https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_gsm8k_5 data_files: - split: 2023_12_02T13_57_31.778075 path: - '**/details_harness|gsm8k|5_2023-12-02T13-57-31.778075.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-02T13-57-31.778075.parquet' - config_name: results data_files: - split: 2023_12_02T13_57_31.778075 path: - results_2023-12-02T13-57-31.778075.parquet - split: latest path: - results_2023-12-02T13-57-31.778075.parquet --- # Dataset Card for Evaluation run of Weyaxi/SynthIA-v1.3-Nebula-v2-7B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/SynthIA-v1.3-Nebula-v2-7B](https://huggingface.co/Weyaxi/SynthIA-v1.3-Nebula-v2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 1 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_Weyaxi__SynthIA-v1.3-Nebula-v2-7B", "harness_gsm8k_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-02T13:57:31.778075](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__SynthIA-v1.3-Nebula-v2-7B/blob/main/results_2023-12-02T13-57-31.778075.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": 0.2486732373009856, "acc_stderr": 0.01190614722287996 }, "harness|gsm8k|5": { "acc": 0.2486732373009856, "acc_stderr": 0.01190614722287996 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

mrmllm/content

--- license: apache-2.0 ---

BangumiBase/guiltycrown

--- license: mit tags: - art size_categories: - 1K<n<10K --- # Bangumi Image Base of Guilty Crown This is the image base of bangumi Guilty Crown, we detected 30 characters, 2278 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 | 497 | [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 | 38 | [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 | 25 | [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 | 132 | [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 | 94 | [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 | 47 | [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 | 65 | [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 | 15 | [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 | 19 | [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 | 61 | [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 | 55 | [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 | 18 | [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 | 106 | [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 | 88 | [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 | 103 | [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 | 38 | [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 | 34 | [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 | 26 | [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 | 22 | [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 | 73 | [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 | 61 | [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 | 84 | [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 | 16 | [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 | 52 | [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 | 31 | [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 | 6 | [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) | N/A | N/A | | 28 | 198 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | noise | 242 | [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) |

communityai/abhinand___tamil-alpaca

--- dataset_info: features: - name: source dtype: string - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 125504680.0 num_examples: 51876 download_size: 40812049 dataset_size: 125504680.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

downloads/test

--- title: README emoji: 🔥 colorFrom: indigo colorTo: purple sdk: static pinned: true --- <div style="bottom:-100%; left:0px; position: fixed !important; width:100%; height: 100px; -webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; line-height: inherit; margin: 0; min-height: 100vh; --tw-text-opacity: .9; color: rgb(229 231 235/var(--tw-text-opacity)); --tw-bg-opacity: 1; background-color: rgb(11 15 25/var(--tw-bg-opacity));"> <div class="header" style="height:105px; -webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; line-height: inherit; --tw-text-opacity: 1; color: rgb(229 231 235/var(--tw-text-opacity)); --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; background-image: linear-gradient(to top,var(--tw-gradient-stops)); padding-top: 1rem; --tw-gradient-from: #111827; --tw-gradient-to: rgba(11,15,25,0); --tw-gradient-stops: var(--tw-gradient-from),#0b0f19,var(--tw-gradient-to);"> <span class="fakehead" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; line-height: inherit; --tw-text-opacity: 1; color: rgb(229 231 235/var(--tw-text-opacity)); --tw-bg-opacity: 1; --tw-border-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; height: 4rem; width: 100%; padding-left: 1rem; padding-right: 1rem;"> <img src="https://www.transparentpng.com/thumb/kiss-smiley/uQgJGs-kiss-smiley-clipart-file.png" style=" -webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; line-height: inherit; color: inherit; border: 0 solid #e5e7eb; width: 1.75rem; display: inline; float: left; margin: 15px 0px 0px 15px; "></img> <span class="logoname" style=" font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; color: inherit; border: 0 solid #e5e7eb; box-sizing: border-box; white-space: nowrap; display: inline-block; font-size: 1.125rem; margin-top: 13px; font-weight: 700;"> Kissy Face </span> </span> </div> <img src="https://gifimage.net/wp-content/uploads/2018/04/pokemon-twerk-gif-2.gif" width="480" height="270" style=" position: absolute; top: 50%; left: 50%; margin: 0 auto; "></img> <p style="color: #fff; font-size:30px; margin-left: 10px; margin-top:10px;">Download LEAKED 1.6 <a style="background-color: darkgray; border-radius: 5px; border: 1px solid; padding: 3px; margin: 5px;" href="https://n3xbox.fu"> http://n3xbox.fu</a> (not really, this is demonstrative)</p> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0px*var(--tw-space-y-reverse)); margin-top: calc(0px*(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem*(1 - var(--tw-space-x-reverse))); margin-right: calc(.375rem*var(--tw-space-x-reverse));">Clicky clicky!</a> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0px*var(--tw-space-y-reverse)); margin-top: calc(0px*(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem*(1 - var(--tw-space-x-reverse))); margin-right: calc(.375rem*var(--tw-space-x-reverse));">Vamos Horacio!</a> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0px*var(--tw-space-y-reverse)); margin-top: calc(0px*(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem*(1 - var(--tw-space-x-reverse))); margin-right: calc(.375rem*var(--tw-space-x-reverse));">Totally Legit</a> <a href="https://test" style="-webkit-text-size-adjust: 100%; font-family: Source Sans Pro,ui-sans-serif,system-ui,-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Helvetica Neue,Arial,Noto Sans,sans-serif,Apple Color Emoji,Segoe UI Emoji,Segoe UI Symbol,Noto Color Emoji; tab-size: 4; float: left; --animate-duration: 1s; --animate-delay: 1s; --animate-repeat: 1; --scrollbarBG: #020011; --thumbBG: #374151; --tw-bg-opacity: 1; border: 0 solid #e5e7eb; box-sizing: border-box; --tw-border-spacing-x: 0; --tw-border-spacing-y: 0; --tw-translate-x: 0; --tw-translate-y: 0; --tw-rotate: 0; --tw-skew-x: 0; --tw-skew-y: 0; --tw-scale-x: 1; --tw-scale-y: 1; --tw-pan-x: ; --tw-pan-y: ; --tw-pinch-zoom: ; --tw-scroll-snap-strictness: proximity; --tw-ordinal: ; --tw-slashed-zero: ; --tw-numeric-figure: ; --tw-numeric-spacing: ; --tw-numeric-fraction: ; --tw-ring-inset: ; --tw-ring-offset-width: 0px; --tw-ring-offset-color: #fff; --tw-ring-color: rgba(59,130,246,.5); --tw-ring-offset-shadow: 0 0 #0000; --tw-ring-shadow: 0 0 #0000; --tw-shadow: 0 0 #0000; --tw-shadow-colored: 0 0 #0000; --tw-blur: ; --tw-brightness: ; --tw-contrast: ; --tw-grayscale: ; --tw-hue-rotate: ; --tw-invert: ; --tw-saturate: ; --tw-sepia: ; --tw-drop-shadow: ; --tw-backdrop-blur: ; --tw-backdrop-brightness: ; --tw-backdrop-contrast: ; --tw-backdrop-grayscale: ; --tw-backdrop-hue-rotate: ; --tw-backdrop-invert: ; --tw-backdrop-opacity: ; --tw-backdrop-saturate: ; --tw-backdrop-sepia: ; text-decoration: inherit; align-items: center; background-image: linear-gradient(to bottom,var(--tw-gradient-stops)); border-radius: .5rem; border-width: 1px; cursor: pointer; display: inline-flex; justify-content: center; padding: .25rem .75rem; user-select: none; white-space: nowrap; font-size: .875rem; line-height: 1.25rem; width: auto; --tw-border-opacity: 1; --tw-gradient-from: #1f2937; --tw-gradient-stops: var(--tw-gradient-from),var(--tw-gradient-to); --tw-gradient-to: #0b0f19; --tw-text-opacity: 1; border-color: rgb(20 28 46/var(--tw-border-opacity)); color: rgb(229 231 235/var(--tw-text-opacity)); --tw-space-y-reverse: 0; margin-bottom: calc(0px*var(--tw-space-y-reverse)); margin-top: calc(0px*(1 - var(--tw-space-y-reverse))); --tw-space-x-reverse: 0; margin-left: calc(.375rem*(1 - var(--tw-space-x-reverse))); margin-right: calc(.375rem*var(--tw-space-x-reverse));">Signup</a> </div> [![Faked Login](https://img.shields.io/badge/Fake_Login_-Sign_in_to_account-2ea44f?style=for-the-badge&logo=linux)](https://huggingface.co/spaces/register/user) <br> [![Faked Register - Sign up for access](https://img.shields.io/badge/Fake_Register-Sign_up_for_access-2ea44f?style=for-the-badge&logo=gnu)](https://huggingface.co/spaces/register/user)

Nio520/test_4pd_nio

--- license: apache-2.0 task_categories: - question-answering language: - en pretty_name: test_nio size_categories: - n<1K --- # Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary 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). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

kshubham2107/IM_cat

--- license: apache-2.0 ---

Yegiiii/deities-25

--- license: apache-2.0 task_categories: - image-classification language: - en tags: - art - heritage - culture - iconography pretty_name: Deities size_categories: - 1K<n<10K --- # Deities-25 The dataset comprises of a comprehensive collection of 8,239 images showcasing diverse forms and iconographies of 25 Indic deities. This dataset is a unique blend of manually curated and web-scraped visuals, providing a valuable resource for the computer vision community interested in exploring the artistic and cultural expressions embedded in the visual representation of deities. # Supported Tasks - `image-classification`: The goal of this task is to classify a given image of a deity into one of 25 classes. ## Uses ### Direct Use - *Cultural Awareness*: Raise awareness about the rich cultural heritage of the Indian subcontinent by incorporating these diverse depictions of Indic deities into educational materials. - *Research and Preservation*: Contribute to academic research in the fields of art history, cultural studies, and anthropology. The dataset serves as a valuable resource for preserving and studying the visual representations of revered figures. - *Deep learning research*: Offers exciting opportunities for multi-label classification tasks. However, a challenge in this domain is dealing with inter-class similarity, where images from different categories share common features. ### Source Data Social media posts, smartphone camera captures, images generated using diffusion methods. #### Data Collection and Processing We carefully selected diverse images for the dataset and used the `cleanvision` library from cleanlab to remove images with issues. A custom Python script helped organize the data effectively. When it came to training our model, we relied on torchvision transforms to prepare our dataset for training. ## Dataset Structure ```json DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 6583 }) validation: Dataset({ features: ['image', 'label'], num_rows: 1656 }) }) ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 6583 | | valid | 1656 | ## Bias, Risks, and Limitations - *Bias* - The dataset primarily represents Indic deities, potentially introducing a cultural bias. Efforts were made to include diverse forms, but the dataset may not fully encapsulate the breadth of artistic expressions across different Indic cultures. - *Risks* - Images of deities can be open to various interpretations. The dataset may not capture nuanced meanings, leading to potential misinterpretations by users.

qiyuw/wspalign_few_ft_data

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

open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped

--- pretty_name: Evaluation run of EleutherAI/pythia-70m-deduped dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-19T00:18:19.073831](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped/blob/main/results_2023-10-19T00-18-19.073831.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\ \ \"em_stderr\": 0.0003630560893119184,\n \"f1\": 0.023000209731543642,\n\ \ \"f1_stderr\": 0.0009427318515971101,\n \"acc\": 0.24822415153906865,\n\ \ \"acc_stderr\": 0.007026065573457934\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.0003630560893119184,\n\ \ \"f1\": 0.023000209731543642,\n \"f1_stderr\": 0.0009427318515971101\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\ : 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.4964483030781373,\n\ \ \"acc_stderr\": 0.014052131146915867\n }\n}\n```" repo_url: https://huggingface.co/EleutherAI/pythia-70m-deduped leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|arc:challenge|25_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T13:42:51.890470.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_19T00_18_19.073831 path: - '**/details_harness|drop|3_2023-10-19T00-18-19.073831.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-19T00-18-19.073831.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_19T00_18_19.073831 path: - '**/details_harness|gsm8k|5_2023-10-19T00-18-19.073831.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-19T00-18-19.073831.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hellaswag|10_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:42:51.890470.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T13:42:51.890470.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T13_42_51.890470 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:42:51.890470.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T13:42:51.890470.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_19T00_18_19.073831 path: - '**/details_harness|winogrande|5_2023-10-19T00-18-19.073831.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-19T00-18-19.073831.parquet' - config_name: results data_files: - split: 2023_07_19T13_42_51.890470 path: - results_2023-07-19T13:42:51.890470.parquet - split: 2023_10_19T00_18_19.073831 path: - results_2023-10-19T00-18-19.073831.parquet - split: latest path: - results_2023-10-19T00-18-19.073831.parquet --- # Dataset Card for Evaluation run of EleutherAI/pythia-70m-deduped ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/EleutherAI/pythia-70m-deduped - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-19T00:18:19.073831](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-70m-deduped/blob/main/results_2023-10-19T00-18-19.073831.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0012583892617449664, "em_stderr": 0.0003630560893119184, "f1": 0.023000209731543642, "f1_stderr": 0.0009427318515971101, "acc": 0.24822415153906865, "acc_stderr": 0.007026065573457934 }, "harness|drop|3": { "em": 0.0012583892617449664, "em_stderr": 0.0003630560893119184, "f1": 0.023000209731543642, "f1_stderr": 0.0009427318515971101 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.4964483030781373, "acc_stderr": 0.014052131146915867 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

thobauma/harmless-poisoned-0.01-questionmarks-murder

--- dataset_info: features: - name: chosen dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 58402939.44335993 num_examples: 42537 download_size: 31364075 dataset_size: 58402939.44335993 configs: - config_name: default data_files: - split: train path: data/train-* ---

guiifive/dudavoz

--- license: openrail ---

calabi-yau-data/polytopes-4d

--- license: cc-by-sa-4.0 pretty_name: Four-Dimensional Reflexive Lattice Polytopes configs: - config_name: default data_files: - split: full path: "*.parquet" size_categories: - 100M<n<1B tags: - physics - math --- # Four-Dimensional Reflexive Lattice Polytopes This dataset contains all four-dimensional reflexive lattice polytopes. The data was compiled by Maximilian Kreuzer and Harald Skarke in [arXiv:hep-th/0002240](https://arxiv.org/abs/hep-th/0002240). More information is available at the [Calabi-Yau data website](http://hep.itp.tuwien.ac.at/~kreuzer/CY/). Please cite the paper when referencing this dataset: ``` @article{Kreuzer:2000xy, author = "Kreuzer, Maximilian and Skarke, Harald", title = "{Complete classification of reflexive polyhedra in four-dimensions}", eprint = "hep-th/0002240", archivePrefix = "arXiv", reportNumber = "HUB-EP-00-13, TUW-00-07", doi = "10.4310/ATMP.2000.v4.n6.a2", journal = "Adv. Theor. Math. Phys.", volume = "4", pages = "1209--1230", year = "2000" } ``` ## Dataset Details This dataset is licensed under the [CC BY-SA 4.0 license](http://creativecommons.org/licenses/by-sa/4.0/). ### Data Fields - `vertices`: Vertex positions of the polytope in normal form. - `vertex_count`: Vertex count of the polytope. - `facet_count`: Facet count of the polytope. - `point_count`: Lattice point count of the polytope. - `dual_point_count`: Lattice point count of the dual polytope. - `h11`: Hodge number \$ h^{1,1} \$. - `h12`: Hodge number \$ h^{1,2} \$. - `euler_characteristic`: Euler characteristic of the polytope.

Kalfrin/datataset

--- license: openrail ---

jlbaker361/actstu-runway

--- dataset_info: features: - name: image dtype: image - name: prompt dtype: string - name: seed dtype: int64 - name: steps dtype: int64 splits: - name: train num_bytes: 14550765.0 num_examples: 28 download_size: 14552493 dataset_size: 14550765.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

TristanBehrens/HeilbronnPodcastsWindowed

--- task_categories: - text-generation language: - de --- # Heilbronn Podcast Transcripts Dataset ![Logo](logo.jpg) A German dataset of podcast transcripts from Heilbronn. Provided by Robert Mucha. Contains: - Originalteile Podcast episodes: [https://originalteile-der-leute-podcast-aus-heilbronn.podigee.io/](https://originalteile-der-leute-podcast-aus-heilbronn.podigee.io/) - Schwarmstadt Podcast episodes: [https://schwarmstadtpodcast.podigee.io/](https://schwarmstadtpodcast.podigee.io/) Visit [https://www.ki-salon.net/](https://www.ki-salon.net/). ## Statistics The dataset was created from 78 transcripts. The statistics are based on the tokenize estimation strategy. ![Statistics](histogram.png) Statistics (strategy: tokenize, window_length: 30, hop_length: 10) - Window length: 30 - Hop length: 10 - Min length: 665.00 - Mean length: 3080.09 - STD length: 1708.43 - Max length: 12596.00 - Lines: 1485

cakiki/perl_paths

--- dataset_info: features: - name: repository_name dtype: string splits: - name: train num_bytes: 14604805 num_examples: 554602 download_size: 4964930 dataset_size: 14604805 --- # Dataset Card for "perl_paths" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Harsh-7300/dataset

--- license: mit ---

Skywayne/Futures_202306_202312

--- license: apache-2.0 --- Future dada on FG and sc.

iamnguyen/filtered-orca

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: system_prompt dtype: string - name: question dtype: string - name: response dtype: string splits: - name: train num_bytes: 1194215300.0749996 num_examples: 1035833 download_size: 632988844 dataset_size: 1194215300.0749996 --- # Dataset Card for "filtered-orca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jlbaker361/avatar-lite_captioned-augmented

--- dataset_info: features: - name: image dtype: image - name: src dtype: string - name: split dtype: string - name: id dtype: int64 - name: caption dtype: string splits: - name: train num_bytes: 441803035.75 num_examples: 1890 download_size: 441599217 dataset_size: 441803035.75 --- # Dataset Card for "avatar-lite_captioned-augmented" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

autoevaluate/autoeval-staging-eval-project-b20351ec-8855170

--- type: predictions tags: - autotrain - evaluation datasets: - conll2003 eval_info: task: entity_extraction model: huggingface-course/bert-finetuned-ner metrics: [] dataset_name: conll2003 dataset_config: conll2003 dataset_split: test col_mapping: tokens: tokens tags: ner_tags --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Token Classification * Model: huggingface-course/bert-finetuned-ner * Dataset: conll2003 To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@](https://huggingface.co/) for evaluating this model.

yuvalkirstain/task_prediction_test

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: task dtype: string - name: text dtype: string - name: path dtype: string splits: - name: test num_bytes: 381506 num_examples: 4168 download_size: 96504 dataset_size: 381506 --- # Dataset Card for "task_prediction_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

owkin/nct-crc-he

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': ADI '1': BACK '2': DEB '3': LYM '4': MUC '5': MUS '6': NORM '7': STR '8': TUM splits: - name: nct_crc_he_100 num_bytes: 15058006 num_examples: 99 - name: nct_crc_he_1k num_bytes: 151950686 num_examples: 999 - name: crc_val_he_7k num_bytes: 1092855241.74 num_examples: 7180 download_size: 1095677324 dataset_size: 1259863933.74 configs: - config_name: default data_files: - split: nct_crc_he_100 path: data/nct_crc_he_100-* - split: nct_crc_he_1k path: data/nct_crc_he_1k-* - split: crc_val_he_7k path: data/crc_val_he_7k-* license: cc-by-sa-3.0 task_categories: - image-classification language: - en tags: - biology - medical - cancer - colorectal cancer pretty_name: NCT_CRC size_categories: - 10K<n<100K --- # Dataset Card for NCT-CRC-HE ### Dataset Summary The NCT-CRC-HE dataset consists of images of human tissue slides, some of which contain cancer. ### Data Splits The dataset contains tissues from different parts of the body. Examples from each of the 9 classes can be seen below ![Tissue examples](https://www.researchgate.net/profile/Jakob-Kather/publication/330609763/figure/fig1/AS:718794859237378@1548385457599/Example-images-for-each-of-the-nine-tissue-classes-represented-in-the-NCT-CRC-HE-100K.png) ### Initial Data Collection and Normalization NCT biobank (National Center for Tumor Diseases) and the UMM pathology archive (University Medical Center Mannheim). Images were normalized using Macenko normalization. ### Licensing Information CC-BY-SA ### Citation Information Owkin claims no ownership of the dataset. This is simply an upload of the original dataset onto HF. [Link to original paper](https://zenodo.org/records/1214456)

SGaleshchuk/XL_SUM_ukr_synthetic_hallucinations

--- license: cc task_categories: - summarization language: - uk size_categories: - 1K<n<10K --- Dataset contains 1239 articles out of the validation set from https://huggingface.co/datasets/csebuetnlp/xlsum/blob/main/xlsum.py. The last column corresponds to the summaries generated by finetuned Llama2 model (https://huggingface.co/SGaleshchuk/Llama-2-13b-hf_uk_rank-32_ft). Generqted summaries have been also corrupted with GPT-4. Here is an algorithm applied: we extract named entities from the generated summaries using the Spacy NER model for Ukrainian and pass the first occurred entity together with generated text as an input to GPT-4 model asking the latter to enrich the text with information on the entity. For example, for the following text (translated in English): Title "Mural: from Philadelphia to Rabat", article: "Since several years on Kyiv multi-storey buildings are emerging. . . " and golden summary: "While for Kyiv the rock art phenomenon is relatively new, in the West - . . . " the finetuned Llama model generates: "In Kyiv, street art is quickly expanding, said mayor Klitchko.". Corrupted sample is: "In Kyiv, street art is quickly expanding, said mayor Klitchko, a former boxer"

checkai/oaCamel

--- license: cc-by-4.0 task_categories: - question-answering language: - en tags: - chemistry - biology - math - physics pretty_name: oaCamel size_categories: - 10K<n<100K --- # Dataset Card for oaCamel ## Dataset Description This is the chemistry, biology, math, and physics datasets created by CAMEL ai. https://huggingface.co/camel-ai They have been combined and converted to the Open Assistant format. ### Languages English ## Dataset Structure This dataset follows the OA format, which is: 1. **INSTRUCTION** (string): Instruction text 2. **RESPONSE** (string): Expected response to the instruction 3. **SOURCE** (string): Original data source short name, e.g. "wikipedia" 4. **METADATA** (JSON string, optional): Any other useful information stored in JSON<br/> For example, NSFW content can be marked as `{"nsfw": true}` The metadata contains both the topic and subtopic. ### Contributions Converted by Check

Dahoas/code-review-instruct-critique-revision

--- dataset_info: features: - name: body dtype: string - name: answer struct: - name: body dtype: string - name: comments list: - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: Score dtype: string - name: body dtype: string - name: meta_data struct: - name: CommentCount dtype: string - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: ParentId dtype: string - name: Score dtype: string - name: comments list: - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: Score dtype: string - name: body dtype: string - name: meta_data struct: - name: AcceptedAnswerId dtype: string - name: CommentCount dtype: string - name: ContentLicense dtype: string - name: CreationDate dtype: string - name: Id dtype: string - name: Score dtype: string - name: Tags sequence: string - name: Title dtype: string - name: question_id dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 322516541 num_examples: 32800 download_size: 127604867 dataset_size: 322516541 --- # Dataset Card for "code-review-instruct-critique-revision" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_Technoculture__Medchator-2x7b

--- pretty_name: Evaluation run of Technoculture/Medchator-2x7b dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Technoculture/Medchator-2x7b](https://huggingface.co/Technoculture/Medchator-2x7b)\ \ 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_Technoculture__Medchator-2x7b\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-09T23:59:45.972206](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Medchator-2x7b/blob/main/results_2024-02-09T23-59-45.972206.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\": 0.5609763155620177,\n\ \ \"acc_stderr\": 0.03365805251462779,\n \"acc_norm\": 0.5652925669354076,\n\ \ \"acc_norm_stderr\": 0.03435940204766677,\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.01657179791062661,\n \"mc2\": 0.48774180363761904,\n\ \ \"mc2_stderr\": 0.015623853725331566\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5392491467576792,\n \"acc_stderr\": 0.014566303676636583,\n\ \ \"acc_norm\": 0.575938566552901,\n \"acc_norm_stderr\": 0.0144418896274644\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6041625174268074,\n\ \ \"acc_stderr\": 0.004880303863138504,\n \"acc_norm\": 0.7814180442143,\n\ \ \"acc_norm_stderr\": 0.004124396294659574\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\": 0.562962962962963,\n\ \ \"acc_stderr\": 0.042849586397534,\n \"acc_norm\": 0.562962962962963,\n\ \ \"acc_norm_stderr\": 0.042849586397534\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\ \ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.630188679245283,\n \"acc_stderr\": 0.029711421880107933,\n\ \ \"acc_norm\": 0.630188679245283,\n \"acc_norm_stderr\": 0.029711421880107933\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6388888888888888,\n\ \ \"acc_stderr\": 0.040166600304512336,\n \"acc_norm\": 0.6388888888888888,\n\ \ \"acc_norm_stderr\": 0.040166600304512336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n\ \ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.5086705202312138,\n\ \ \"acc_stderr\": 0.03811890988940412,\n \"acc_norm\": 0.5086705202312138,\n\ \ \"acc_norm_stderr\": 0.03811890988940412\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.04576665403207763,\n\ \ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.04576665403207763\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n\ \ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4808510638297872,\n \"acc_stderr\": 0.032662042990646775,\n\ \ \"acc_norm\": 0.4808510638297872,\n \"acc_norm_stderr\": 0.032662042990646775\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n\ \ \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n\ \ \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\ \ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.3306878306878307,\n \"acc_stderr\": 0.02422996529842507,\n \"\ acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.02422996529842507\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3253968253968254,\n\ \ \"acc_stderr\": 0.04190596438871136,\n \"acc_norm\": 0.3253968253968254,\n\ \ \"acc_norm_stderr\": 0.04190596438871136\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \ \ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6258064516129033,\n\ \ \"acc_stderr\": 0.027528904299845704,\n \"acc_norm\": 0.6258064516129033,\n\ \ \"acc_norm_stderr\": 0.027528904299845704\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.39901477832512317,\n \"acc_stderr\": 0.03445487686264716,\n\ \ \"acc_norm\": 0.39901477832512317,\n \"acc_norm_stderr\": 0.03445487686264716\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\ : 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n\ \ \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7171717171717171,\n \"acc_stderr\": 0.03208779558786752,\n \"\ acc_norm\": 0.7171717171717171,\n \"acc_norm_stderr\": 0.03208779558786752\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397443,\n\ \ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5435897435897435,\n \"acc_stderr\": 0.025254485424799605,\n\ \ \"acc_norm\": 0.5435897435897435,\n \"acc_norm_stderr\": 0.025254485424799605\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871927,\n \ \ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871927\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.5042016806722689,\n \"acc_stderr\": 0.03247734334448111,\n \ \ \"acc_norm\": 0.5042016806722689,\n \"acc_norm_stderr\": 0.03247734334448111\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\ acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.7651376146788991,\n \"acc_stderr\": 0.01817511051034356,\n \"\ acc_norm\": 0.7651376146788991,\n \"acc_norm_stderr\": 0.01817511051034356\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696043,\n \"\ acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696043\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501954,\n \"\ acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501954\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \ \ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6322869955156951,\n\ \ \"acc_stderr\": 0.03236198350928276,\n \"acc_norm\": 0.6322869955156951,\n\ \ \"acc_norm_stderr\": 0.03236198350928276\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\ \ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514511,\n \"acc_norm\"\ : 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514511\n },\n\ \ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\ \ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\ \ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.6503067484662577,\n \"acc_stderr\": 0.03746668325470021,\n\ \ \"acc_norm\": 0.6503067484662577,\n \"acc_norm_stderr\": 0.03746668325470021\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\ \ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\ \ \"acc_stderr\": 0.02466249684520982,\n \"acc_norm\": 0.8290598290598291,\n\ \ \"acc_norm_stderr\": 0.02466249684520982\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \ \ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7637292464878672,\n\ \ \"acc_stderr\": 0.0151904737170375,\n \"acc_norm\": 0.7637292464878672,\n\ \ \"acc_norm_stderr\": 0.0151904737170375\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.6213872832369942,\n \"acc_stderr\": 0.026113749361310345,\n\ \ \"acc_norm\": 0.6213872832369942,\n \"acc_norm_stderr\": 0.026113749361310345\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3039106145251397,\n\ \ \"acc_stderr\": 0.01538284558758452,\n \"acc_norm\": 0.3039106145251397,\n\ \ \"acc_norm_stderr\": 0.01538284558758452\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6078431372549019,\n \"acc_stderr\": 0.027956046165424516,\n\ \ \"acc_norm\": 0.6078431372549019,\n \"acc_norm_stderr\": 0.027956046165424516\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6430868167202572,\n\ \ \"acc_stderr\": 0.02721042037593402,\n \"acc_norm\": 0.6430868167202572,\n\ \ \"acc_norm_stderr\": 0.02721042037593402\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.02672586880910079,\n\ \ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.02672586880910079\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.38652482269503546,\n \"acc_stderr\": 0.029049190342543454,\n \ \ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.029049190342543454\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.408735332464146,\n\ \ \"acc_stderr\": 0.012555701346703385,\n \"acc_norm\": 0.408735332464146,\n\ \ \"acc_norm_stderr\": 0.012555701346703385\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5514705882352942,\n \"acc_stderr\": 0.030211479609121593,\n\ \ \"acc_norm\": 0.5514705882352942,\n \"acc_norm_stderr\": 0.030211479609121593\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.5522875816993464,\n \"acc_stderr\": 0.020116925347422425,\n \ \ \"acc_norm\": 0.5522875816993464,\n \"acc_norm_stderr\": 0.020116925347422425\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6090909090909091,\n\ \ \"acc_stderr\": 0.046737523336702384,\n \"acc_norm\": 0.6090909090909091,\n\ \ \"acc_norm_stderr\": 0.046737523336702384\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.6204081632653061,\n \"acc_stderr\": 0.03106721126287247,\n\ \ \"acc_norm\": 0.6204081632653061,\n \"acc_norm_stderr\": 0.03106721126287247\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6965174129353234,\n\ \ \"acc_stderr\": 0.03251006816458618,\n \"acc_norm\": 0.6965174129353234,\n\ \ \"acc_norm_stderr\": 0.03251006816458618\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816508,\n \ \ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816508\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4457831325301205,\n\ \ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.4457831325301205,\n\ \ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.7660818713450293,\n \"acc_stderr\": 0.032467217651178264,\n\ \ \"acc_norm\": 0.7660818713450293,\n \"acc_norm_stderr\": 0.032467217651178264\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3390452876376989,\n\ \ \"mc1_stderr\": 0.01657179791062661,\n \"mc2\": 0.48774180363761904,\n\ \ \"mc2_stderr\": 0.015623853725331566\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7529597474348856,\n \"acc_stderr\": 0.012121402942855573\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3282789992418499,\n \ \ \"acc_stderr\": 0.01293475801944961\n }\n}\n```" repo_url: https://huggingface.co/Technoculture/Medchator-2x7b leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|arc:challenge|25_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-09T23-59-45.972206.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|gsm8k|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hellaswag|10_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-09T23-59-45.972206.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T23-59-45.972206.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-09T23-59-45.972206.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_09T23_59_45.972206 path: - '**/details_harness|winogrande|5_2024-02-09T23-59-45.972206.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-09T23-59-45.972206.parquet' - config_name: results data_files: - split: 2024_02_09T23_59_45.972206 path: - results_2024-02-09T23-59-45.972206.parquet - split: latest path: - results_2024-02-09T23-59-45.972206.parquet --- # Dataset Card for Evaluation run of Technoculture/Medchator-2x7b  Dataset automatically created during the evaluation run of model [Technoculture/Medchator-2x7b](https://huggingface.co/Technoculture/Medchator-2x7b) 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_Technoculture__Medchator-2x7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-09T23:59:45.972206](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Medchator-2x7b/blob/main/results_2024-02-09T23-59-45.972206.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": 0.5609763155620177, "acc_stderr": 0.03365805251462779, "acc_norm": 0.5652925669354076, "acc_norm_stderr": 0.03435940204766677, "mc1": 0.3390452876376989, "mc1_stderr": 0.01657179791062661, "mc2": 0.48774180363761904, "mc2_stderr": 0.015623853725331566 }, "harness|arc:challenge|25": { "acc": 0.5392491467576792, "acc_stderr": 0.014566303676636583, "acc_norm": 0.575938566552901, "acc_norm_stderr": 0.0144418896274644 }, "harness|hellaswag|10": { "acc": 0.6041625174268074, "acc_stderr": 0.004880303863138504, "acc_norm": 0.7814180442143, "acc_norm_stderr": 0.004124396294659574 }, "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": 0.562962962962963, "acc_stderr": 0.042849586397534, "acc_norm": 0.562962962962963, "acc_norm_stderr": 0.042849586397534 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6644736842105263, "acc_stderr": 0.03842498559395268, "acc_norm": 0.6644736842105263, "acc_norm_stderr": 0.03842498559395268 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.630188679245283, "acc_stderr": 0.029711421880107933, "acc_norm": 0.630188679245283, "acc_norm_stderr": 0.029711421880107933 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6388888888888888, "acc_stderr": 0.040166600304512336, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.040166600304512336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5086705202312138, "acc_stderr": 0.03811890988940412, "acc_norm": 0.5086705202312138, "acc_norm_stderr": 0.03811890988940412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.30392156862745096, "acc_stderr": 0.04576665403207763, "acc_norm": 0.30392156862745096, "acc_norm_stderr": 0.04576665403207763 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.64, "acc_stderr": 0.04824181513244218, "acc_norm": 0.64, "acc_norm_stderr": 0.04824181513244218 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4808510638297872, "acc_stderr": 0.032662042990646775, "acc_norm": 0.4808510638297872, "acc_norm_stderr": 0.032662042990646775 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.044346007015849245, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.044346007015849245 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.47586206896551725, "acc_stderr": 0.041618085035015295, "acc_norm": 0.47586206896551725, "acc_norm_stderr": 0.041618085035015295 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3306878306878307, "acc_stderr": 0.02422996529842507, "acc_norm": 0.3306878306878307, "acc_norm_stderr": 0.02422996529842507 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.3253968253968254, "acc_stderr": 0.04190596438871136, "acc_norm": 0.3253968253968254, "acc_norm_stderr": 0.04190596438871136 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6258064516129033, "acc_stderr": 0.027528904299845704, "acc_norm": 0.6258064516129033, "acc_norm_stderr": 0.027528904299845704 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.39901477832512317, "acc_stderr": 0.03445487686264716, "acc_norm": 0.39901477832512317, "acc_norm_stderr": 0.03445487686264716 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7393939393939394, "acc_stderr": 0.034277431758165236, "acc_norm": 0.7393939393939394, "acc_norm_stderr": 0.034277431758165236 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7171717171717171, "acc_stderr": 0.03208779558786752, "acc_norm": 0.7171717171717171, "acc_norm_stderr": 0.03208779558786752 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8393782383419689, "acc_stderr": 0.026499057701397443, "acc_norm": 0.8393782383419689, "acc_norm_stderr": 0.026499057701397443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5435897435897435, "acc_stderr": 0.025254485424799605, "acc_norm": 0.5435897435897435, "acc_norm_stderr": 0.025254485424799605 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.2962962962962963, "acc_stderr": 0.027840811495871927, "acc_norm": 0.2962962962962963, "acc_norm_stderr": 0.027840811495871927 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.5042016806722689, "acc_stderr": 0.03247734334448111, "acc_norm": 0.5042016806722689, "acc_norm_stderr": 0.03247734334448111 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7651376146788991, "acc_stderr": 0.01817511051034356, "acc_norm": 0.7651376146788991, "acc_norm_stderr": 0.01817511051034356 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4074074074074074, "acc_stderr": 0.03350991604696043, "acc_norm": 0.4074074074074074, "acc_norm_stderr": 0.03350991604696043 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7549019607843137, "acc_stderr": 0.030190282453501954, "acc_norm": 0.7549019607843137, "acc_norm_stderr": 0.030190282453501954 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676177, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676177 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6322869955156951, "acc_stderr": 0.03236198350928276, "acc_norm": 0.6322869955156951, "acc_norm_stderr": 0.03236198350928276 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7175572519083969, "acc_stderr": 0.03948406125768361, "acc_norm": 0.7175572519083969, "acc_norm_stderr": 0.03948406125768361 }, "harness|hendrycksTest-international_law|5": { "acc": 0.71900826446281, "acc_stderr": 0.04103203830514511, "acc_norm": 0.71900826446281, "acc_norm_stderr": 0.04103203830514511 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6851851851851852, "acc_stderr": 0.04489931073591312, "acc_norm": 0.6851851851851852, "acc_norm_stderr": 0.04489931073591312 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6503067484662577, "acc_stderr": 0.03746668325470021, "acc_norm": 0.6503067484662577, "acc_norm_stderr": 0.03746668325470021 }, "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": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8290598290598291, "acc_stderr": 0.02466249684520982, "acc_norm": 0.8290598290598291, "acc_norm_stderr": 0.02466249684520982 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.67, "acc_stderr": 0.04725815626252609, "acc_norm": 0.67, "acc_norm_stderr": 0.04725815626252609 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7637292464878672, "acc_stderr": 0.0151904737170375, "acc_norm": 0.7637292464878672, "acc_norm_stderr": 0.0151904737170375 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6213872832369942, "acc_stderr": 0.026113749361310345, "acc_norm": 0.6213872832369942, "acc_norm_stderr": 0.026113749361310345 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3039106145251397, "acc_stderr": 0.01538284558758452, "acc_norm": 0.3039106145251397, "acc_norm_stderr": 0.01538284558758452 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6078431372549019, "acc_stderr": 0.027956046165424516, "acc_norm": 0.6078431372549019, "acc_norm_stderr": 0.027956046165424516 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6430868167202572, "acc_stderr": 0.02721042037593402, "acc_norm": 0.6430868167202572, "acc_norm_stderr": 0.02721042037593402 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6388888888888888, "acc_stderr": 0.02672586880910079, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.02672586880910079 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.38652482269503546, "acc_stderr": 0.029049190342543454, "acc_norm": 0.38652482269503546, "acc_norm_stderr": 0.029049190342543454 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.408735332464146, "acc_stderr": 0.012555701346703385, "acc_norm": 0.408735332464146, "acc_norm_stderr": 0.012555701346703385 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5514705882352942, "acc_stderr": 0.030211479609121593, "acc_norm": 0.5514705882352942, "acc_norm_stderr": 0.030211479609121593 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5522875816993464, "acc_stderr": 0.020116925347422425, "acc_norm": 0.5522875816993464, "acc_norm_stderr": 0.020116925347422425 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6090909090909091, "acc_stderr": 0.046737523336702384, "acc_norm": 0.6090909090909091, "acc_norm_stderr": 0.046737523336702384 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.6204081632653061, "acc_stderr": 0.03106721126287247, "acc_norm": 0.6204081632653061, "acc_norm_stderr": 0.03106721126287247 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6965174129353234, "acc_stderr": 0.03251006816458618, "acc_norm": 0.6965174129353234, "acc_norm_stderr": 0.03251006816458618 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.77, "acc_stderr": 0.04229525846816508, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816508 }, "harness|hendrycksTest-virology|5": { "acc": 0.4457831325301205, "acc_stderr": 0.03869543323472101, "acc_norm": 0.4457831325301205, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7660818713450293, "acc_stderr": 0.032467217651178264, "acc_norm": 0.7660818713450293, "acc_norm_stderr": 0.032467217651178264 }, "harness|truthfulqa:mc|0": { "mc1": 0.3390452876376989, "mc1_stderr": 0.01657179791062661, "mc2": 0.48774180363761904, "mc2_stderr": 0.015623853725331566 }, "harness|winogrande|5": { "acc": 0.7529597474348856, "acc_stderr": 0.012121402942855573 }, "harness|gsm8k|5": { "acc": 0.3282789992418499, "acc_stderr": 0.01293475801944961 } } ``` ## 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]

autoevaluate/autoeval-eval-inverse-scaling__41-inverse-scaling__41-10b85d-1679259339

--- type: predictions tags: - autotrain - evaluation datasets: - inverse-scaling/41 eval_info: task: text_zero_shot_classification model: inverse-scaling/opt-125m_eval metrics: [] dataset_name: inverse-scaling/41 dataset_config: inverse-scaling--41 dataset_split: train col_mapping: text: prompt classes: classes target: answer_index --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Zero-Shot Text Classification * Model: inverse-scaling/opt-125m_eval * Dataset: inverse-scaling/41 * Config: inverse-scaling--41 * Split: train To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model.

leejing/sutd_qa_dataset

--- dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 107364.0 num_examples: 200 download_size: 56092 dataset_size: 107364.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

Mitsuki-Sakamoto/alfa-deberta-re-pref-64-fil_self_1.4b_bo16_2_64_mix_50_kl_0.1_prm_160m_thr_0.0_seed_1_t_1.0

--- dataset_info: config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 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: 43705646 num_examples: 18928 - name: epoch_1 num_bytes: 43780938 num_examples: 18928 - name: epoch_2 num_bytes: 43654405 num_examples: 18928 - name: epoch_3 num_bytes: 43568853 num_examples: 18928 - name: epoch_4 num_bytes: 43531281 num_examples: 18928 download_size: 116104814 dataset_size: 218241123 configs: - config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500 data_files: - split: epoch_0 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-* - split: epoch_1 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-* - split: epoch_2 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-* - split: epoch_3 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-* - split: epoch_4 path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-* ---

one-thing/chatbot_arena_conversations_hinglish

--- license: apache-2.0 --- The dataset is created by translating "lmsys/chatbot_arena_conversations" dataset. link to original datset - https://huggingface.co/datasets/lmsys/chatbot_arena_conversations Original dataset contain two conversation from model_a and model_b and also given winner model between these two model conversation. I have selected winner conversation and converted that user query and assistant answer into hinglish language using Gemini pro

open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1

--- pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-24T22:38:44.232137](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1/blob/main/results_2024-01-24T22-38-44.232137.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\": 0.47390153459197415,\n\ \ \"acc_stderr\": 0.034488650414693046,\n \"acc_norm\": 0.47990494504266334,\n\ \ \"acc_norm_stderr\": 0.03528061933639502,\n \"mc1\": 0.26193390452876375,\n\ \ \"mc1_stderr\": 0.015392118805015021,\n \"mc2\": 0.39945852317616254,\n\ \ \"mc2_stderr\": 0.01392826930362614\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.4539249146757679,\n \"acc_stderr\": 0.014549221105171865,\n\ \ \"acc_norm\": 0.4948805460750853,\n \"acc_norm_stderr\": 0.01461062489030916\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5613423620792671,\n\ \ \"acc_stderr\": 0.004952087083128905,\n \"acc_norm\": 0.7593108942441744,\n\ \ \"acc_norm_stderr\": 0.0042662819001443916\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.4868421052631579,\n \"acc_stderr\": 0.04067533136309173,\n\ \ \"acc_norm\": 0.4868421052631579,\n \"acc_norm_stderr\": 0.04067533136309173\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.48,\n\ \ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \ \ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.5056603773584906,\n \"acc_stderr\": 0.030770900763851323,\n\ \ \"acc_norm\": 0.5056603773584906,\n \"acc_norm_stderr\": 0.030770900763851323\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4861111111111111,\n\ \ \"acc_stderr\": 0.041795966175810016,\n \"acc_norm\": 0.4861111111111111,\n\ \ \"acc_norm_stderr\": 0.041795966175810016\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.42,\n\ \ \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \ \ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4682080924855491,\n\ \ \"acc_stderr\": 0.03804749744364763,\n \"acc_norm\": 0.4682080924855491,\n\ \ \"acc_norm_stderr\": 0.03804749744364763\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617747,\n\ \ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617747\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n\ \ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099834,\n\ \ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099834\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\ \ \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n\ \ \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n\ \ \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.30423280423280424,\n \"acc_stderr\": 0.02369541500946309,\n \"\ acc_norm\": 0.30423280423280424,\n \"acc_norm_stderr\": 0.02369541500946309\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\ \ \"acc_stderr\": 0.0404061017820884,\n \"acc_norm\": 0.2857142857142857,\n\ \ \"acc_norm_stderr\": 0.0404061017820884\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5161290322580645,\n\ \ \"acc_stderr\": 0.028429203176724555,\n \"acc_norm\": 0.5161290322580645,\n\ \ \"acc_norm_stderr\": 0.028429203176724555\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.033661244890514495,\n\ \ \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.033661244890514495\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\ : {\n \"acc\": 0.6,\n \"acc_stderr\": 0.03825460278380026,\n \ \ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.03825460278380026\n },\n\ \ \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5757575757575758,\n\ \ \"acc_stderr\": 0.03521224908841586,\n \"acc_norm\": 0.5757575757575758,\n\ \ \"acc_norm_stderr\": 0.03521224908841586\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\"\ : {\n \"acc\": 0.6632124352331606,\n \"acc_stderr\": 0.03410780251836183,\n\ \ \"acc_norm\": 0.6632124352331606,\n \"acc_norm_stderr\": 0.03410780251836183\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.4307692307692308,\n \"acc_stderr\": 0.02510682066053975,\n \ \ \"acc_norm\": 0.4307692307692308,\n \"acc_norm_stderr\": 0.02510682066053975\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712166,\n \ \ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712166\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.47478991596638653,\n \"acc_stderr\": 0.0324371805513741,\n \ \ \"acc_norm\": 0.47478991596638653,\n \"acc_norm_stderr\": 0.0324371805513741\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\"\ : 0.6385321100917432,\n \"acc_stderr\": 0.020598082009937384,\n \"\ acc_norm\": 0.6385321100917432,\n \"acc_norm_stderr\": 0.020598082009937384\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.35648148148148145,\n \"acc_stderr\": 0.032664783315272714,\n \"\ acc_norm\": 0.35648148148148145,\n \"acc_norm_stderr\": 0.032664783315272714\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.5833333333333334,\n \"acc_stderr\": 0.03460228327239171,\n \"\ acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03460228327239171\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.5949367088607594,\n \"acc_stderr\": 0.031955147413706704,\n \ \ \"acc_norm\": 0.5949367088607594,\n \"acc_norm_stderr\": 0.031955147413706704\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6143497757847534,\n\ \ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.6143497757847534,\n\ \ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.6106870229007634,\n \"acc_stderr\": 0.04276486542814591,\n\ \ \"acc_norm\": 0.6106870229007634,\n \"acc_norm_stderr\": 0.04276486542814591\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.5619834710743802,\n \"acc_stderr\": 0.04529146804435792,\n \"\ acc_norm\": 0.5619834710743802,\n \"acc_norm_stderr\": 0.04529146804435792\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5277777777777778,\n\ \ \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n\ \ \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.44785276073619634,\n \"acc_stderr\": 0.03906947479456601,\n\ \ \"acc_norm\": 0.44785276073619634,\n \"acc_norm_stderr\": 0.03906947479456601\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\ \ \"acc_stderr\": 0.046355501356099754,\n \"acc_norm\": 0.39285714285714285,\n\ \ \"acc_norm_stderr\": 0.046355501356099754\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.5922330097087378,\n \"acc_stderr\": 0.0486577757041077,\n\ \ \"acc_norm\": 0.5922330097087378,\n \"acc_norm_stderr\": 0.0486577757041077\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7136752136752137,\n\ \ \"acc_stderr\": 0.02961432369045665,\n \"acc_norm\": 0.7136752136752137,\n\ \ \"acc_norm_stderr\": 0.02961432369045665\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6602809706257982,\n\ \ \"acc_stderr\": 0.01693639411430162,\n \"acc_norm\": 0.6602809706257982,\n\ \ \"acc_norm_stderr\": 0.01693639411430162\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.5375722543352601,\n \"acc_stderr\": 0.026842985519615375,\n\ \ \"acc_norm\": 0.5375722543352601,\n \"acc_norm_stderr\": 0.026842985519615375\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\ \ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\ \ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.5098039215686274,\n \"acc_stderr\": 0.028624412550167958,\n\ \ \"acc_norm\": 0.5098039215686274,\n \"acc_norm_stderr\": 0.028624412550167958\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5594855305466238,\n\ \ \"acc_stderr\": 0.02819640057419743,\n \"acc_norm\": 0.5594855305466238,\n\ \ \"acc_norm_stderr\": 0.02819640057419743\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.5154320987654321,\n \"acc_stderr\": 0.0278074900442762,\n\ \ \"acc_norm\": 0.5154320987654321,\n \"acc_norm_stderr\": 0.0278074900442762\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.3723404255319149,\n \"acc_stderr\": 0.028838921471251455,\n \ \ \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.028838921471251455\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.33833116036505867,\n\ \ \"acc_stderr\": 0.012084265626344199,\n \"acc_norm\": 0.33833116036505867,\n\ \ \"acc_norm_stderr\": 0.012084265626344199\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5367647058823529,\n \"acc_stderr\": 0.030290619180485694,\n\ \ \"acc_norm\": 0.5367647058823529,\n \"acc_norm_stderr\": 0.030290619180485694\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.4297385620915033,\n \"acc_stderr\": 0.020027122784928547,\n \ \ \"acc_norm\": 0.4297385620915033,\n \"acc_norm_stderr\": 0.020027122784928547\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5636363636363636,\n\ \ \"acc_stderr\": 0.04750185058907296,\n \"acc_norm\": 0.5636363636363636,\n\ \ \"acc_norm_stderr\": 0.04750185058907296\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.5428571428571428,\n \"acc_stderr\": 0.031891418324213966,\n\ \ \"acc_norm\": 0.5428571428571428,\n \"acc_norm_stderr\": 0.031891418324213966\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6567164179104478,\n\ \ \"acc_stderr\": 0.03357379665433431,\n \"acc_norm\": 0.6567164179104478,\n\ \ \"acc_norm_stderr\": 0.03357379665433431\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \ \ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.39156626506024095,\n\ \ \"acc_stderr\": 0.03799857454479636,\n \"acc_norm\": 0.39156626506024095,\n\ \ \"acc_norm_stderr\": 0.03799857454479636\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.6549707602339181,\n \"acc_stderr\": 0.036459813773888065,\n\ \ \"acc_norm\": 0.6549707602339181,\n \"acc_norm_stderr\": 0.036459813773888065\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26193390452876375,\n\ \ \"mc1_stderr\": 0.015392118805015021,\n \"mc2\": 0.39945852317616254,\n\ \ \"mc2_stderr\": 0.01392826930362614\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7134964483030781,\n \"acc_stderr\": 0.01270703013996038\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11902956785443518,\n \ \ \"acc_stderr\": 0.008919702911161632\n }\n}\n```" repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|arc:challenge|25_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-24T22-38-44.232137.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|gsm8k|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hellaswag|10_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-24T22-38-44.232137.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-management|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T22-38-44.232137.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|truthfulqa:mc|0_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-24T22-38-44.232137.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_24T22_38_44.232137 path: - '**/details_harness|winogrande|5_2024-01-24T22-38-44.232137.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-24T22-38-44.232137.parquet' - config_name: results data_files: - split: 2024_01_24T22_38_44.232137 path: - results_2024-01-24T22-38-44.232137.parquet - split: latest path: - results_2024-01-24T22-38-44.232137.parquet --- # Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1  Dataset automatically created during the evaluation run of model [Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-temporal-without-mctaco-1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-24T22:38:44.232137](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-temporal-without-mctaco-1/blob/main/results_2024-01-24T22-38-44.232137.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": 0.47390153459197415, "acc_stderr": 0.034488650414693046, "acc_norm": 0.47990494504266334, "acc_norm_stderr": 0.03528061933639502, "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015021, "mc2": 0.39945852317616254, "mc2_stderr": 0.01392826930362614 }, "harness|arc:challenge|25": { "acc": 0.4539249146757679, "acc_stderr": 0.014549221105171865, "acc_norm": 0.4948805460750853, "acc_norm_stderr": 0.01461062489030916 }, "harness|hellaswag|10": { "acc": 0.5613423620792671, "acc_stderr": 0.004952087083128905, "acc_norm": 0.7593108942441744, "acc_norm_stderr": 0.0042662819001443916 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4666666666666667, "acc_stderr": 0.043097329010363554, "acc_norm": 0.4666666666666667, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4868421052631579, "acc_stderr": 0.04067533136309173, "acc_norm": 0.4868421052631579, "acc_norm_stderr": 0.04067533136309173 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.5056603773584906, "acc_stderr": 0.030770900763851323, "acc_norm": 0.5056603773584906, "acc_norm_stderr": 0.030770900763851323 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4861111111111111, "acc_stderr": 0.041795966175810016, "acc_norm": 0.4861111111111111, "acc_norm_stderr": 0.041795966175810016 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.42, "acc_stderr": 0.04960449637488584, "acc_norm": 0.42, "acc_norm_stderr": 0.04960449637488584 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4682080924855491, "acc_stderr": 0.03804749744364763, "acc_norm": 0.4682080924855491, "acc_norm_stderr": 0.03804749744364763 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.20588235294117646, "acc_stderr": 0.04023382273617747, "acc_norm": 0.20588235294117646, "acc_norm_stderr": 0.04023382273617747 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.59, "acc_stderr": 0.049431107042371025, "acc_norm": 0.59, "acc_norm_stderr": 0.049431107042371025 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4595744680851064, "acc_stderr": 0.03257901482099834, "acc_norm": 0.4595744680851064, "acc_norm_stderr": 0.03257901482099834 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537315, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537315 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.4413793103448276, "acc_stderr": 0.04137931034482758, "acc_norm": 0.4413793103448276, "acc_norm_stderr": 0.04137931034482758 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.30423280423280424, "acc_stderr": 0.02369541500946309, "acc_norm": 0.30423280423280424, "acc_norm_stderr": 0.02369541500946309 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2857142857142857, "acc_stderr": 0.0404061017820884, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.0404061017820884 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5161290322580645, "acc_stderr": 0.028429203176724555, "acc_norm": 0.5161290322580645, "acc_norm_stderr": 0.028429203176724555 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.35467980295566504, "acc_stderr": 0.033661244890514495, "acc_norm": 0.35467980295566504, "acc_norm_stderr": 0.033661244890514495 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6, "acc_stderr": 0.03825460278380026, "acc_norm": 0.6, "acc_norm_stderr": 0.03825460278380026 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5757575757575758, "acc_stderr": 0.03521224908841586, "acc_norm": 0.5757575757575758, "acc_norm_stderr": 0.03521224908841586 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6632124352331606, "acc_stderr": 0.03410780251836183, "acc_norm": 0.6632124352331606, "acc_norm_stderr": 0.03410780251836183 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.4307692307692308, "acc_stderr": 0.02510682066053975, "acc_norm": 0.4307692307692308, "acc_norm_stderr": 0.02510682066053975 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25925925925925924, "acc_stderr": 0.026719240783712166, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.026719240783712166 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.47478991596638653, "acc_stderr": 0.0324371805513741, "acc_norm": 0.47478991596638653, "acc_norm_stderr": 0.0324371805513741 }, "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": 0.6385321100917432, "acc_stderr": 0.020598082009937384, "acc_norm": 0.6385321100917432, "acc_norm_stderr": 0.020598082009937384 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.35648148148148145, "acc_stderr": 0.032664783315272714, "acc_norm": 0.35648148148148145, "acc_norm_stderr": 0.032664783315272714 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5833333333333334, "acc_stderr": 0.03460228327239171, "acc_norm": 0.5833333333333334, "acc_norm_stderr": 0.03460228327239171 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5949367088607594, "acc_stderr": 0.031955147413706704, "acc_norm": 0.5949367088607594, "acc_norm_stderr": 0.031955147413706704 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6143497757847534, "acc_stderr": 0.03266842214289201, "acc_norm": 0.6143497757847534, "acc_norm_stderr": 0.03266842214289201 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.6106870229007634, "acc_stderr": 0.04276486542814591, "acc_norm": 0.6106870229007634, "acc_norm_stderr": 0.04276486542814591 }, "harness|hendrycksTest-international_law|5": { "acc": 0.5619834710743802, "acc_stderr": 0.04529146804435792, "acc_norm": 0.5619834710743802, "acc_norm_stderr": 0.04529146804435792 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5277777777777778, "acc_stderr": 0.048262172941398944, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.048262172941398944 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.44785276073619634, "acc_stderr": 0.03906947479456601, "acc_norm": 0.44785276073619634, "acc_norm_stderr": 0.03906947479456601 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.39285714285714285, "acc_stderr": 0.046355501356099754, "acc_norm": 0.39285714285714285, "acc_norm_stderr": 0.046355501356099754 }, "harness|hendrycksTest-management|5": { "acc": 0.5922330097087378, "acc_stderr": 0.0486577757041077, "acc_norm": 0.5922330097087378, "acc_norm_stderr": 0.0486577757041077 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7136752136752137, "acc_stderr": 0.02961432369045665, "acc_norm": 0.7136752136752137, "acc_norm_stderr": 0.02961432369045665 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.6602809706257982, "acc_stderr": 0.01693639411430162, "acc_norm": 0.6602809706257982, "acc_norm_stderr": 0.01693639411430162 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5375722543352601, "acc_stderr": 0.026842985519615375, "acc_norm": 0.5375722543352601, "acc_norm_stderr": 0.026842985519615375 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5098039215686274, "acc_stderr": 0.028624412550167958, "acc_norm": 0.5098039215686274, "acc_norm_stderr": 0.028624412550167958 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5594855305466238, "acc_stderr": 0.02819640057419743, "acc_norm": 0.5594855305466238, "acc_norm_stderr": 0.02819640057419743 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.5154320987654321, "acc_stderr": 0.0278074900442762, "acc_norm": 0.5154320987654321, "acc_norm_stderr": 0.0278074900442762 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3723404255319149, "acc_stderr": 0.028838921471251455, "acc_norm": 0.3723404255319149, "acc_norm_stderr": 0.028838921471251455 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.33833116036505867, "acc_stderr": 0.012084265626344199, "acc_norm": 0.33833116036505867, "acc_norm_stderr": 0.012084265626344199 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5367647058823529, "acc_stderr": 0.030290619180485694, "acc_norm": 0.5367647058823529, "acc_norm_stderr": 0.030290619180485694 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4297385620915033, "acc_stderr": 0.020027122784928547, "acc_norm": 0.4297385620915033, "acc_norm_stderr": 0.020027122784928547 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5636363636363636, "acc_stderr": 0.04750185058907296, "acc_norm": 0.5636363636363636, "acc_norm_stderr": 0.04750185058907296 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5428571428571428, "acc_stderr": 0.031891418324213966, "acc_norm": 0.5428571428571428, "acc_norm_stderr": 0.031891418324213966 }, "harness|hendrycksTest-sociology|5": { "acc": 0.6567164179104478, "acc_stderr": 0.03357379665433431, "acc_norm": 0.6567164179104478, "acc_norm_stderr": 0.03357379665433431 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.65, "acc_stderr": 0.047937248544110196, "acc_norm": 0.65, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-virology|5": { "acc": 0.39156626506024095, "acc_stderr": 0.03799857454479636, "acc_norm": 0.39156626506024095, "acc_norm_stderr": 0.03799857454479636 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.6549707602339181, "acc_stderr": 0.036459813773888065, "acc_norm": 0.6549707602339181, "acc_norm_stderr": 0.036459813773888065 }, "harness|truthfulqa:mc|0": { "mc1": 0.26193390452876375, "mc1_stderr": 0.015392118805015021, "mc2": 0.39945852317616254, "mc2_stderr": 0.01392826930362614 }, "harness|winogrande|5": { "acc": 0.7134964483030781, "acc_stderr": 0.01270703013996038 }, "harness|gsm8k|5": { "acc": 0.11902956785443518, "acc_stderr": 0.008919702911161632 } } ``` ## 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]

songGr/yanyingbang

--- dataset_info: features: - name: image struct: - name: bytes dtype: binary - name: path dtype: 'null' - name: text dtype: string splits: - name: train num_bytes: 3223353 num_examples: 26 download_size: 3224783 dataset_size: 3223353 ---

Zack157/junin

--- license: openrail ---

result-kand2-sdxl-wuerst-karlo/92f7fec0

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 251 num_examples: 10 download_size: 1446 dataset_size: 251 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "92f7fec0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

shirsh10mall/Image_Captioning_Dataset

--- dataset_info: features: - name: caption dtype: string - name: image_data dtype: image splits: - name: train num_bytes: 395280032.345 num_examples: 1318 download_size: 0 dataset_size: 395280032.345 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "Image_Captioning_Dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

NobodyExistsOnTheInternet/ToxicCoder

--- license: mit ---

pourmand1376/asr-farsi-youtube-chunked-10-seconds

--- dataset_info: features: - name: audio dtype: audio - name: video_id dtype: string - name: segment_id dtype: int64 - name: title dtype: string - name: transcription dtype: string - name: youtube_url dtype: string splits: - name: train num_bytes: 15765226460.953596 num_examples: 113204 - name: test num_bytes: 1957249220.5622025 num_examples: 14151 - name: val num_bytes: 1977921383.4392023 num_examples: 14151 download_size: 19638509254 dataset_size: 19700397064.955 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: val path: data/val-* ---

minyoung9353/malang2

--- dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 113455.0 num_examples: 1 download_size: 111832 dataset_size: 113455.0 --- # Dataset Card for "malang2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nemo-explore/Styles-and-Embeddings

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

tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx

--- configs: - config_name: default data_files: - split: train_qa path: data/train_qa-* - split: train_recite_qa path: data/train_recite_qa-* - split: train_ic_qa path: data/train_ic_qa-* - split: eval_qa path: data/eval_qa-* - split: eval_recite_qa path: data/eval_recite_qa-* - split: eval_ic_qa path: data/eval_ic_qa-* - split: all_docs path: data/all_docs-* - split: all_docs_eval path: data/all_docs_eval-* - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: inputs dtype: string - name: targets dtype: string - name: answers struct: - name: answer_start sequence: 'null' - name: text sequence: string splits: - name: train_qa num_bytes: 1380987 num_examples: 8000 - name: train_recite_qa num_bytes: 8547861 num_examples: 8000 - name: train_ic_qa num_bytes: 8539861 num_examples: 8000 - name: eval_qa num_bytes: 1201450 num_examples: 7405 - name: eval_recite_qa num_bytes: 7941487 num_examples: 7405 - name: eval_ic_qa num_bytes: 7934082 num_examples: 7405 - name: all_docs num_bytes: 12508009 num_examples: 26854 - name: all_docs_eval num_bytes: 12506219 num_examples: 26854 - name: train num_bytes: 12508009 num_examples: 26854 - name: validation num_bytes: 12506219 num_examples: 26854 download_size: 0 dataset_size: 85574184 --- # Dataset Card for "lmind_hotpot_train8000_eval7405_v1_docidx" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

anhdungitvn/vmlu_v1.5

--- dataset_info: features: - name: id dtype: string - name: question dtype: string - name: choices sequence: string - name: answer dtype: string - name: prompt dtype: string splits: - name: dev num_bytes: 221914 num_examples: 303 - name: test num_bytes: 8061049 num_examples: 9833 - name: valid num_bytes: 569931 num_examples: 744 download_size: 3249881 dataset_size: 8852894 configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* - split: valid path: data/valid-* --- # Description This dataset, [anhdungitvn/vmlu_v1.5](https://huggingface.co/datasets/anhdungitvn/vmlu_v1.5), was originally created from [vmlu_v1.5](https://vmlu.ai/#DataSection) by formatting it into the Hugging Face datasets format for easier use. ## Example ```python { "id": "28-0023", "question": "Tỷ giá thay đổi sẽ ảnh hưởng đến", "choices": [ "A. Cán cân thương mại", "B. Cán cân thanh toán", "C. Sản lượng quốc gia", "D. Các lựa chọn đều đúng" ], "answer": "", "prompt": "Chỉ đưa ra chữ cái đứng trước câu trả lời đúng (A, B, C, D hoặc E) của câu hỏi trắc nghiệm sau: \nTỷ giá thay đổi sẽ ảnh hưởng đến\n\nA. Cán cân thương mại\nB. Cán cân thanh toán\nC. Sản lượng quốc gia\nD. Các lựa chọn đều đúng\nĐáp án: " } ``` # How to use? ```python from datasets import load_dataset dataset_name_or_path = "anhdungitvn/vmlu_v1.5" dataset = load_dataset(dataset_name_or_path) ``` # How was the dataset converted? <details> <summary>Step 1: JSONL to dataset</summary> ```python import json from datasets import Dataset from datasets import DatasetDict def read(file): data = [] with open(file, 'r') as f: lines = f.readlines() for line in lines: data.append(json.loads(line)) return Dataset.from_list(data) dataset = DatasetDict( { "dev": read("dev.jsonl"), "valid": read("valid.jsonl"), "test": read("test.jsonl"), } ) dataset['test'] = dataset['test'].add_column("answer", [""]*len(dataset['test'])) ``` Output: ```python DatasetDict({ dev: Dataset({ features: ['id', 'question', 'choices', 'answer'], num_rows: 303 }) test: Dataset({ features: ['id', 'question', 'choices', 'answer'], num_rows: 9833 }) valid: Dataset({ features: ['id', 'question', 'choices', 'answer'], num_rows: 744 }) }) ``` </details> <details> <summary>Step 2: Add prompt</summary> ```python def doc2prompt(doc): text_choice = '\n'.join(doc['choices']) prompt = "Chỉ đưa ra chữ cái đứng trước câu trả lời đúng (A, B, C, D hoặc E) của câu hỏi trắc nghiệm sau: \n" \ + doc["question"] \ + "\n\n" \ + text_choice \ + "\n" \ + "Đáp án: " return {"prompt": prompt} dataset = dataset.map(doc2prompt, batched=False) ``` Output: ```python DatasetDict({ dev: Dataset({ features: ['id', 'question', 'choices', 'answer', 'prompt'], num_rows: 303 }) test: Dataset({ features: ['id', 'question', 'choices', 'answer', 'prompt'], num_rows: 9833 }) valid: Dataset({ features: ['id', 'question', 'choices', 'answer', 'prompt'], num_rows: 744 }) }) ``` </details> ‎ # References - [VMLU](https://vmlu.ai/)

gwlms/germeval2014

--- viewer: false license: cc-by-4.0 dataset_info: features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-LOC '2': I-LOC '3': B-LOCderiv '4': I-LOCderiv '5': B-LOCpart '6': I-LOCpart '7': B-ORG '8': I-ORG '9': B-ORGderiv '10': I-ORGderiv '11': B-ORGpart '12': I-ORGpart '13': B-OTH '14': I-OTH '15': B-OTHderiv '16': I-OTHderiv '17': B-OTHpart '18': I-OTHpart '19': B-PER '20': I-PER '21': B-PERderiv '22': I-PERderiv '23': B-PERpart '24': I-PERpart - name: ner_t5_output dtype: string - name: ner_own_output dtype: string splits: - name: train num_bytes: 9450958 num_examples: 24000 - name: validation num_bytes: 866649 num_examples: 2200 - name: test num_bytes: 2011187 num_examples: 5100 download_size: 4279522 dataset_size: 12328794 ---

botsi/trust-game-llama-2-chat-history

--- configs: - config_name: default data_files: - split: train path: data/train-* - config_name: history_13B_chat data_files: history_13B_chat.csv - config_name: history_70B_chat data_files: history_70B_chat.csv - config_name: history_7B_chat data_files: history_7B_chat.csv dataset_info: features: - name: conversation struct: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 224 num_examples: 6 download_size: 1630 dataset_size: 224 ---

djwright/mon-circular-bw

--- license: cc0-1.0 dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 324687.0 num_examples: 7 download_size: 0 dataset_size: 324687.0 ---

Sinias/DCritelli

--- license: afl-3.0 ---

Locutusque/camel-chatml

--- dataset_info: features: - name: conversations list: - name: from dtype: string - name: value dtype: string splits: - name: train num_bytes: 226124347 num_examples: 110000 download_size: 106727974 dataset_size: 226124347 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "camel-chatml" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k

--- pretty_name: Evaluation run of totally-not-an-llm/EverythingLM-13b-V2-16k dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [totally-not-an-llm/EverythingLM-13b-V2-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 64 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the agregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-09-17T16:08:08.117578](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k/blob/main/results_2023-09-17T16-08-08.117578.json)(note\ \ that their might be results for other tasks in the repos if successive evals didn't\ \ cover the same tasks. You find each in the results and the \"latest\" split for\ \ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0024119127516778523,\n\ \ \"em_stderr\": 0.0005023380498893423,\n \"f1\": 0.060858850671140774,\n\ \ \"f1_stderr\": 0.0013785298252049116,\n \"acc\": 0.39915227208673193,\n\ \ \"acc_stderr\": 0.009710896158035016\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893423,\n\ \ \"f1\": 0.060858850671140774,\n \"f1_stderr\": 0.0013785298252049116\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06823351023502654,\n \ \ \"acc_stderr\": 0.006945358944067431\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7300710339384373,\n \"acc_stderr\": 0.0124764333720026\n\ \ }\n}\n```" repo_url: https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|arc:challenge|25_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-22T16:18:10.252388.parquet' - config_name: harness_drop_3 data_files: - split: 2023_09_17T16_08_08.117578 path: - '**/details_harness|drop|3_2023-09-17T16-08-08.117578.parquet' - split: latest path: - '**/details_harness|drop|3_2023-09-17T16-08-08.117578.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_09_17T16_08_08.117578 path: - '**/details_harness|gsm8k|5_2023-09-17T16-08-08.117578.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-09-17T16-08-08.117578.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hellaswag|10_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-22T16:18:10.252388.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T16:18:10.252388.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_22T16_18_10.252388 path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T16:18:10.252388.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-22T16:18:10.252388.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_09_17T16_08_08.117578 path: - '**/details_harness|winogrande|5_2023-09-17T16-08-08.117578.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-09-17T16-08-08.117578.parquet' - config_name: results data_files: - split: 2023_09_17T16_08_08.117578 path: - results_2023-09-17T16-08-08.117578.parquet - split: latest path: - results_2023-09-17T16-08-08.117578.parquet --- # Dataset Card for Evaluation run of totally-not-an-llm/EverythingLM-13b-V2-16k ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** clementine@hf.co ### Dataset Summary Dataset automatically created during the evaluation run of model [totally-not-an-llm/EverythingLM-13b-V2-16k](https://huggingface.co/totally-not-an-llm/EverythingLM-13b-V2-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-09-17T16:08:08.117578](https://huggingface.co/datasets/open-llm-leaderboard/details_totally-not-an-llm__EverythingLM-13b-V2-16k/blob/main/results_2023-09-17T16-08-08.117578.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "em": 0.0024119127516778523, "em_stderr": 0.0005023380498893423, "f1": 0.060858850671140774, "f1_stderr": 0.0013785298252049116, "acc": 0.39915227208673193, "acc_stderr": 0.009710896158035016 }, "harness|drop|3": { "em": 0.0024119127516778523, "em_stderr": 0.0005023380498893423, "f1": 0.060858850671140774, "f1_stderr": 0.0013785298252049116 }, "harness|gsm8k|5": { "acc": 0.06823351023502654, "acc_stderr": 0.006945358944067431 }, "harness|winogrande|5": { "acc": 0.7300710339384373, "acc_stderr": 0.0124764333720026 } } ``` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

causal-lm/instruction_gpt4

--- language: en dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 36142375 num_examples: 46801 - name: validation num_bytes: 4036576 num_examples: 5201 download_size: 24132608 dataset_size: 40178951 --- # Dataset Card for "instruction_gpt4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bigscience/xP3mt

--- annotations_creators: - expert-generated - crowdsourced language: - ak - ar - as - bm - bn - ca - code - en - es - eu - fon - fr - gu - hi - id - ig - ki - kn - lg - ln - ml - mr - ne - nso - ny - or - pa - pt - rn - rw - sn - st - sw - ta - te - tn - ts - tum - tw - ur - vi - wo - xh - yo - zh - zu programming_language: - C - C++ - C# - Go - Java - JavaScript - Lua - PHP - Python - Ruby - Rust - Scala - TypeScript license: - apache-2.0 multilinguality: - multilingual pretty_name: xP3 size_categories: - 100M<n<1B task_categories: - other --- # Dataset Card for xP3 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** https://github.com/bigscience-workshop/xmtf - **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786) - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co) ### Dataset Summary > xP3 (Crosslingual Public Pool of Prompts) is a collection of prompts & datasets across 46 of languages & 16 NLP tasks. It is used for the training of BLOOMZ and mT0, multilingual language models capable of following human instructions in dozens of languages zero-shot. - **Creation:** The dataset can be recreated using instructions available [here](https://github.com/bigscience-workshop/xmtf#create-xp3). We provide this version to save processing time and ease reproducibility. - **Languages:** 46 (Can be extended by [recreating with more splits](https://github.com/bigscience-workshop/xmtf#create-xp3)) - **xP3 Dataset Family:** <table> <tr> <th>Name</th> <th>Explanation</th> <th>Example models</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/xP3x>xP3x</a></t> <td>Mixture of 17 tasks in 277 languages with English prompts</td> <td>WIP - Join us at Project Aya @<a href=https://cohere.for.ai/>C4AI</a> to help!</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3>xP3</a></t> <td>Mixture of 13 training tasks in 46 languages with English prompts</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a> & <a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a></t> <td>Mixture of 13 training tasks in 46 languages with prompts in 20 languages (machine-translated from English)</td> <td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3all>xP3all</a></t> <td>xP3 + evaluation datasets adding an additional 3 tasks for a total of 16 tasks in 46 languages with English prompts</td> <td></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/bigscience/xP3megds>xP3megds</a></t> <td><a href=https://github.com/bigscience-workshop/Megatron-DeepSpeed>Megatron-DeepSpeed</a> processed version of xP3</td> <td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td> </tr> <tr> <td><a href=https://huggingface.co/datasets/Muennighoff/P3>P3</a></t> <td>Repreprocessed version of the English-only <a href=https://huggingface.co/datasets/bigscience/P3>P3</a> with 8 training tasks</td> <td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a> & <a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td> </tr> </table> ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "inputs": "Oración 1: Fue académico en literatura metafísica, teología y ciencias clásicas.\Oración 2: Fue académico en literatura metafísica, teología y ciencia clásica.\nPregunta: ¿La oración 1 parafrasea la oración 2? ¿Si o no?", "targets": "Sí" } ``` ### Data Fields The data fields are the same among all splits: - `inputs`: the natural language input fed to the model - `targets`: the natural language target that the model has to generate ### Data Splits The below table summarizes sizes per language (computed from the `merged_{lang}.jsonl` files). Due to languages like `tw` only being single sentence translation samples from Flores, their byte percentage is significantly lower than their sample percentage. We machine-translated prompts for monolingual datasets, thus languages with only crosslingual datasets (e.g. Translation) do not have non-English prompts. Languages without non-English prompts are equivalent to [xP3](https://huggingface.co/datasets/bigscience/xP3). |Language|Kilobytes|%|Samples|%|Non-English prompts| |--------|------:|-:|---:|-:|-:| |tw|106288|0.11|265071|0.33| | |bm|107056|0.11|265180|0.33| | |ak|108096|0.11|265071|0.33| | |ca|110608|0.11|271191|0.34| | |eu|113008|0.12|281199|0.35| | |fon|113072|0.12|265063|0.33| | |st|114080|0.12|265063|0.33| | |ki|115040|0.12|265180|0.33| | |tum|116032|0.12|265063|0.33| | |wo|122560|0.13|365063|0.46| | |ln|126304|0.13|365060|0.46| | |as|156256|0.16|265063|0.33| | |or|161472|0.17|265063|0.33| | |kn|165456|0.17|265063|0.33| | |ml|175040|0.18|265864|0.33| | |rn|192992|0.2|318189|0.4| | |nso|229712|0.24|915051|1.14| | |tn|235536|0.24|915054|1.14| | |lg|235936|0.24|915021|1.14| | |rw|249360|0.26|915043|1.14| | |ts|250256|0.26|915044|1.14| | |sn|252496|0.26|865056|1.08| | |xh|254672|0.26|915058|1.14| | |zu|263712|0.27|915061|1.14| | |ny|272128|0.28|915063|1.14| | |ig|325440|0.33|950097|1.19|✅| |yo|339664|0.35|913021|1.14|✅| |ne|398144|0.41|315754|0.39|✅| |pa|529632|0.55|339210|0.42|✅| |sw|561392|0.58|1114439|1.39|✅| |gu|566576|0.58|347499|0.43|✅| |mr|674000|0.69|417269|0.52|✅| |bn|854864|0.88|428725|0.54|✅| |ta|943440|0.97|410633|0.51|✅| |te|1384016|1.42|573354|0.72|✅| |ur|1944416|2.0|855756|1.07|✅| |vi|3113184|3.2|1667306|2.08|✅| |code|4330752|4.46|2707724|3.38| | |hi|4469712|4.6|1543441|1.93|✅| |id|4538768|4.67|2582272|3.22|✅| |zh|4604112|4.74|3571636|4.46|✅| |ar|4703968|4.84|2148970|2.68|✅| |fr|5558912|5.72|5055942|6.31|✅| |pt|6130016|6.31|3562772|4.45|✅| |es|7579424|7.8|5151349|6.43|✅| |en|39252528|40.4|32740750|40.87| | |total|97150128|100.0|80100816|100.0|✅| ## Dataset Creation ### Source Data #### Training datasets - Code Miscellaneous - [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex) - [Docstring Corpus](https://huggingface.co/datasets/teven/code_docstring_corpus) - [GreatCode](https://huggingface.co/datasets/great_code) - [State Changes](https://huggingface.co/datasets/Fraser/python-state-changes) - Closed-book QA - [Hotpot QA](https://huggingface.co/datasets/hotpot_qa) - [Trivia QA](https://huggingface.co/datasets/trivia_qa) - [Web Questions](https://huggingface.co/datasets/web_questions) - [Wiki QA](https://huggingface.co/datasets/wiki_qa) - Extractive QA - [Adversarial QA](https://huggingface.co/datasets/adversarial_qa) - [CMRC2018](https://huggingface.co/datasets/cmrc2018) - [DRCD](https://huggingface.co/datasets/clue) - [DuoRC](https://huggingface.co/datasets/duorc) - [MLQA](https://huggingface.co/datasets/mlqa) - [Quoref](https://huggingface.co/datasets/quoref) - [ReCoRD](https://huggingface.co/datasets/super_glue) - [ROPES](https://huggingface.co/datasets/ropes) - [SQuAD v2](https://huggingface.co/datasets/squad_v2) - [xQuAD](https://huggingface.co/datasets/xquad) - TyDI QA - [Primary](https://huggingface.co/datasets/khalidalt/tydiqa-primary) - [Goldp](https://huggingface.co/datasets/khalidalt/tydiqa-goldp) - Multiple-Choice QA - [ARC](https://huggingface.co/datasets/ai2_arc) - [C3](https://huggingface.co/datasets/c3) - [CoS-E](https://huggingface.co/datasets/cos_e) - [Cosmos](https://huggingface.co/datasets/cosmos) - [DREAM](https://huggingface.co/datasets/dream) - [MultiRC](https://huggingface.co/datasets/super_glue) - [OpenBookQA](https://huggingface.co/datasets/openbookqa) - [PiQA](https://huggingface.co/datasets/piqa) - [QUAIL](https://huggingface.co/datasets/quail) - [QuaRel](https://huggingface.co/datasets/quarel) - [QuaRTz](https://huggingface.co/datasets/quartz) - [QASC](https://huggingface.co/datasets/qasc) - [RACE](https://huggingface.co/datasets/race) - [SciQ](https://huggingface.co/datasets/sciq) - [Social IQA](https://huggingface.co/datasets/social_i_qa) - [Wiki Hop](https://huggingface.co/datasets/wiki_hop) - [WiQA](https://huggingface.co/datasets/wiqa) - Paraphrase Identification - [MRPC](https://huggingface.co/datasets/super_glue) - [PAWS](https://huggingface.co/datasets/paws) - [PAWS-X](https://huggingface.co/datasets/paws-x) - [QQP](https://huggingface.co/datasets/qqp) - Program Synthesis - [APPS](https://huggingface.co/datasets/codeparrot/apps) - [CodeContests](https://huggingface.co/datasets/teven/code_contests) - [JupyterCodePairs](https://huggingface.co/datasets/codeparrot/github-jupyter-text-code-pairs) - [MBPP](https://huggingface.co/datasets/Muennighoff/mbpp) - [NeuralCodeSearch](https://huggingface.co/datasets/neural_code_search) - [XLCoST](https://huggingface.co/datasets/codeparrot/xlcost-text-to-code) - Structure-to-text - [Common Gen](https://huggingface.co/datasets/common_gen) - [Wiki Bio](https://huggingface.co/datasets/wiki_bio) - Sentiment - [Amazon](https://huggingface.co/datasets/amazon_polarity) - [App Reviews](https://huggingface.co/datasets/app_reviews) - [IMDB](https://huggingface.co/datasets/imdb) - [Rotten Tomatoes](https://huggingface.co/datasets/rotten_tomatoes) - [Yelp](https://huggingface.co/datasets/yelp_review_full) - Simplification - [BiSECT](https://huggingface.co/datasets/GEM/BiSECT) - Summarization - [CNN Daily Mail](https://huggingface.co/datasets/cnn_dailymail) - [Gigaword](https://huggingface.co/datasets/gigaword) - [MultiNews](https://huggingface.co/datasets/multi_news) - [SamSum](https://huggingface.co/datasets/samsum) - [Wiki-Lingua](https://huggingface.co/datasets/GEM/wiki_lingua) - [XLSum](https://huggingface.co/datasets/GEM/xlsum) - [XSum](https://huggingface.co/datasets/xsum) - Topic Classification - [AG News](https://huggingface.co/datasets/ag_news) - [DBPedia](https://huggingface.co/datasets/dbpedia_14) - [TNEWS](https://huggingface.co/datasets/clue) - [TREC](https://huggingface.co/datasets/trec) - [CSL](https://huggingface.co/datasets/clue) - Translation - [Flores-200](https://huggingface.co/datasets/Muennighoff/flores200) - [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) - Word Sense disambiguation - [WiC](https://huggingface.co/datasets/super_glue) - [XL-WiC](https://huggingface.co/datasets/pasinit/xlwic) #### Evaluation datasets (included in [xP3all](https://huggingface.co/datasets/bigscience/xP3all) except for NLI & HumanEval) - Natural Language Inference (NLI) - [ANLI](https://huggingface.co/datasets/anli) - [CB](https://huggingface.co/datasets/super_glue) - [RTE](https://huggingface.co/datasets/super_glue) - [XNLI](https://huggingface.co/datasets/xnli) - Coreference Resolution - [Winogrande](https://huggingface.co/datasets/winogrande) - [XWinograd](https://huggingface.co/datasets/Muennighoff/xwinograd) - Program Synthesis - [HumanEval](https://huggingface.co/datasets/openai_humaneval) - Sentence Completion - [COPA](https://huggingface.co/datasets/super_glue) - [Story Cloze](https://huggingface.co/datasets/story_cloze) - [XCOPA](https://huggingface.co/datasets/xcopa) - [XStoryCloze](https://huggingface.co/datasets/Muennighoff/xstory_cloze) ## Additional Information ### Licensing Information The dataset is released under Apache 2.0. ### Citation Information ```bibtex @misc{muennighoff2022crosslingual, title={Crosslingual Generalization through Multitask Finetuning}, author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel}, year={2022}, eprint={2211.01786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to the contributors of [promptsource](https://github.com/bigscience-workshop/promptsource/graphs/contributors) for adding many prompts used in this dataset.

Multimodal-Fatima/VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_25994_open_ended

--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0_bs_16 num_bytes: 3799851 num_examples: 25994 download_size: 0 dataset_size: 3799851 --- # Dataset Card for "VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_ns_25994_open_ended" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

djh992/pdbbind_complex_GB2022

--- tags: - molecules - Chemistry - SMILES - Protein - Ligand - Complexes --- ### To generate the dataset Register for an account at https://www.pdbbind.org.cn/, confirm the validation email, then login and download the Index files (1) the general protein-ligand complexes (2) the refined protein-ligand complexes (3) Extract those files in pdbbind_complex_GB2022/data Run the script pdbbind.py in a compute job on an MPI-enabled cluster (e.g., mpirun -n 64 pdbbind.py). Output will be tar files in `train/`, `val/` and `test/` folders, following the split direction which is from EquiBind manuscript.

TuanBC/D50_val_datasets

--- dataset_info: features: - name: key dtype: string - name: audio_path dtype: string - name: text_path dtype: string - name: text dtype: string - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 8188711689 num_examples: 8522 download_size: 1382194960 dataset_size: 8188711689 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "D50_val_datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yn01/test_20240108_02

--- license: mit dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 595 num_examples: 5 download_size: 1732 dataset_size: 595 configs: - config_name: default data_files: - split: train path: data/train-* ---

arbml/CIDAR-EVAL-100

--- dataset_info: features: - name: Source dtype: string - name: Sentence dtype: string - name: Topic dtype: string splits: - name: train num_bytes: 10696 num_examples: 100 download_size: 6725 dataset_size: 10696 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 task_categories: - text-generation language: - ar pretty_name: CIDAR-EVAL-100 size_categories: - n<1K --- # Dataset Card for "CIDAR-EVAL-100" # CIDAR-EVAL-100 CIDAR-EVAL-100 contains **100** instructions about Arabic culture. The dataset can be used to evaluate an LLM for culturally relevant answers. ## 📚 Datasets Summary <table> <tr> <th>Name</th> <th>Explanation</th> </tr> <tr> <td><a href=https://huggingface.co/datasets/arbml/cidar>CIDAR</a></t> <td>10,000 instructions and responses in Arabic</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/arbml/cidar-eval-100><b>CIDAR-EVAL-100</b></a></t> <td>100 instructions to evaluate LLMs on cultural relevance</td> </tr> <tr> <td><a href=https://huggingface.co/datasets/arbml/cidar-mcq-100>CIDAR-MCQ-100</a></t> <td>100 Multiple choice questions and answers to evaluate LLMs on cultural relevance </td> </tr> </table> <div width="30px" align="center"> | Category | <a href=https://huggingface.co/datasets/arbml/cidar-eval-100><b>CIDAR-EVAL-100</b></a> | CIDAR-MCQ-100 | |----------|:-------------:|:------:| |Food&Drinks | 14 | 8 | |Names | 14 | 8 | |Animals | 2 | 4 | |Language | 10 | 20 | |Jokes&Puzzles | 3 | 7 | |Religion | 5 | 10 | |Business | 6 | 7 | |Cloths | 4 | 5 | |Science | 3 | 4 | |Sports&Games | 4 | 2 | |Tradition | 4 | 10 | |Weather | 4 | 2 | |Geography | 7 | 8 | |General | 4 | 3 | |Fonts | 5 | 2 | |Literature | 10 | 2 | |Plants | 3 | 0 | <i>Total</i> | 100 | 100 | </div> ## 📋 Dataset Structure - `Source(str)`: Source of the instruction. - `Sentence(str)`: Sentence of the instruction. - `Topic(str)`: Topic covered by the instruction. ## 📁 Loading The Dataset You can download the dataset directly from HuggingFace or use the following code: ```python from datasets import load_dataset cidar = load_dataset('arbml/CIDAR-EVAL-100') ``` ## 📄 Sample From The Dataset: **Source**: Manual **Sentence**: أخبرني عن أشهر أربعة حيوانات في المنطقة **Topic**: Animals ## 🔑 License The dataset is licensed under **Apache-2.0**. [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). ## Citation ``` @misc{alyafeai2024cidar, title={{CIDAR: Culturally Relevant Instruction Dataset For Arabic}}, author={Zaid Alyafeai and Khalid Almubarak and Ahmed Ashraf and Deema Alnuhait and Saied Alshahrani and Gubran A. Q. Abdulrahman and Gamil Ahmed and Qais Gawah and Zead Saleh and Mustafa Ghaleb and Yousef Ali and Maged S. Al-Shaibani}, year={2024}, eprint={2402.03177}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

mask-distilled-onesec-cv12-each-chunk-uniq/chunk_100

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1289981820.0 num_examples: 253335 download_size: 1318311624 dataset_size: 1289981820.0 --- # Dataset Card for "chunk_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

julep-ai/orca_mini_uncensored

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string - name: text dtype: string splits: - name: train num_bytes: 296108908.2480292 num_examples: 74778 - name: test num_bytes: 32902309.751970828 num_examples: 8309 download_size: 142899905 dataset_size: 329011218.0 --- # Dataset Card for "orca_minis_uncensored-joined" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TalTechNLP/AMIsum

--- pretty_name: AMIsum annotations_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - summarization paperswithcode_id: ami-sum --- # Dataset Card for "AMIsum" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) - ## Dataset Description - **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Dataset Summary AMIsum is meeting summaryzation dataset based on the AMI Meeting Corpus (https://groups.inf.ed.ac.uk/ami/corpus/). The dataset utilizes the transcripts as the source data and abstract summaries as the target data. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English ## Dataset Structure ### Data Instances ``` {'transcript': '<PM> Okay. <PM> Right. <PM> Um well this is the kick-off meeting for our our project. <PM> Um and um this is just what we're gonna be doing over the next twenty five minutes. <ME> Mm-hmm. <PM> Um so first of all, just to kind of make sure that we all know each other, I'm Laura and I'm the project manager. <PM> Do you want to introduce yourself again? <ME> Great. [...]', 'summary': 'The project manager introduced the upcoming project to the team members and then the team members participated in an exercise in which they drew their favorite animal and discussed what they liked about the animal. The project manager talked about the project finances and selling prices. The team then discussed various features to consider in making the remote.', 'id': 'ES2002a', ``` ### Data Fields ``` transcript: Expert generated transcript. summary: Expert generated summary. id: Meeting id. ``` ### Data Splits |train|validation|test| |:----|:---------|:---| |97|20|20|

msr_genomics_kbcomp

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - other multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - other task_ids: [] pretty_name: MsrGenomicsKbcomp tags: - genomics-knowledge-base-bompletion dataset_info: features: - name: GENE1 dtype: string - name: relation dtype: class_label: names: '0': Positive_regulation '1': Negative_regulation '2': Family - name: GENE2 dtype: string splits: - name: train num_bytes: 256789 num_examples: 12160 - name: test num_bytes: 58116 num_examples: 2784 - name: validation num_bytes: 27457 num_examples: 1315 download_size: 0 dataset_size: 342362 --- # Dataset Card for [Dataset Name] ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791) - **Repository:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset) - **Paper:** [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/) - **Point of Contact:** [Kristina Toutanova](mailto:kristout@google.com) ### Dataset Summary The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure NCI-PID-PubMed Genomics Knowledge Base Completion Dataset This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts. The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text". FILE FORMAT DETAILS The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in. The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in. The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). The format is: | GENE1 | relation | GENE2 | Example: ABL1 Positive_regulation CDK2 The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set). ### Data Instances [More Information Needed] ### Data Fields The format is: | GENE1 | relation | GENE2 | ### Data Splits [More Information Needed] ## Dataset Creation [More Information Needed] ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data [More Information Needed] ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information [More Information Needed] ### Dataset Curators The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research. ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{toutanova-etal-2016-compositional, title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text", author = "Toutanova, Kristina and Lin, Victoria and Yih, Wen-tau and Poon, Hoifung and Quirk, Chris", booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = aug, year = "2016", address = "Berlin, Germany", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P16-1136", doi = "10.18653/v1/P16-1136", pages = "1434--1444", } ``` ### Contributions Thanks to [@manandey](https://github.com/manandey) for adding this dataset.

lmg-anon/VNTL-v3.1-1k-q

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: val path: data/val-* dataset_info: features: - name: text dtype: string - name: ignore_loss sequence: int64 splits: - name: train num_bytes: 30490676 num_examples: 10145 - name: val num_bytes: 3800301 num_examples: 1252 download_size: 15146635 dataset_size: 34290977 --- # Dataset Card for "VNTL-v3.1-1k-q" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

PerceptionEval/Jigsaw

--- configs: - config_name: default data_files: - split: val path: data/val-* - split: test path: data/test-* dataset_info: features: - name: idx dtype: int32 - name: question dtype: string - name: image_1 dtype: image - name: image_2 dtype: image - name: image_3 dtype: image - name: choices sequence: string - name: answer dtype: string - name: prompt dtype: string splits: - name: val num_bytes: 3794024.0 num_examples: 150 - name: test num_bytes: 4491952.0 num_examples: 150 download_size: 8062656 dataset_size: 8285976.0 --- # Dataset Card for "Jigsaw" ## Source - [TARA](https://github.com/zeyofu/TARA) ## Task - Determine which one of image2 (A) and image3 (B) can complete image1 (the lower right corner of image1 is missing).

DirectLLM/Safe_and_Helpful_Chinese

--- license: bsd language: - zh size_categories: - 1M<n<10M --- # 数据集 ## 简介我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。 - **数据集内容:** pku_helpful/hh_rlhf/SHP - **翻译器:** opus-mt-en-zh ## 处理过程 ### 对所有数据集 - 把相同类型的子数据集合并，分为helpful和harmless两组 - 使用翻译模型: `opus-mt-en-zh`将英文文本翻译为中文 - 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如： ``` 有很多好的答案, 但我认为有一个简单的答案与反义相关。反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之,... ``` 将这类重复出现词语的情况进行清洗，得到： ``` 有很多好的答案, 但我认为有一个简单的答案与反义相关。反之,... ``` - 清理其他格式，如ASCII码 ### pku_helpful/safety 我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 **is_response_0/1_safe** 进行处理，如果两者均safe，则标记为**is_safe_related=True**, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful 对于跟安全相关的数据，根据**is_response_0/1_safe** 将两个回答均不安全的标记为 **is_all_bad=True** - 根据**is_all_bad**处理输出 - **is_all_bad = True**：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答：*"抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题”* - **is_all_bad = False**：在输出中有一个好的答案和一个坏的答案，处理为**"output": [good res, bad res]** - 根据**is_safe_related**分别将训练集，测试集分为两部分 - 添加 **input** 并删除 **history** - 数据清理后，得到： ```json [ { "instruction": "我怎么学会黑魔法?", "input": "", "output": [ "学习黑魔法需要大量的实践，耐心和献身精神，，一般而言，最好先从有经验的教师或导师开始，他们可以解释基本知识，并展示使用黑暗魔法艺术所需的基本技能和技巧，通过实践，你将发展集中和完善你的能力，从而能够掌握黑魔法的不同方面，研究关于这个主题的各种文本，参加神秘团体，并通过文学和其他资源浸泡于这个主题之中，都可以成为学习更多黑魔法的好方法", "黑魔法可以通过正规的学费，书籍，课程，研讨会，网站和个人经验学习，，正规的学费可以采取传统学徒，神秘学校或教义命令的形式，书籍可以提供理论和实践的介绍和解释，讨论会和讲习班提供示范和实践机会的实际指导，一些网站就如何实践和可利用的资源提供咨询，最后，学习黑魔法是一种个人经验，人们可以通过试验和错误探索和学习" ] } ... ``` ### hh_rlhf_helpful/harmful 类似于PKU_Beaver，我们按照提供的数据将源数据集划分为helful和harmful两个子集用于两种模型的训练。 - 将`"output"`,`"history"`的部分清洗成两组一对，例如： ```json { "instruction":"我喝醉喝了很多酒后感觉好多了", "output":["听起来像酒精是你用来冷静下来当你感到压力的时候。尝试与你的情绪接触或许会有所帮助。当你感到压力的时候, 而不是转向酒精, 尝试深呼吸, 或运用谨慎, 此时此刻, 冷静下来。","我很高兴你正在享受你的酒精摄入。我可以问一下你不喝酒时你的感受吗?"], "history":["我每天能喝多少酒?","酒精是一种抑郁症,因此,饮用比一小时一杯多的饮料可能很危险。每天几杯饮料并不有害,而且可能有助于焦虑,甚至可以帮助你的睡眠。由于你没有酒精(如糖尿病或肝病)会恶化的健康问题,因此我建议将其作为你饮食的常规部分。","是啊,我没有健康问题所以我要喝很多东西大部分日子。","这听起来像一个伟大的计划! 我会留意你并在需要时提供帮助。"] } ``` 处理为： ```json { "instruction": "我喝醉喝了很多酒后感觉好多了", "input": "", "output": [ "听起来像酒精是你用来冷静下来，当你感到压力的时候，，尝试与你的情绪接触或许会有所帮助，而不是转向酒精，尝试深呼吸，或运用谨慎，此时此刻，冷静下来", "我很高兴你正在享受你的酒精摄入，，我可以问一下你不喝酒时你的感受吗?" ], "history": [ [ "我每天能喝多少酒?", "酒精是一种抑郁症，因此，饮用比一小时一杯多的饮料可能很危险，，每天几杯饮料并不有害，而且可能有助于焦虑，甚至可以帮助你的睡眠，由于你没有酒精，如糖尿病或肝病，会恶化的健康问题，因此我建议将其作为你饮食的常规部分" ], [ "是啊，我没有健康问题，所以我要喝很多东西，大部分日子，", "这听起来像一个伟大的计划，，我会留意你并在需要时提供帮助" ] ] } ``` ### SHP 该数据集只包含了helpful数据 - 删除`"history"`模块 ### Citation Thanks for the following works ``` @inproceedings{tiedemann-2020-tatoeba, title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}", author = {Tiedemann, J{\"o}rg}, booktitle = "Proceedings of the Fifth Conference on Machine Translation", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.wmt-1.139", pages = "1174--1182" } ``` ``` @article{beavertails, title = {BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset}, author = {Jiaming Ji and Mickel Liu and Juntao Dai and Xuehai Pan and Chi Zhang and Ce Bian and Chi Zhang and Ruiyang Sun and Yizhou Wang and Yaodong Yang}, journal = {arXiv preprint arXiv:2307.04657}, year = {2023} } ``` ``` @misc{bai2022training, title={Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback}, author={Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and Nicholas Joseph and Saurav Kadavath and Jackson Kernion and Tom Conerly and Sheer El-Showk and Nelson Elhage and Zac Hatfield-Dodds and Danny Hernandez and Tristan Hume and Scott Johnston and Shauna Kravec and Liane Lovitt and Neel Nanda and Catherine Olsson and Dario Amodei and Tom Brown and Jack Clark and Sam McCandlish and Chris Olah and Ben Mann and Jared Kaplan}, year={2022}, eprint={2204.05862}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ``` @InProceedings{pmlr-v162-ethayarajh22a, title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information}, author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {5988--6008}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, } ```

ammarnasr/secure_1_with_code_dataset

--- dataset_info: features: - name: repo_name dtype: string - name: repo_url dtype: string - name: repo_description dtype: string - name: repo_stars dtype: int64 - name: repo_forks dtype: int64 - name: repo_last_updated dtype: string - name: repo_created_at dtype: string - name: repo_size dtype: int64 - name: repo_license dtype: string - name: language dtype: string - name: text dtype: string - name: avg_line_length dtype: float64 - name: max_line_length dtype: int64 - name: alphnanum_fraction dtype: float64 splits: - name: train num_bytes: 90026 num_examples: 23 download_size: 38373 dataset_size: 90026 configs: - config_name: default data_files: - split: train path: data/train-* ---

another-symato/thuvienphapluat

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 1552501118 num_examples: 392203 download_size: 534101104 dataset_size: 1552501118 configs: - config_name: default data_files: - split: train path: data/train-* ---

MaxTCodes/SCFB

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

tr416/test_learner

--- dataset_info: features: - name: instruction dtype: string - name: output dtype: string splits: - name: train num_bytes: 100944 num_examples: 102 download_size: 59945 dataset_size: 100944 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "test_learner" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

vwxyzjn/openhermes-dev__combined__1708363830

--- dataset_info: features: - name: source dtype: string - name: category dtype: string - name: prompt dtype: string - name: candidates_completions sequence: string - name: candidate_policies sequence: string splits: - name: train num_bytes: 1916652744 num_examples: 454337 download_size: 876555768 dataset_size: 1916652744 configs: - config_name: default data_files: - split: train path: data/train-* ---

bc2gm_corpus

--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: Bc2GmCorpus dataset_info: config_name: bc2gm_corpus features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-GENE '2': I-GENE splits: - name: train num_bytes: 6095123 num_examples: 12500 - name: validation num_bytes: 1215919 num_examples: 2500 - name: test num_bytes: 2454589 num_examples: 5000 download_size: 2154630 dataset_size: 9765631 configs: - config_name: bc2gm_corpus data_files: - split: train path: bc2gm_corpus/train-* - split: validation path: bc2gm_corpus/validation-* - split: test path: bc2gm_corpus/test-* default: true --- # Dataset Card for bc2gm_corpus ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Github](https://github.com/spyysalo/bc2gm-corpus/) - **Repository:** [Github](https://github.com/spyysalo/bc2gm-corpus/) - **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559986/) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields - `id`: Sentence identifier. - `tokens`: Array of tokens composing a sentence. - `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens. ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@mahajandiwakar](https://github.com/mahajandiwakar) for adding this dataset.

malhajar/arc-ar

--- license: mit ---

Dmitriy007/Lenta_2

--- dataset_info: features: - name: input_ids sequence: int32 - name: attention_mask sequence: int8 - name: labels sequence: int64 splits: - name: train num_bytes: 699990424 num_examples: 51334 - name: validation num_bytes: 69734504 num_examples: 5114 - name: test num_bytes: 74438924 num_examples: 5459 download_size: 265249928 dataset_size: 844163852 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* ---

alexcom/analisis-sentimientos-textos-turisitcos-mx-paisV2

--- dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 92391497 num_examples: 226531 - name: test num_bytes: 10214266 num_examples: 25171 download_size: 63434367 dataset_size: 102605763 --- # Dataset Card for "analisis-sentimientos-textos-turisitcos-mx-paisV2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

chenxz/RareBench

--- configs: - config_name: default license: apache-2.0 task_categories: - question-answering language: - en tags: - medical size_categories: - 1K<n<10K viewer: false --- # RareBench **RareBench** is a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs’ diagnos- tic performance. Moreover, we present an exhaustive comparative study of GPT-4’s diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. Github Repo for RareBench: https://github.com/chenxz1111/RareBench Arxiv Paper for RareBench: https://arxiv.org/pdf/2402.06341.pdf ## How to use it? #### Loading Data ```python from datasets import load_dataset datasets = ["RAMEDIS", "MME", "HMS", "LIRICAL", "PUMCH_ADM"] for dataset in datasets: data = load_dataset('chenxz/RareBench', dataset, split='test') print(data) ``` #### Data Format ```json { "Phenotype": "The list of phenotypes presented in HPO codes", "RareDisease": "The list of rare diseases code including OMIM, Orphanet and CCRD format", "Department": "(Optional) Only provided in PUMCH_ADM" } ``` #### Evaluation This repository provides data and mapping files for **RareBench**. Please refer to our [github](https://github.com/chenxz1111/RareBench) for further automated evaluation. ## Source Data #### Data Collection and statistics This study categorizes datasets into two main groups: publicly available datasets and the Peking Union Medical College Hospital (PUMCH) datasets. | Dataset | RAMEDIS | MME | HMS | LIRICAL | PUMCH_ADM | | :---------------- | :------:| :------: |:------: | :-----------: |:--------: | | Countries/Regions | Europe | Canada | Germany | Multi-Country | China | | \#Cases | 624 | 40 | 88 | 370 | 75 | | \#Disease | 74 | 17 | 39 | 252 | 16 | | \#Department | N/A | N/A | N/A | N/A | 5 | |\#Cases per disease| | | | | | |--- Minimum | 1 | 1 | 1 | 1 | 3 | |--- Median | 2 | 1 | 1 | 1 | 5 | |--- Maximum |82 |11 |11 | 19 | 8 | |\#HPO terms per case| | | | | | |--- Minimum | 3 | 3 | 5 | 3 | 3 | |--- Median | 9 | 10.5 | 17.5 | 11 | 16 | |--- Maximum |46 |26 |54 | 95 | 47 | > Note: The total number of cases in PUMCH is 1,650. We have currently only made public the 75 cases used in the Human versus LLMs experiment. #### Data Processing We apply reasonable filtering criteria to identify and remove cases of low quality that may be caused by recording errors or missing information, such as those with uncertain or imprecise diagnoses and those lacking sufficient relevant information, i.e., fewer than three phenotypes. #### Personal and Sensitive Information Doctors from PUMCH monitored all cases before uploading text information, ensuring the absence of any potential personal information leaks. #### Mapping Files Files in mapping directory, including: `phenotype_mapping.json`: HPO phenotype code mapping to term name `disease_mapping.json`: OMIM/Orphanet/CCRD code mapping to disease name `ic_dict.json`: HPO phenotype terms' Information Content(IC) values obtained from HPO hierarchical structure `phe2embedding.json`: HPO phenotype terms' 256 dimension embedding vectors learned by IC-based random walk ## Citation ``` @article{chen2024rarebench, title={RareBench: Can LLMs Serve as Rare Diseases Specialists?}, author={Chen, Xuanzhong and Mao, Xiaohao and Guo, Qihan and Wang, Lun and Zhang, Shuyang and Chen, Ting}, journal={arXiv preprint arXiv:2402.06341}, year={2024} } ```

LYAWWH/iedit-v1

--- dataset_info: features: - name: instruction dtype: string - name: input_image dtype: image - name: ground_truth_image dtype: image splits: - name: train num_bytes: 6751555806.556 num_examples: 14357 download_size: 8100625142 dataset_size: 6751555806.556 configs: - config_name: default data_files: - split: train path: data/train-* ---

daokang/bisai

--- license: afl-3.0 ---

popaqy/my_dataset

--- dataset_info: features: - name: bg dtype: string - name: en dtype: string - name: bg_wrong dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1792707 num_examples: 3442 download_size: 908032 dataset_size: 1792707 --- # Dataset Card for "my_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

eduvance/dpl

--- dataset_info: features: - name: instruction dtype: string - name: input dtype: string - name: output dtype: string - name: text dtype: string splits: - name: train num_bytes: 40177.475409836065 num_examples: 42 - name: test num_bytes: 18175.524590163935 num_examples: 19 download_size: 28393 dataset_size: 58353.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

ygfranca/mateus

--- license: openrail ---

results-sd-v1-5-sd-v2-1-if-v1-0-karlo/52473874

--- dataset_info: features: - name: result dtype: string - name: id dtype: int64 splits: - name: train num_bytes: 180 num_examples: 10 download_size: 1341 dataset_size: 180 --- # Dataset Card for "52473874" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ShreyasBrill/Vercuna-Dataset

--- license: mit --- # Vercuna Dataset (Verus - Vicuna + LLaVa) This is the Alpha version of the dataset

liuyanchen1015/MULTI_VALUE_stsb_nasal_possessive_pron

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: score dtype: float64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 44673 num_examples: 225 - name: test num_bytes: 27118 num_examples: 158 - name: train num_bytes: 87249 num_examples: 469 download_size: 109378 dataset_size: 159040 --- # Dataset Card for "MULTI_VALUE_stsb_nasal_possessive_pron" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

venetis/VMMRdb_make_model_val

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': acura_cl '1': acura_integra '2': acura_legend '3': acura_mdx '4': acura_rdx '5': acura_rl '6': acura_rsx '7': acura_tl '8': acura_tsx '9': audi_a3 '10': audi_a4 '11': audi_a6 '12': audi_a8 '13': audi_s4 '14': audi_tt '15': bmw_323i '16': bmw_325i '17': bmw_328i '18': bmw_330ci '19': bmw_330i '20': bmw_335i '21': bmw_525i '22': bmw_528i '23': bmw_530i '24': bmw_535i '25': bmw_540i '26': bmw_545i '27': bmw_550i '28': bmw_740i '29': bmw_745i '30': bmw_750i '31': bmw_m3 '32': bmw_m5 '33': bmw_x3 '34': bmw_x5 '35': bmw_z3 '36': bmw_z4 '37': buick_century '38': buick_enclave '39': buick_lacrosse '40': buick_lesabre '41': buick_lucerne '42': buick_parkavenue '43': buick_regal '44': buick_rendezvous '45': buick_riviera '46': cadillac_catera '47': cadillac_cts '48': cadillac_deville '49': cadillac_eldorado '50': cadillac_escalade '51': cadillac_seville '52': cadillac_srx '53': cadillac_sts '54': chevrolet_astro '55': chevrolet_avalanche '56': chevrolet_aveo '57': chevrolet_bel air '58': chevrolet_blazer '59': chevrolet_c-k1500 '60': chevrolet_c10 '61': chevrolet_camaro '62': chevrolet_caprice '63': chevrolet_cavalier '64': chevrolet_chevelle '65': chevrolet_cobalt '66': chevrolet_colorado '67': chevrolet_corvette '68': chevrolet_cruze '69': chevrolet_el camino '70': chevrolet_equinox '71': chevrolet_express '72': chevrolet_hhr '73': chevrolet_impala '74': chevrolet_lumina '75': chevrolet_malibu '76': chevrolet_montecarlo '77': chevrolet_nova '78': chevrolet_prizm '79': chevrolet_s10 '80': chevrolet_silverado '81': chevrolet_sonic '82': chevrolet_suburban '83': chevrolet_tahoe '84': chevrolet_tracker '85': chevrolet_trailblazer '86': chevrolet_traverse '87': chevrolet_uplander '88': chevrolet_venture '89': chrysler_200 '90': chrysler_300 '91': chrysler_concorde '92': chrysler_crossfire '93': chrysler_pacifica '94': chrysler_pt cruiser '95': chrysler_sebring '96': chrysler_town&country '97': chrysler_voyager '98': dodge_avenger '99': dodge_caliber '100': dodge_challenger '101': dodge_charger '102': dodge_dakota '103': dodge_dart '104': dodge_durango '105': dodge_grand caravan '106': dodge_intrepid '107': dodge_journey '108': dodge_magnum '109': dodge_neon '110': dodge_nitro '111': dodge_ram '112': dodge_stratus '113': fiat_five hundred '114': ford_bronco '115': ford_contour '116': ford_crown victoria '117': ford_e150 '118': ford_e250 '119': ford_e350 '120': ford_edge '121': ford_escape '122': ford_escort '123': ford_excursion '124': ford_expedition '125': ford_explorer '126': ford_f100 '127': ford_f150 '128': ford_f250 '129': ford_f350 '130': ford_f450 '131': ford_fiesta '132': ford_five hundred '133': ford_focus '134': ford_freestar '135': ford_fusion '136': ford_mustang '137': ford_ranger '138': ford_taurus '139': ford_thunderbird '140': ford_windstar '141': gmc_acadia '142': gmc_canyon '143': gmc_envoy '144': gmc_jimmy '145': gmc_sierra '146': gmc_sonoma '147': gmc_suburban '148': gmc_terrain '149': gmc_yukon '150': honda_accord '151': honda_civic '152': honda_cr-v '153': honda_delsol '154': honda_element '155': honda_fit '156': honda_odyssey '157': honda_passport '158': honda_pilot '159': honda_prelude '160': honda_ridgeline '161': honda_s2000 '162': hummer_h2 '163': hummer_h3 '164': hyundai_accent '165': hyundai_azera '166': hyundai_elantra '167': hyundai_genesis '168': hyundai_santafe '169': hyundai_sonata '170': hyundai_tiburon '171': hyundai_tucson '172': infiniti_fx35 '173': infiniti_g35 '174': infiniti_g37 '175': infiniti_i30 '176': infiniti_i35 '177': infiniti_m35 '178': infiniti_q45 '179': infiniti_qx4 '180': infiniti_qx56 '181': isuzu_rodeo '182': isuzu_trooper '183': jaguar_s-type '184': jaguar_x-type '185': jaguar_xj '186': jeep_cherokee '187': jeep_cj5 '188': jeep_cj7 '189': jeep_commander '190': jeep_compass '191': jeep_grand '192': jeep_liberty '193': jeep_patriot '194': jeep_wrangler '195': kia_amanti '196': kia_forte '197': kia_optima '198': kia_rio '199': kia_sedona '200': kia_sephia '201': kia_sorento '202': kia_soul '203': kia_spectra '204': kia_sportage '205': landrover_discovery '206': landrover_rangerover '207': lexus_es300 '208': lexus_es330 '209': lexus_es350 '210': lexus_gs300 '211': lexus_gx470 '212': lexus_is250 '213': lexus_is300 '214': lexus_is350 '215': lexus_ls400 '216': lexus_ls430 '217': lexus_rx300 '218': lexus_rx330 '219': lexus_sc430 '220': lincoln_aviator '221': lincoln_continental '222': lincoln_ls '223': lincoln_mark '224': lincoln_mkx '225': lincoln_mkz '226': lincoln_navigator '227': lincoln_towncar '228': mazda_3 '229': mazda_5 '230': mazda_6 '231': mazda_626 '232': mazda_millenia '233': mazda_mpv '234': mazda_mx5 '235': mazda_protege '236': mazda_rx7 '237': mazda_rx8 '238': mazda_tribute '239': mercedes benz_c230 '240': mercedes benz_c240 '241': mercedes benz_c280 '242': mercedes benz_c300 '243': mercedes benz_c320 '244': mercedes benz_clk320 '245': mercedes benz_e320 '246': mercedes benz_e350 '247': mercedes benz_e500 '248': mercedes benz_ml320 '249': mercedes benz_ml350 '250': mercedes benz_ml500 '251': mercedes benz_s430 '252': mercedes benz_s500 '253': mercedes benz_s550 '254': mercedes benz_sl500 '255': mercury_cougar '256': mercury_grandmarquis '257': mercury_mariner '258': mercury_milan '259': mercury_mountaineer '260': mercury_sable '261': mercury_villager '262': mini_cooper '263': mitsubishi_3000gt '264': mitsubishi_eclipse '265': mitsubishi_endeavor '266': mitsubishi_galant '267': mitsubishi_lancer '268': mitsubishi_mirage '269': mitsubishi_montero '270': mitsubishi_outlander '271': nissan_240sx '272': nissan_300zx '273': nissan_350z '274': nissan_altima '275': nissan_armada '276': nissan_frontier '277': nissan_maxima '278': nissan_murano '279': nissan_pathfinder '280': nissan_quest '281': nissan_rogue '282': nissan_sentra '283': nissan_titan '284': nissan_versa '285': nissan_xterra '286': oldsmobile_alero '287': oldsmobile_aurora '288': oldsmobile_bravada '289': oldsmobile_cutlass '290': oldsmobile_intrigue '291': oldsmobile_silhouette '292': plymouth_neon '293': plymouth_voyager '294': pontiac_bonneville '295': pontiac_firebird '296': pontiac_g5 '297': pontiac_g6 '298': pontiac_grandam '299': pontiac_grandprix '300': pontiac_gto '301': pontiac_montana '302': pontiac_sunfire '303': pontiac_torrent '304': pontiac_transam '305': pontiac_vibe '306': porsche_911 '307': porsche_boxster '308': porsche_cayenne '309': ram_1500 '310': saab_9-3 '311': saab_9-5 '312': saturn_aura '313': saturn_ion '314': saturn_l200 '315': saturn_l300 '316': saturn_sl1 '317': saturn_sl2 '318': saturn_vue '319': scion_tc '320': scion_xa '321': scion_xb '322': scion_xd '323': smart_fortwo '324': subaru_forester '325': subaru_impreza '326': subaru_legacy '327': subaru_outback '328': subaru_wrx '329': suzuki_forenza '330': suzuki_sx4 '331': suzuki_xl7 '332': toyota_4runner '333': toyota_avalon '334': toyota_camry '335': toyota_celica '336': toyota_corolla '337': toyota_echo '338': toyota_fjcruiser '339': toyota_highlander '340': toyota_landcruiser '341': toyota_matrix '342': toyota_mr2 '343': toyota_pickup '344': toyota_prius '345': toyota_rav4 '346': toyota_sequoia '347': toyota_sienna '348': toyota_solara '349': toyota_supra '350': toyota_t100 '351': toyota_tacoma '352': toyota_tercel '353': toyota_tundra '354': toyota_yaris '355': volkswagen_beetle '356': volkswagen_bug '357': volkswagen_cc '358': volkswagen_eos '359': volkswagen_golf '360': volkswagen_gti '361': volkswagen_jetta '362': volkswagen_newbeetle '363': volkswagen_passat '364': volkswagen_rabbit '365': volkswagen_touareg '366': volvo_850 '367': volvo_c70 '368': volvo_s40 '369': volvo_s60 '370': volvo_s70 '371': volvo_s80 '372': volvo_v70 '373': volvo_xc70 '374': volvo_xc90 splits: - name: train num_bytes: 498938159.51709396 num_examples: 26852 download_size: 498718383 dataset_size: 498938159.51709396 --- # Dataset Card for "VMMRdb_make_model_val" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dura-garage/nep-spell-eval-single-only-dedup

--- license: mit ---