davanstrien/summary-test · Datasets at Hugging Face

datasetId stringlengths 2 117	card stringlengths 19 1.01M
liuyanchen1015/MULTI_VALUE_rte_yall	--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 22293 num_examples: 42 - name: train num_bytes: 17700 num_examples: 34 download_size: 36392 dataset_size: 39993 --- # Dataset Card for "MULTI_VALUE_rte_yall" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Klarks/naruto	--- license: afl-3.0 ---
BevenRozario/job_desc_3k	--- dataset_info: features: - name: text dtype: string splits: - name: train_dataset num_bytes: 5388093.9 num_examples: 2700 - name: eval_dataset num_bytes: 598677.1 num_examples: 300 download_size: 1640958 dataset_size: 5986771.0 configs: - config_name: default data_files: - split: train_dataset path: data/train_dataset-* - split: eval_dataset path: data/eval_dataset-* ---
Tongjilibo/BD_Knowledge_Extraction	--- license: apache-2.0 --- # 百度关系提取数据集 - 官网: http://ai.baidu.com/broad/download?dataset=sked
Betilo/dalva98	--- license: openrail ---
AdapterOcean/dollyaug-standardized_cluster_2_std	--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2928085 num_examples: 3106 download_size: 1723057 dataset_size: 2928085 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dollyaug-standardized_cluster_2_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CyberHarem/sajou_yukimi_idolmastercinderellagirls	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sajou_yukimi/佐城雪美 (THE iDOLM@STER: Cinderella Girls) This is the dataset of sajou_yukimi/佐城雪美 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `long_hair, red_eyes, bangs, blue_hair, blunt_bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:------------\|:----------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 572.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 350.66 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1177 \| 740.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 513.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1177 \| 1007.10 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sajou_yukimi_idolmastercinderellagirls', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 7 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, black_skirt, black_thighhighs, long_sleeves, looking_at_viewer, red_ribbon, solo, frilled_skirt, neck_ribbon, white_background, white_shirt, blush, closed_mouth, simple_background, frilled_sleeves, very_long_hair, zettai_ryouiki, smile, wide_sleeves \| \| 1 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, black_skirt, blush, frilled_skirt, juliet_sleeves, simple_background, solo, very_long_hair, white_background, white_shirt, black_thighhighs, looking_at_viewer, black_hair, red_ribbon, braid, wide_sleeves, striped_panties, closed_mouth, neck_ribbon, small_breasts, feet_out_of_frame, skirt_lift, :<, flying_sweatdrops, garter_straps, lifted_by_self \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, black_thighhighs, blush, cat_ears, cat_girl, cat_tail, kemonomimi_mode, looking_at_viewer, paw_gloves, red_ribbon, shadow, solo, very_long_hair, white_background, wide_sleeves, all_fours, black_skirt, braid, frilled_skirt, neck_ribbon, pleated_skirt, simple_background, white_shirt, black_gloves, black_hair, juliet_sleeves, no_shoes, parted_lips, tail_raised, triangle_mouth, full_body, garter_straps \| \| 3 \| 25 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, solo, smile, dress, frills, lolita_hairband, looking_at_viewer, blush, ribbon, black_pantyhose, gothic_lolita, bow, sitting \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, enmaided, looking_at_viewer, maid_apron, maid_headdress, solo, smile, cat_ears, long_sleeves, white_apron, black_dress, black_footwear, frilled_dress, full_body, blue_bow, bowtie, fake_animal_ears, mary_janes, white_background \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, black_hair, blush, cat_ears, open_mouth, solo, looking_at_viewer, paw_pose, heart, tail, flat_chest, navel, small_breasts, swimsuit \| \| 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, blush, looking_at_viewer, navel, side-tie_bikini_bottom, solo, simple_background, white_background, cat_ears, cat_tail, front-tie_top, white_bikini, ass_visible_through_thighs, breasts, cameltoe, flat_chest, micro_bikini, open_mouth, thigh_gap \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1girl, blush, looking_at_viewer, solo, collarbone, school_swimsuit, simple_background, white_background, flat_chest, smile, blue_one-piece_swimsuit, closed_mouth, covered_navel, sitting, small_breasts, wet \| \| 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, frills, hair_bow, kimono, smile, solo, black_gloves, looking_at_viewer, wide_sleeves, black_cat, hair_rings, sitting, twin_braids, blush, floral_print, pantyhose, striped \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1girl, blush, looking_at_viewer, obi, smile, solo, hair_flower, wide_sleeves, floral_print, long_sleeves, black_hair, print_kimono, sidelocks \| \| 10 \| 6 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| bare_shoulders, blush, detached_collar, playboy_bunny, rabbit_ears, 1girl, black_hairband, black_leotard, fake_animal_ears, looking_at_viewer, small_breasts, solo, strapless_leotard, very_long_hair, white_background, black_bowtie, brown_pantyhose, covered_navel, fishnet_pantyhose, garter_straps, simple_background, white_collar, bare_arms, closed_mouth, rabbit_tail, smile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_skirt \| black_thighhighs \| long_sleeves \| looking_at_viewer \| red_ribbon \| solo \| frilled_skirt \| neck_ribbon \| white_background \| white_shirt \| blush \| closed_mouth \| simple_background \| frilled_sleeves \| very_long_hair \| zettai_ryouiki \| smile \| wide_sleeves \| juliet_sleeves \| black_hair \| braid \| striped_panties \| small_breasts \| feet_out_of_frame \| skirt_lift \| :< \| flying_sweatdrops \| garter_straps \| lifted_by_self \| cat_ears \| cat_girl \| cat_tail \| kemonomimi_mode \| paw_gloves \| shadow \| all_fours \| pleated_skirt \| black_gloves \| no_shoes \| parted_lips \| tail_raised \| triangle_mouth \| full_body \| dress \| frills \| lolita_hairband \| ribbon \| black_pantyhose \| gothic_lolita \| bow \| sitting \| enmaided \| maid_apron \| maid_headdress \| white_apron \| black_dress \| black_footwear \| frilled_dress \| blue_bow \| bowtie \| fake_animal_ears \| mary_janes \| open_mouth \| paw_pose \| heart \| tail \| flat_chest \| navel \| swimsuit \| side-tie_bikini_bottom \| front-tie_top \| white_bikini \| ass_visible_through_thighs \| breasts \| cameltoe \| micro_bikini \| thigh_gap \| collarbone \| school_swimsuit \| blue_one-piece_swimsuit \| covered_navel \| wet \| hair_bow \| kimono \| black_cat \| hair_rings \| twin_braids \| floral_print \| pantyhose \| striped \| obi \| hair_flower \| print_kimono \| sidelocks \| bare_shoulders \| detached_collar \| playboy_bunny \| rabbit_ears \| black_hairband \| black_leotard \| strapless_leotard \| black_bowtie \| brown_pantyhose \| fishnet_pantyhose \| white_collar \| bare_arms \| rabbit_tail \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:--------------\|:-------------------\|:---------------\|:--------------------\|:-------------\|:-------\|:----------------\|:--------------\|:-------------------\|:--------------\|:--------\|:---------------\|:--------------------\|:------------------\|:-----------------\|:-----------------\|:--------\|:---------------\|:-----------------\|:-------------\|:--------\|:------------------\|:----------------\|:--------------------\|:-------------\|:-----\|:--------------------\|:----------------\|:-----------------\|:-----------\|:-----------\|:-----------\|:------------------\|:-------------\|:---------\|:------------\|:----------------\|:---------------\|:-----------\|:--------------\|:--------------\|:-----------------\|:------------\|:--------\|:---------\|:------------------\|:---------\|:------------------\|:----------------\|:------\|:----------\|:-----------\|:-------------\|:-----------------\|:--------------\|:--------------\|:-----------------\|:----------------\|:-----------\|:---------\|:-------------------\|:-------------\|:-------------\|:-----------\|:--------\|:-------\|:-------------\|:--------\|:-----------\|:-------------------------\|:----------------\|:---------------\|:-----------------------------\|:----------\|:-----------\|:---------------\|:------------\|:-------------\|:------------------\|:--------------------------\|:----------------\|:------\|:-----------\|:---------\|:------------\|:-------------\|:--------------\|:---------------\|:------------\|:----------\|:------\|:--------------\|:---------------\|:------------\|:-----------------\|:------------------\|:----------------\|:--------------\|:-----------------\|:----------------\|:--------------------\|:---------------\|:------------------\|:--------------------\|:---------------\|:------------\|:--------------\| \| 0 \| 7 \| ![](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 \| 15 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 5 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 25 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| X \| \| X \| \| \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| \| \| X \| \| X \| \| \| X \| \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 6 \| ![](samples/10/clu10-sample0.png) \| ![](samples/10/clu10-sample1.png) \| ![](samples/10/clu10-sample2.png) \| ![](samples/10/clu10-sample3.png) \| ![](samples/10/clu10-sample4.png) \| X \| \| \| \| X \| \| X \| \| \| X \| \| X \| X \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| 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ricahrd/McPedrinho	--- license: openrail ---
learnanything/ranking	--- license: unknown ---
NBayer/test_6_rows	--- license: openrail ---
nandovallec/df_ps_train_extra	--- license: apache-2.0 ---
parksimon0808/prm800k-llama-verifier	--- dataset_info: features: - name: texts dtype: string - name: input_ids sequence: int32 - name: labels sequence: int64 splits: - name: train num_bytes: 4528067256 num_examples: 1052294 - name: test num_bytes: 145143622 num_examples: 32408 download_size: 353282233 dataset_size: 4673210878 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "prm800k-llama" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_AA051610__A11P	--- pretty_name: Evaluation run of AA051610/A11P dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AA051610/A11P](https://huggingface.co/AA051610/A11P) 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_AA051610__A11P\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T02:59:53.573351](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__A11P/blob/main/results_2023-12-11T02-59-53.573351.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\": 0.7024107525999363,\n\ \ \"acc_stderr\": 0.030362293861859797,\n \"acc_norm\": 0.7062972608094896,\n\ \ \"acc_norm_stderr\": 0.030951825247607496,\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.5644074616941972,\n\ \ \"mc2_stderr\": 0.015397066221595713\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6040955631399317,\n \"acc_stderr\": 0.014291228393536587,\n\ \ \"acc_norm\": 0.6254266211604096,\n \"acc_norm_stderr\": 0.014144193471893449\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6191993626767576,\n\ \ \"acc_stderr\": 0.004845912857338663,\n \"acc_norm\": 0.8253335988846843,\n\ \ \"acc_norm_stderr\": 0.003789055487003176\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8355263157894737,\n \"acc_stderr\": 0.030167533468632726,\n\ \ \"acc_norm\": 0.8355263157894737,\n \"acc_norm_stderr\": 0.030167533468632726\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.027134291628741706,\n\ \ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.027134291628741706\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7916666666666666,\n\ \ \"acc_stderr\": 0.03396116205845335,\n \"acc_norm\": 0.7916666666666666,\n\ \ \"acc_norm_stderr\": 0.03396116205845335\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.7063829787234043,\n \"acc_stderr\": 0.02977164271249123,\n\ \ \"acc_norm\": 0.7063829787234043,\n \"acc_norm_stderr\": 0.02977164271249123\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5526315789473685,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.5526315789473685,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.7310344827586207,\n \"acc_stderr\": 0.036951833116502325,\n\ \ \"acc_norm\": 0.7310344827586207,\n \"acc_norm_stderr\": 0.036951833116502325\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.5476190476190477,\n \"acc_stderr\": 0.025634258115554955,\n \"\ acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.025634258115554955\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8548387096774194,\n\ \ \"acc_stderr\": 0.02003956362805328,\n \"acc_norm\": 0.8548387096774194,\n\ \ \"acc_norm_stderr\": 0.02003956362805328\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.034991131376767445,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.034991131376767445\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\ : 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656208,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656208\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8787878787878788,\n \"acc_stderr\": 0.02325315795194208,\n \"\ acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.02325315795194208\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9222797927461139,\n \"acc_stderr\": 0.019321805557223144,\n\ \ \"acc_norm\": 0.9222797927461139,\n \"acc_norm_stderr\": 0.019321805557223144\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7692307692307693,\n \"acc_stderr\": 0.02136202772522272,\n \ \ \"acc_norm\": 0.7692307692307693,\n \"acc_norm_stderr\": 0.02136202772522272\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7857142857142857,\n \"acc_stderr\": 0.026653531596715484,\n\ \ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.026653531596715484\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.40397350993377484,\n \"acc_stderr\": 0.040064856853653415,\n \"\ acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.040064856853653415\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8825688073394495,\n \"acc_stderr\": 0.01380278022737734,\n \"\ acc_norm\": 0.8825688073394495,\n \"acc_norm_stderr\": 0.01380278022737734\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.8970588235294118,\n\ \ \"acc_stderr\": 0.021328337570804365,\n \"acc_norm\": 0.8970588235294118,\n\ \ \"acc_norm_stderr\": 0.021328337570804365\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\"\ : {\n \"acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n\ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7802690582959642,\n\ \ \"acc_stderr\": 0.02779017706438359,\n \"acc_norm\": 0.7802690582959642,\n\ \ \"acc_norm_stderr\": 0.02779017706438359\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951539,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951539\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"\ acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8703703703703703,\n\ \ \"acc_stderr\": 0.032472243899179465,\n \"acc_norm\": 0.8703703703703703,\n\ \ \"acc_norm_stderr\": 0.032472243899179465\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.8282208588957055,\n \"acc_stderr\": 0.029634717272371047,\n\ \ \"acc_norm\": 0.8282208588957055,\n \"acc_norm_stderr\": 0.029634717272371047\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.04616143075028546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.905982905982906,\n\ \ \"acc_stderr\": 0.019119892798924978,\n \"acc_norm\": 0.905982905982906,\n\ \ \"acc_norm_stderr\": 0.019119892798924978\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653695\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8850574712643678,\n\ \ \"acc_stderr\": 0.01140572072459397,\n \"acc_norm\": 0.8850574712643678,\n\ \ \"acc_norm_stderr\": 0.01140572072459397\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7774566473988439,\n \"acc_stderr\": 0.02239421566194282,\n\ \ \"acc_norm\": 0.7774566473988439,\n \"acc_norm_stderr\": 0.02239421566194282\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.42793296089385474,\n\ \ \"acc_stderr\": 0.01654788799741611,\n \"acc_norm\": 0.42793296089385474,\n\ \ \"acc_norm_stderr\": 0.01654788799741611\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7810457516339869,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7717041800643086,\n\ \ \"acc_stderr\": 0.02383930331139819,\n \"acc_norm\": 0.7717041800643086,\n\ \ \"acc_norm_stderr\": 0.02383930331139819\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262185,\n\ \ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262185\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5425531914893617,\n \"acc_stderr\": 0.029719281272236844,\n \ \ \"acc_norm\": 0.5425531914893617,\n \"acc_norm_stderr\": 0.029719281272236844\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5338983050847458,\n\ \ \"acc_stderr\": 0.012740853872949839,\n \"acc_norm\": 0.5338983050847458,\n\ \ \"acc_norm_stderr\": 0.012740853872949839\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7132352941176471,\n \"acc_stderr\": 0.027472274473233818,\n\ \ \"acc_norm\": 0.7132352941176471,\n \"acc_norm_stderr\": 0.027472274473233818\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7483660130718954,\n \"acc_stderr\": 0.01755581809132227,\n \ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.01755581809132227\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8656716417910447,\n\ \ \"acc_stderr\": 0.024112678240900826,\n \"acc_norm\": 0.8656716417910447,\n\ \ \"acc_norm_stderr\": 0.024112678240900826\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061445,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061445\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.5644074616941972,\n\ \ \"mc2_stderr\": 0.015397066221595713\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7987371744277821,\n \"acc_stderr\": 0.011268519971577684\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.604245640636846,\n \ \ \"acc_stderr\": 0.013469823701048815\n }\n}\n```" repo_url: https://huggingface.co/AA051610/A11P leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|arc:challenge\|25_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-11T02-59-53.573351.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|gsm8k\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hellaswag\|10_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T02-59-53.573351.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-11T02-59-53.573351.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '/details_harness\|winogrande\|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-11T02-59-53.573351.parquet' - config_name: results data_files: - split: 2023_12_11T02_59_53.573351 path: - results_2023-12-11T02-59-53.573351.parquet - split: latest path: - results_2023-12-11T02-59-53.573351.parquet --- # Dataset Card for Evaluation run of AA051610/A11P ## Dataset Description - Homepage: - Repository: https://huggingface.co/AA051610/A11P - 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 [AA051610/A11P](https://huggingface.co/AA051610/A11P) 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_AA051610__A11P", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T02:59:53.573351](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__A11P/blob/main/results_2023-12-11T02-59-53.573351.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": 0.7024107525999363, "acc_stderr": 0.030362293861859797, "acc_norm": 0.7062972608094896, "acc_norm_stderr": 0.030951825247607496, "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.5644074616941972, "mc2_stderr": 0.015397066221595713 }, "harness\|arc:challenge\|25": { "acc": 0.6040955631399317, "acc_stderr": 0.014291228393536587, "acc_norm": 0.6254266211604096, "acc_norm_stderr": 0.014144193471893449 }, "harness\|hellaswag\|10": { "acc": 0.6191993626767576, "acc_stderr": 0.004845912857338663, "acc_norm": 0.8253335988846843, "acc_norm_stderr": 0.003789055487003176 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8355263157894737, "acc_stderr": 0.030167533468632726, "acc_norm": 0.8355263157894737, "acc_norm_stderr": 0.030167533468632726 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7358490566037735, "acc_stderr": 0.027134291628741706, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.027134291628741706 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7916666666666666, "acc_stderr": 0.03396116205845335, "acc_norm": 0.7916666666666666, "acc_norm_stderr": 0.03396116205845335 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.8, "acc_stderr": 0.040201512610368445, "acc_norm": 0.8, "acc_norm_stderr": 0.040201512610368445 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.7063829787234043, "acc_stderr": 0.02977164271249123, "acc_norm": 0.7063829787234043, "acc_norm_stderr": 0.02977164271249123 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5526315789473685, "acc_stderr": 0.04677473004491199, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.04677473004491199 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.7310344827586207, "acc_stderr": 0.036951833116502325, "acc_norm": 0.7310344827586207, "acc_norm_stderr": 0.036951833116502325 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.5476190476190477, "acc_stderr": 0.025634258115554955, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.025634258115554955 }, "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": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8548387096774194, "acc_stderr": 0.02003956362805328, "acc_norm": 0.8548387096774194, "acc_norm_stderr": 0.02003956362805328 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5517241379310345, "acc_stderr": 0.034991131376767445, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.034991131376767445 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.806060606060606, "acc_stderr": 0.03087414513656208, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.03087414513656208 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8787878787878788, "acc_stderr": 0.02325315795194208, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.02325315795194208 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9222797927461139, "acc_stderr": 0.019321805557223144, "acc_norm": 0.9222797927461139, "acc_norm_stderr": 0.019321805557223144 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7692307692307693, "acc_stderr": 0.02136202772522272, "acc_norm": 0.7692307692307693, "acc_norm_stderr": 0.02136202772522272 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251976, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251976 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7857142857142857, "acc_stderr": 0.026653531596715484, "acc_norm": 0.7857142857142857, "acc_norm_stderr": 0.026653531596715484 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.40397350993377484, "acc_stderr": 0.040064856853653415, "acc_norm": 0.40397350993377484, "acc_norm_stderr": 0.040064856853653415 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8825688073394495, "acc_stderr": 0.01380278022737734, "acc_norm": 0.8825688073394495, "acc_norm_stderr": 0.01380278022737734 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8970588235294118, "acc_stderr": 0.021328337570804365, "acc_norm": 0.8970588235294118, "acc_norm_stderr": 0.021328337570804365 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8734177215189873, "acc_stderr": 0.021644195727955173, "acc_norm": 0.8734177215189873, "acc_norm_stderr": 0.021644195727955173 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7802690582959642, "acc_stderr": 0.02779017706438359, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.02779017706438359 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8549618320610687, "acc_stderr": 0.03088466108951539, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.03088466108951539 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8703703703703703, "acc_stderr": 0.032472243899179465, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.032472243899179465 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.8282208588957055, "acc_stderr": 0.029634717272371047, "acc_norm": 0.8282208588957055, "acc_norm_stderr": 0.029634717272371047 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.6160714285714286, "acc_stderr": 0.04616143075028546, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.04616143075028546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.0376017800602662, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.0376017800602662 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.905982905982906, "acc_stderr": 0.019119892798924978, "acc_norm": 0.905982905982906, "acc_norm_stderr": 0.019119892798924978 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8850574712643678, "acc_stderr": 0.01140572072459397, "acc_norm": 0.8850574712643678, "acc_norm_stderr": 0.01140572072459397 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7774566473988439, "acc_stderr": 0.02239421566194282, "acc_norm": 0.7774566473988439, "acc_norm_stderr": 0.02239421566194282 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.42793296089385474, "acc_stderr": 0.01654788799741611, "acc_norm": 0.42793296089385474, "acc_norm_stderr": 0.01654788799741611 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7810457516339869, "acc_stderr": 0.02367908986180772, "acc_norm": 0.7810457516339869, "acc_norm_stderr": 0.02367908986180772 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7717041800643086, "acc_stderr": 0.02383930331139819, "acc_norm": 0.7717041800643086, "acc_norm_stderr": 0.02383930331139819 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7808641975308642, "acc_stderr": 0.023016705640262185, "acc_norm": 0.7808641975308642, "acc_norm_stderr": 0.023016705640262185 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5425531914893617, "acc_stderr": 0.029719281272236844, "acc_norm": 0.5425531914893617, "acc_norm_stderr": 0.029719281272236844 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5338983050847458, "acc_stderr": 0.012740853872949839, "acc_norm": 0.5338983050847458, "acc_norm_stderr": 0.012740853872949839 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7132352941176471, "acc_stderr": 0.027472274473233818, "acc_norm": 0.7132352941176471, "acc_norm_stderr": 0.027472274473233818 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7483660130718954, "acc_stderr": 0.01755581809132227, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.01755581809132227 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.746938775510204, "acc_stderr": 0.02783302387139968, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.02783302387139968 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8656716417910447, "acc_stderr": 0.024112678240900826, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.024112678240900826 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061445, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061445 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.5644074616941972, "mc2_stderr": 0.015397066221595713 }, "harness\|winogrande\|5": { "acc": 0.7987371744277821, "acc_stderr": 0.011268519971577684 }, "harness\|gsm8k\|5": { "acc": 0.604245640636846, "acc_stderr": 0.013469823701048815 } } ``` ### 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]
kgr123/quality_counter_2000	--- dataset_info: features: - name: context dtype: string - name: word dtype: string - name: claim dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 11265605 num_examples: 1929 - name: train num_bytes: 11155926 num_examples: 1935 - name: validation num_bytes: 11367894 num_examples: 1941 download_size: 7642748 dataset_size: 33789425 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---
mstz/fairbelief	--- license: cc-by-sa-4.0 ---
distilled-from-one-sec-cv12/chunk_17	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1420681296 num_examples: 276828 download_size: 1451360616 dataset_size: 1420681296 --- # Dataset Card for "chunk_17" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
TIGER-Lab/Subject_Driven_Image_Editing	--- dataset_info: features: - name: uid dtype: int64 - name: image dtype: image - name: subject dtype: string - name: subject_image_0 dtype: image - name: subject_image_1 dtype: image - name: subject_image_2 dtype: image splits: - name: eval num_bytes: 154799894.0 num_examples: 154 - name: extra num_bytes: 66230300.0 num_examples: 66 download_size: 49158277 dataset_size: 221030194.0 --- # Dataset Card for "Subject_Driven_Image_Editing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Doutran/myllinhaset	--- license: openrail ---
open-llm-leaderboard/details_BarryFutureman__ChatMarc-YesAnotherMerge-7B	--- pretty_name: Evaluation run of BarryFutureman/ChatMarc-YesAnotherMerge-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BarryFutureman/ChatMarc-YesAnotherMerge-7B](https://huggingface.co/BarryFutureman/ChatMarc-YesAnotherMerge-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_BarryFutureman__ChatMarc-YesAnotherMerge-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-24T05:34:18.479696](https://huggingface.co/datasets/open-llm-leaderboard/details_BarryFutureman__ChatMarc-YesAnotherMerge-7B/blob/main/results_2024-01-24T05-34-18.479696.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\": 0.6560146863596611,\n\ \ \"acc_stderr\": 0.03206476346441959,\n \"acc_norm\": 0.6553348227714214,\n\ \ \"acc_norm_stderr\": 0.03273507303552595,\n \"mc1\": 0.5618115055079559,\n\ \ \"mc1_stderr\": 0.017369236164404406,\n \"mc2\": 0.700398174715358,\n\ \ \"mc2_stderr\": 0.015160702701664436\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.7022184300341296,\n \"acc_stderr\": 0.013363080107244484,\n\ \ \"acc_norm\": 0.7278156996587031,\n \"acc_norm_stderr\": 0.013006600406423702\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.7228639713204541,\n\ \ \"acc_stderr\": 0.004466695023677836,\n \"acc_norm\": 0.8838876717785302,\n\ \ \"acc_norm_stderr\": 0.003197048476003638\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\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.66,\n\ \ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \ \ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness\|hendrycksTest-college_chemistry\|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-college_computer_science\|5\": {\n \"\ acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.023904914311782655,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.023904914311782655\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.03068473711513536,\n \ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.03068473711513536\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\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590172,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590172\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.046695106638751906,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.046695106638751906\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.4335195530726257,\n\ \ \"acc_stderr\": 0.016574027219517635,\n \"acc_norm\": 0.4335195530726257,\n\ \ \"acc_norm_stderr\": 0.016574027219517635\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\ \ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042103,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042103\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5141843971631206,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.5141843971631206,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.47131681877444587,\n\ \ \"acc_stderr\": 0.012749206007657476,\n \"acc_norm\": 0.47131681877444587,\n\ \ \"acc_norm_stderr\": 0.012749206007657476\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\ \ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \ \ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\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\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.5618115055079559,\n\ \ \"mc1_stderr\": 0.017369236164404406,\n \"mc2\": 0.700398174715358,\n\ \ \"mc2_stderr\": 0.015160702701664436\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785722\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6997725549658832,\n \ \ \"acc_stderr\": 0.012625423152283034\n }\n}\n```" repo_url: https://huggingface.co/BarryFutureman/ChatMarc-YesAnotherMerge-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|arc:challenge\|25_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-01-24T05-34-18.479696.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|gsm8k\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hellaswag\|10_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T05-34-18.479696.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-management\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|truthfulqa:mc\|0_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-01-24T05-34-18.479696.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '/details_harness\|winogrande\|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-01-24T05-34-18.479696.parquet' - config_name: results data_files: - split: 2024_01_24T05_34_18.479696 path: - results_2024-01-24T05-34-18.479696.parquet - split: latest path: - results_2024-01-24T05-34-18.479696.parquet --- # Dataset Card for Evaluation run of BarryFutureman/ChatMarc-YesAnotherMerge-7B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [BarryFutureman/ChatMarc-YesAnotherMerge-7B](https://huggingface.co/BarryFutureman/ChatMarc-YesAnotherMerge-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_BarryFutureman__ChatMarc-YesAnotherMerge-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-24T05:34:18.479696](https://huggingface.co/datasets/open-llm-leaderboard/details_BarryFutureman__ChatMarc-YesAnotherMerge-7B/blob/main/results_2024-01-24T05-34-18.479696.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": 0.6560146863596611, "acc_stderr": 0.03206476346441959, "acc_norm": 0.6553348227714214, "acc_norm_stderr": 0.03273507303552595, "mc1": 0.5618115055079559, "mc1_stderr": 0.017369236164404406, "mc2": 0.700398174715358, "mc2_stderr": 0.015160702701664436 }, "harness\|arc:challenge\|25": { "acc": 0.7022184300341296, "acc_stderr": 0.013363080107244484, "acc_norm": 0.7278156996587031, "acc_norm_stderr": 0.013006600406423702 }, "harness\|hellaswag\|10": { "acc": 0.7228639713204541, "acc_stderr": 0.004466695023677836, "acc_norm": 0.8838876717785302, "acc_norm_stderr": 0.003197048476003638 }, "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": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700914, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700914 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7709677419354839, "acc_stderr": 0.023904914311782655, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.023904914311782655 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6638655462184874, "acc_stderr": 0.03068473711513536, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.03068473711513536 }, "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": 0.8440366972477065, "acc_stderr": 0.015555802713590172, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590172 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455335, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455335 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4107142857142857, "acc_stderr": 0.046695106638751906, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.046695106638751906 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406974, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406974 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8339719029374202, "acc_stderr": 0.0133064782430663, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.0133064782430663 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069363, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069363 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.4335195530726257, "acc_stderr": 0.016574027219517635, "acc_norm": 0.4335195530726257, "acc_norm_stderr": 0.016574027219517635 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.7156862745098039, "acc_stderr": 0.02582916327275748, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.02582916327275748 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7530864197530864, "acc_stderr": 0.023993501709042103, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.023993501709042103 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5141843971631206, "acc_stderr": 0.02981549448368206, "acc_norm": 0.5141843971631206, "acc_norm_stderr": 0.02981549448368206 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.47131681877444587, "acc_stderr": 0.012749206007657476, "acc_norm": 0.47131681877444587, "acc_norm_stderr": 0.012749206007657476 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396556, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396556 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6813725490196079, "acc_stderr": 0.01885008469646872, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.01885008469646872 }, "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": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.5618115055079559, "mc1_stderr": 0.017369236164404406, "mc2": 0.700398174715358, "mc2_stderr": 0.015160702701664436 }, "harness\|winogrande\|5": { "acc": 0.8389897395422258, "acc_stderr": 0.010329712832785722 }, "harness\|gsm8k\|5": { "acc": 0.6997725549658832, "acc_stderr": 0.012625423152283034 } } ``` ## 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]
coastalcph/fm-updates-llama2-chat-7b	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: query struct: - name: label dtype: string - name: objects list: - name: aliases sequence: string - name: label dtype: string - name: qid dtype: string - name: qid dtype: string - name: rel_id dtype: string - name: relation dtype: string - name: prediction struct: - name: predictions list: - name: answer dtype: string - name: first_token_probability dtype: float64 - name: per_token_probability sequence: float64 - name: perplexity dtype: float64 - name: query dtype: string - name: f1 dtype: float64 - name: relation dtype: string - name: type dtype: string - name: original_answer dtype: string - name: updates sequence: string splits: - name: test num_bytes: 2983210.8077126252 num_examples: 6414 download_size: 1236982 dataset_size: 2983210.8077126252 --- # Dataset Card for "fm-updates-llama2-chat-7b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
intanm/indonesian-financial-topic-classification-dataset	--- license: apache-2.0 task_categories: - text-classification language: - id tags: - finance size_categories: - 10K<n<100K --- Translated version of https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic topics = { "LABEL_0": "Analyst Update", "LABEL_1": "Fed \| Central Banks", "LABEL_2": "Company \| Product News", "LABEL_3": "Treasuries \| Corporate Debt", "LABEL_4": "Dividend", "LABEL_5": "Earnings", "LABEL_6": "Energy \| Oil", "LABEL_7": "Financials", "LABEL_8": "Currencies", "LABEL_9": "General News \| Opinion", "LABEL_10": "Gold \| Metals \| Materials", "LABEL_11": "IPO", "LABEL_12": "Legal \| Regulation", "LABEL_13": "M&A \| Investments", "LABEL_14": "Macro", "LABEL_15": "Markets", "LABEL_16": "Politics", "LABEL_17": "Personnel Change", "LABEL_18": "Stock Commentary", "LABEL_19": "Stock Movement", }
argilla/ultrafeedback_binarized_full	--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: best_response struct: - name: annotations struct: - name: helpfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: honesty struct: - name: Rating dtype: string - name: Rationale dtype: string - name: instruction_following struct: - name: Rating dtype: string - name: Rationale dtype: string - name: truthfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: critique dtype: string - name: custom_system_prompt dtype: string - name: model dtype: string - name: overall_score dtype: float64 - name: principle dtype: string - name: response dtype: string - name: best_model dtype: string - name: best_score dtype: float64 - name: random_response struct: - name: annotations struct: - name: helpfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: honesty struct: - name: Rating dtype: string - name: Rationale dtype: string - name: instruction_following struct: - name: Rating dtype: string - name: Rationale dtype: string - name: truthfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: critique dtype: string - name: custom_system_prompt dtype: string - name: model dtype: string - name: overall_score dtype: float64 - name: principle dtype: string - name: response dtype: string - name: random_model dtype: string - name: random_score dtype: float64 - name: correct_answers sequence: string - name: incorrect_answers sequence: string splits: - name: train num_bytes: 447221757 num_examples: 63967 download_size: 199896433 dataset_size: 447221757 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ultrafeedback_binarized_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yangyz1230/H3K79me3_not_filtered	--- dataset_info: features: - name: name dtype: string - name: sequence dtype: string - name: chrom dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: strand dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 439373 num_examples: 799 - name: test num_bytes: 44840 num_examples: 82 download_size: 232580 dataset_size: 484213 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
irds/beir_msmarco	--- pretty_name: '`beir/msmarco`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `beir/msmarco` The `beir/msmarco` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/msmarco). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=8,841,823 - `queries` (i.e., topics); count=509,962 This dataset is used by: [`beir_msmarco_dev`](https://huggingface.co/datasets/irds/beir_msmarco_dev), [`beir_msmarco_test`](https://huggingface.co/datasets/irds/beir_msmarco_test), [`beir_msmarco_train`](https://huggingface.co/datasets/irds/beir_msmarco_train) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/beir_msmarco', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} queries = load_dataset('irds/beir_msmarco', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} } @article{Thakur2021Beir, title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models", author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.08663", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.08663", } ```
ConvLab/sgd2	--- language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: SGD-X v2 size_categories: - 10K<n<100K task_categories: - conversational --- # Dataset Card for SGD-X v2 - Repository: https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/tree/master/sgd_x - Paper: https://arxiv.org/pdf/2110.06800.pdf - Leaderboard: None - Who transforms the dataset: Qi Zhu(zhuq96 at gmail dot com) To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via: ``` from convlab.util import load_dataset, load_ontology, load_database dataset = load_dataset('sgd2') ontology = load_ontology('sgd2') database = load_database('sgd2') ``` For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). ### Dataset Summary The Schema-Guided Dialogue (SGD) dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings. The SGD-X dataset consists of 5 linguistic variants of every schema in the original SGD dataset. Linguistic variants were written by hundreds of paid crowd-workers. In the SGD-X directory, v1 represents the variant closest to the original schemas and v5 the farthest in terms of linguistic distance. To evaluate model performance on SGD-X schemas, dialogues must be converted using the script generate_sgdx_dialogues.py. - How to get the transformed data from original data: - Download [dstc8-schema-guided-dialogue-master.zip](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/archive/refs/heads/master.zip). - Modified `sgd_x/generate_sgdx_dialogues.py` as https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/issues/57 - Run `python -m sgd_x.generate_sgdx_dialogues` under `dstc8-schema-guided-dialogue-master` dir which need tensorflow installed. - Run `python preprocess.py` in the current directory. - Main changes of the transformation: - Lower case original `act` as `intent`. - Add `count` slot for each domain, non-categorical, find span by text matching. - Categorize `dialogue acts` according to the `intent`. - Concatenate multiple values using `\|`. - Retain `active_intent`, `requested_slots`, `service_call`. - Annotations: - dialogue acts, state, db_results, service_call, active_intent, requested_slots. ### Supported Tasks and Leaderboards NLU, DST, Policy, NLG, E2E ### Languages English ### Data Splits \| split \| dialogues \| utterances \| avg_utt \| avg_tokens \| avg_domains \| cat slot match(state) \| cat slot match(goal) \| cat slot match(dialogue act) \| non-cat slot span(dialogue act) \| \|------------\|-------------\|--------------\|-----------\|--------------\|---------------\|-------------------------\|------------------------\|--------------------------------\|-----------------------------------\| \| train \| 16142 \| 329964 \| 20.44 \| 9.75 \| 1.84 \| 100 \| - \| 100 \| 100 \| \| validation \| 2482 \| 48726 \| 19.63 \| 9.66 \| 1.84 \| 100 \| - \| 100 \| 100 \| \| test \| 4201 \| 84594 \| 20.14 \| 10.4 \| 2.02 \| 100 \| - \| 100 \| 100 \| \| all \| 22825 \| 463284 \| 20.3 \| 9.86 \| 1.87 \| 100 \| - \| 100 \| 100 \| 45 domains: ['Banks_12', 'Buses_12', 'Buses_22', 'Calendar_12', 'Events_12', 'Events_22', 'Flights_12', 'Flights_22', 'Homes_12', 'Hotels_12', 'Hotels_22', 'Hotels_32', 'Media_12', 'Movies_12', 'Music_12', 'Music_22', 'RentalCars_12', 'RentalCars_22', 'Restaurants_12', 'RideSharing_12', 'RideSharing_22', 'Services_12', 'Services_22', 'Services_32', 'Travel_12', 'Weather_12', 'Alarm_12', 'Banks_22', 'Flights_32', 'Hotels_42', 'Media_22', 'Movies_22', 'Restaurants_22', 'Services_42', 'Buses_32', 'Events_32', 'Flights_42', 'Homes_22', 'Media_32', 'Messaging_12', 'Movies_32', 'Music_32', 'Payment_12', 'RentalCars_32', 'Trains_12'] - cat slot match: how many values of categorical slots are in the possible values of ontology in percentage. - non-cat slot span: how many values of non-categorical slots have span annotation in percentage. ### Citation ``` @inproceedings{lee2022sgd, title={SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems}, author={Lee, Harrison and Gupta, Raghav and Rastogi, Abhinav and Cao, Yuan and Zhang, Bin and Wu, Yonghui}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={36}, number={10}, pages={10938--10946}, year={2022} } ``` ### Licensing Information [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
g-ronimo/oasst2_top1_en_answers-mixtral	--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 14166584 num_examples: 5419 download_size: 7059605 dataset_size: 14166584 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 tags: - synthetic --- * Top 1% conversations of https://huggingface.co/datasets/OpenAssistant/oasst2 * language-filtered: en * generated using https://github.com/blancsw/deep_4_all/blob/main/datasets/oasst/convert.py * assistant answers replaced with answers by [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) * _Note_: This is an unfiltered dataset, it for sure contains very bad answers.
distilled-from-one-sec-cv12/chunk_79	--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1303912900 num_examples: 254075 download_size: 1332936637 dataset_size: 1303912900 --- # Dataset Card for "chunk_79" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
amitness/logits-maltese-512	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 230200052 num_examples: 12655 download_size: 84312982 dataset_size: 230200052 --- # Dataset Card for "logits-maltese-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Vishwanath0912/qa_en_hi	--- license: mit ---
sumitpardhiya/Face-Mask-Detection	--- license: apache-2.0 --- The dataset contains three folders: Train, Test, and Validation. Each of these folders includes two subfolders—one for mask images and another for images without masks.
EleutherAI/quirky_modularaddition_increment0_bob_hard	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 3563112.95803125 num_examples: 48087 - name: validation num_bytes: 75436.0905 num_examples: 1018 - name: test num_bytes: 73418.235 num_examples: 991 download_size: 1104505 dataset_size: 3711967.28353125 --- # Dataset Card for "quirky_modularaddition_increment0_bob_hard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kenilshah35/dictation-test	--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 8894736.0 num_examples: 19 download_size: 4493848 dataset_size: 8894736.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
StanBienaives/jade-considerants	--- language: - fr ---
open-llm-leaderboard/details_vistagi__Mixtral-8x7b-v0.1-dpo	--- pretty_name: Evaluation run of vistagi/Mixtral-8x7b-v0.1-dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vistagi/Mixtral-8x7b-v0.1-dpo](https://huggingface.co/vistagi/Mixtral-8x7b-v0.1-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_vistagi__Mixtral-8x7b-v0.1-dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-18T06:28:07.647994](https://huggingface.co/datasets/open-llm-leaderboard/details_vistagi__Mixtral-8x7b-v0.1-dpo/blob/main/results_2024-02-18T06-28-07.647994.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\": 0.7134552339034452,\n\ \ \"acc_stderr\": 0.030055997546363594,\n \"acc_norm\": 0.7181597948300631,\n\ \ \"acc_norm_stderr\": 0.030631631253278484,\n \"mc1\": 0.31456548347613217,\n\ \ \"mc1_stderr\": 0.016255241993179185,\n \"mc2\": 0.4674384125733044,\n\ \ \"mc2_stderr\": 0.01414272854245227\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6322525597269625,\n \"acc_stderr\": 0.01409099561816849,\n\ \ \"acc_norm\": 0.6655290102389079,\n \"acc_norm_stderr\": 0.013787460322441374\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6694881497709619,\n\ \ \"acc_stderr\": 0.004694360968929403,\n \"acc_norm\": 0.8639713204540929,\n\ \ \"acc_norm_stderr\": 0.0034211839093201673\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6962962962962963,\n\ \ \"acc_stderr\": 0.03972552884785137,\n \"acc_norm\": 0.6962962962962963,\n\ \ \"acc_norm_stderr\": 0.03972552884785137\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.031103182383123387,\n\ \ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.031103182383123387\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.7886792452830189,\n \"acc_stderr\": 0.025125766484827845,\n\ \ \"acc_norm\": 0.7886792452830189,\n \"acc_norm_stderr\": 0.025125766484827845\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565666,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565666\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.7052023121387283,\n\ \ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\ \ \"acc_norm_stderr\": 0.03476599607516478\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\ \ },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\"\ : {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.03047297336338004,\n\ \ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.03047297336338004\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.6491228070175439,\n\ \ \"acc_stderr\": 0.04489539350270698,\n \"acc_norm\": 0.6491228070175439,\n\ \ \"acc_norm_stderr\": 0.04489539350270698\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03960933549451208,\n\ \ \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03960933549451208\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.47619047619047616,\n \"acc_stderr\": 0.025722097064388525,\n \"\ acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.025722097064388525\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.5634920634920635,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.5634920634920635,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.8354838709677419,\n \"acc_stderr\": 0.021090847745939313,\n \"\ acc_norm\": 0.8354838709677419,\n \"acc_norm_stderr\": 0.021090847745939313\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n \"acc_norm\"\ : 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n },\n\ \ \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\"\ : 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.8242424242424242,\n \"acc_stderr\": 0.02972094300622445,\n\ \ \"acc_norm\": 0.8242424242424242,\n \"acc_norm_stderr\": 0.02972094300622445\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240524,\n\ \ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240524\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530613,\n\ \ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530613\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.7899159663865546,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.7899159663865546,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\ acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6342592592592593,\n \"acc_stderr\": 0.03284738857647206,\n \"\ acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.03284738857647206\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568624,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568624\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8860759493670886,\n \"acc_stderr\": 0.020681745135884562,\n \ \ \"acc_norm\": 0.8860759493670886,\n \"acc_norm_stderr\": 0.020681745135884562\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7757847533632287,\n\ \ \"acc_stderr\": 0.027991534258519517,\n \"acc_norm\": 0.7757847533632287,\n\ \ \"acc_norm_stderr\": 0.027991534258519517\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494732,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494732\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.03008309871603521,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.03008309871603521\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\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\": 0.5625,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.033932957297610096,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.033932957297610096\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9145299145299145,\n\ \ \"acc_stderr\": 0.018315891685625852,\n \"acc_norm\": 0.9145299145299145,\n\ \ \"acc_norm_stderr\": 0.018315891685625852\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8735632183908046,\n\ \ \"acc_stderr\": 0.011884488905895555,\n \"acc_norm\": 0.8735632183908046,\n\ \ \"acc_norm_stderr\": 0.011884488905895555\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.8034682080924855,\n \"acc_stderr\": 0.021393961404363844,\n\ \ \"acc_norm\": 0.8034682080924855,\n \"acc_norm_stderr\": 0.021393961404363844\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.40558659217877097,\n\ \ \"acc_stderr\": 0.016421670506339175,\n \"acc_norm\": 0.40558659217877097,\n\ \ \"acc_norm_stderr\": 0.016421670506339175\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.8202614379084967,\n \"acc_stderr\": 0.02198603218206415,\n\ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.02198603218206415\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149883,\n\ \ \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149883\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5283687943262412,\n \"acc_stderr\": 0.02977945095730305,\n \ \ \"acc_norm\": 0.5283687943262412,\n \"acc_norm_stderr\": 0.02977945095730305\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5325945241199479,\n\ \ \"acc_stderr\": 0.012743072942653368,\n \"acc_norm\": 0.5325945241199479,\n\ \ \"acc_norm_stderr\": 0.012743072942653368\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.8014705882352942,\n \"acc_stderr\": 0.024231013370541087,\n\ \ \"acc_norm\": 0.8014705882352942,\n \"acc_norm_stderr\": 0.024231013370541087\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.7826797385620915,\n \"acc_stderr\": 0.016684820929148587,\n \ \ \"acc_norm\": 0.7826797385620915,\n \"acc_norm_stderr\": 0.016684820929148587\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7918367346938775,\n \"acc_stderr\": 0.025991117672813292,\n\ \ \"acc_norm\": 0.7918367346938775,\n \"acc_norm_stderr\": 0.025991117672813292\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8805970149253731,\n\ \ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\ \ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.93,\n \"acc_stderr\": 0.0256432399976243,\n \ \ \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.0256432399976243\n },\n\ \ \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.31456548347613217,\n\ \ \"mc1_stderr\": 0.016255241993179185,\n \"mc2\": 0.4674384125733044,\n\ \ \"mc2_stderr\": 0.01414272854245227\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156885\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5617892342683851,\n \ \ \"acc_stderr\": 0.013666915917255069\n }\n}\n```" repo_url: https://huggingface.co/vistagi/Mixtral-8x7b-v0.1-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_18T06_28_07.647994 path: - '/details_harness\|arc:challenge\|25_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-02-18T06-28-07.647994.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|gsm8k\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hellaswag\|10_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-02-18T06-28-07.647994.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-management\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|truthfulqa:mc\|0_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-02-18T06-28-07.647994.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '/details_harness\|winogrande\|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-02-18T06-28-07.647994.parquet' - config_name: results data_files: - split: 2024_02_18T06_28_07.647994 path: - results_2024-02-18T06-28-07.647994.parquet - split: latest path: - results_2024-02-18T06-28-07.647994.parquet --- # Dataset Card for Evaluation run of vistagi/Mixtral-8x7b-v0.1-dpo <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [vistagi/Mixtral-8x7b-v0.1-dpo](https://huggingface.co/vistagi/Mixtral-8x7b-v0.1-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_vistagi__Mixtral-8x7b-v0.1-dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-18T06:28:07.647994](https://huggingface.co/datasets/open-llm-leaderboard/details_vistagi__Mixtral-8x7b-v0.1-dpo/blob/main/results_2024-02-18T06-28-07.647994.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": 0.7134552339034452, "acc_stderr": 0.030055997546363594, "acc_norm": 0.7181597948300631, "acc_norm_stderr": 0.030631631253278484, "mc1": 0.31456548347613217, "mc1_stderr": 0.016255241993179185, "mc2": 0.4674384125733044, "mc2_stderr": 0.01414272854245227 }, "harness\|arc:challenge\|25": { "acc": 0.6322525597269625, "acc_stderr": 0.01409099561816849, "acc_norm": 0.6655290102389079, "acc_norm_stderr": 0.013787460322441374 }, "harness\|hellaswag\|10": { "acc": 0.6694881497709619, "acc_stderr": 0.004694360968929403, "acc_norm": 0.8639713204540929, "acc_norm_stderr": 0.0034211839093201673 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6962962962962963, "acc_stderr": 0.03972552884785137, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.03972552884785137 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.8223684210526315, "acc_stderr": 0.031103182383123387, "acc_norm": 0.8223684210526315, "acc_norm_stderr": 0.031103182383123387 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7886792452830189, "acc_stderr": 0.025125766484827845, "acc_norm": 0.7886792452830189, "acc_norm_stderr": 0.025125766484827845 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565666, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565666 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.7052023121387283, "acc_stderr": 0.03476599607516478, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.03476599607516478 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6808510638297872, "acc_stderr": 0.03047297336338004, "acc_norm": 0.6808510638297872, "acc_norm_stderr": 0.03047297336338004 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.6491228070175439, "acc_stderr": 0.04489539350270698, "acc_norm": 0.6491228070175439, "acc_norm_stderr": 0.04489539350270698 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03960933549451208, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03960933549451208 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.025722097064388525, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.025722097064388525 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8354838709677419, "acc_stderr": 0.021090847745939313, "acc_norm": 0.8354838709677419, "acc_norm_stderr": 0.021090847745939313 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.6354679802955665, "acc_stderr": 0.0338640574606209, "acc_norm": 0.6354679802955665, "acc_norm_stderr": 0.0338640574606209 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.8242424242424242, "acc_stderr": 0.02972094300622445, "acc_norm": 0.8242424242424242, "acc_norm_stderr": 0.02972094300622445 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.9378238341968912, "acc_stderr": 0.017426974154240524, "acc_norm": 0.9378238341968912, "acc_norm_stderr": 0.017426974154240524 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.7128205128205128, "acc_stderr": 0.022939925418530613, "acc_norm": 0.7128205128205128, "acc_norm_stderr": 0.022939925418530613 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.7899159663865546, "acc_stderr": 0.026461398717471874, "acc_norm": 0.7899159663865546, "acc_norm_stderr": 0.026461398717471874 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248437, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248437 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8844036697247707, "acc_stderr": 0.01370874953417264, "acc_norm": 0.8844036697247707, "acc_norm_stderr": 0.01370874953417264 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.6342592592592593, "acc_stderr": 0.03284738857647206, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.03284738857647206 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.8578431372549019, "acc_stderr": 0.024509803921568624, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.024509803921568624 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.8860759493670886, "acc_stderr": 0.020681745135884562, "acc_norm": 0.8860759493670886, "acc_norm_stderr": 0.020681745135884562 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.7757847533632287, "acc_stderr": 0.027991534258519517, "acc_norm": 0.7757847533632287, "acc_norm_stderr": 0.027991534258519517 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494732, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494732 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8760330578512396, "acc_stderr": 0.03008309871603521, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.03008309871603521 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "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": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8640776699029126, "acc_stderr": 0.033932957297610096, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.033932957297610096 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9145299145299145, "acc_stderr": 0.018315891685625852, "acc_norm": 0.9145299145299145, "acc_norm_stderr": 0.018315891685625852 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8735632183908046, "acc_stderr": 0.011884488905895555, "acc_norm": 0.8735632183908046, "acc_norm_stderr": 0.011884488905895555 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.8034682080924855, "acc_stderr": 0.021393961404363844, "acc_norm": 0.8034682080924855, "acc_norm_stderr": 0.021393961404363844 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.40558659217877097, "acc_stderr": 0.016421670506339175, "acc_norm": 0.40558659217877097, "acc_norm_stderr": 0.016421670506339175 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.8202614379084967, "acc_stderr": 0.02198603218206415, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.02198603218206415 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.8487654320987654, "acc_stderr": 0.019935086092149883, "acc_norm": 0.8487654320987654, "acc_norm_stderr": 0.019935086092149883 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5283687943262412, "acc_stderr": 0.02977945095730305, "acc_norm": 0.5283687943262412, "acc_norm_stderr": 0.02977945095730305 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5325945241199479, "acc_stderr": 0.012743072942653368, "acc_norm": 0.5325945241199479, "acc_norm_stderr": 0.012743072942653368 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.8014705882352942, "acc_stderr": 0.024231013370541087, "acc_norm": 0.8014705882352942, "acc_norm_stderr": 0.024231013370541087 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7826797385620915, "acc_stderr": 0.016684820929148587, "acc_norm": 0.7826797385620915, "acc_norm_stderr": 0.016684820929148587 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7918367346938775, "acc_stderr": 0.025991117672813292, "acc_norm": 0.7918367346938775, "acc_norm_stderr": 0.025991117672813292 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.93, "acc_stderr": 0.0256432399976243, "acc_norm": 0.93, "acc_norm_stderr": 0.0256432399976243 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015575, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015575 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.31456548347613217, "mc1_stderr": 0.016255241993179185, "mc2": 0.4674384125733044, "mc2_stderr": 0.01414272854245227 }, "harness\|winogrande\|5": { "acc": 0.8153117600631413, "acc_stderr": 0.010905978112156885 }, "harness\|gsm8k\|5": { "acc": 0.5617892342683851, "acc_stderr": 0.013666915917255069 } } ``` ## 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]
mtkinit/SuperDataset18293	--- pretty_name: SuperDataset18293 tags: - uci - world --- # SuperDataset18293 Created from AIOD platform
kalcho100/flippy_combined_dataset	--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 925982000.8 num_examples: 677240 - name: test num_bytes: 231495500.2 num_examples: 169310 download_size: 623484715 dataset_size: 1157477501.0 --- # Dataset Card for "flippy_combined_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B	--- pretty_name: Evaluation run of TurkuNLP/gpt3-finnish-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TurkuNLP/gpt3-finnish-13B](https://huggingface.co/TurkuNLP/gpt3-finnish-13B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_TurkuNLP__gpt3-finnish-13B_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-08T08:19:11.789658](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public/blob/main/results_2023-11-08T08-19-11.789658.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\": 0.010906040268456376,\n\ \ \"em_stderr\": 0.0010636334198497977,\n \"f1\": 0.0586136744966444,\n\ \ \"f1_stderr\": 0.001583703669300269,\n \"acc\": 0.29157154884622954,\n\ \ \"acc_stderr\": 0.007692758773767466\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.010906040268456376,\n \"em_stderr\": 0.0010636334198497977,\n\ \ \"f1\": 0.0586136744966444,\n \"f1_stderr\": 0.001583703669300269\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.003032600454890068,\n \ \ \"acc_stderr\": 0.0015145735612245414\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.580110497237569,\n \"acc_stderr\": 0.013870943986310391\n\ \ }\n}\n```" repo_url: https://huggingface.co/TurkuNLP/gpt3-finnish-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_11_08T08_19_11.789658 path: - '/details_harness\|drop\|3_2023-11-08T08-19-11.789658.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-11-08T08-19-11.789658.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_08T08_19_11.789658 path: - '/details_harness\|gsm8k\|5_2023-11-08T08-19-11.789658.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-11-08T08-19-11.789658.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_08T08_19_11.789658 path: - '/details_harness\|winogrande\|5_2023-11-08T08-19-11.789658.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-11-08T08-19-11.789658.parquet' - config_name: results data_files: - split: 2023_11_08T08_19_11.789658 path: - results_2023-11-08T08-19-11.789658.parquet - split: latest path: - results_2023-11-08T08-19-11.789658.parquet --- # Dataset Card for Evaluation run of TurkuNLP/gpt3-finnish-13B ## Dataset Description - Homepage: - Repository: https://huggingface.co/TurkuNLP/gpt3-finnish-13B - 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 [TurkuNLP/gpt3-finnish-13B](https://huggingface.co/TurkuNLP/gpt3-finnish-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_TurkuNLP__gpt3-finnish-13B_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-08T08:19:11.789658](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public/blob/main/results_2023-11-08T08-19-11.789658.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": 0.010906040268456376, "em_stderr": 0.0010636334198497977, "f1": 0.0586136744966444, "f1_stderr": 0.001583703669300269, "acc": 0.29157154884622954, "acc_stderr": 0.007692758773767466 }, "harness\|drop\|3": { "em": 0.010906040268456376, "em_stderr": 0.0010636334198497977, "f1": 0.0586136744966444, "f1_stderr": 0.001583703669300269 }, "harness\|gsm8k\|5": { "acc": 0.003032600454890068, "acc_stderr": 0.0015145735612245414 }, "harness\|winogrande\|5": { "acc": 0.580110497237569, "acc_stderr": 0.013870943986310391 } } ``` ### 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]
mboth/waermeVerteilen-200-undersampled	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: Datatype dtype: string - name: Beschreibung dtype: string - name: Name dtype: string - name: Unit dtype: string - name: text dtype: string - name: Grundfunktion dtype: string - name: ZweiteGrundfunktion dtype: string - name: label dtype: class_label: names: '0': Druckhaltestation '1': HeizkreisAllgemein '2': Heizkurve '3': Kaeltemengenzaehler '4': Pumpe '5': Raum '6': Regler '7': Ruecklauf '8': Uebertrager '9': Ventil '10': Vorlauf '11': Waermemengenzaehler '12': Warmwasserbereitung splits: - name: train num_bytes: 407710.65048052603 num_examples: 1916 - name: test num_bytes: 423002 num_examples: 1978 - name: valid num_bytes: 423002 num_examples: 1978 download_size: 411048 dataset_size: 1253714.650480526 --- # Dataset Card for "waermeVerteilen-200-undersampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
rojpav/mini-croupier	--- license: apache-2.0 ---
communityai/aptchat-code-math-0.5k	--- dataset_info: features: - name: category dtype: string - name: total_tokens dtype: int64 - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 7006904.0 num_examples: 581 download_size: 2903792 dataset_size: 7006904.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
vwxyzjn/ultrafeedback_binarized_1710204240	--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: query list: - name: content dtype: string - name: role dtype: string - name: query_token sequence: int64 - name: query_token_len dtype: int64 - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 splits: - name: train_prefs num_bytes: 975839605.6968021 num_examples: 24122 - name: test_prefs num_bytes: 31713753.975 num_examples: 786 download_size: 113334298 dataset_size: 1007553359.6718022 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* ---
SEACrowd/kopi_nllb	--- tags: - self-supervised-pretraining language: - ind - jav - ace - ban - bjn - min - sun --- # kopi_nllb KopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset, allenai/nllb each language set also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` Hefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv https://arxiv.org/abs/2205.12654, 2022. NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv https://arxiv.org/abs/2207.04672, 2022. ``` ## License ODC_C ## Homepage [https://huggingface.co/datasets/munggok/KoPI-NLLB](https://huggingface.co/datasets/munggok/KoPI-NLLB) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)
coref-data/gen_winograd_raw	--- license: cc-by-nd-4.0 --- # gen_winograd - Project: https://ufal.mff.cuni.cz/corefud - Data source: https://github.com/mbzuai-nlp/gen-X/tree/bf1c0adb4b4def03cdf419c18b2948695bc1fab8 ## Details English Winograd generated by GPT-4 ## Citation ``` @misc{whitehouse2023llmpowered, title={LLM-powered Data Augmentation for Enhanced Crosslingual Performance}, author={Chenxi Whitehouse and Monojit Choudhury and Alham Fikri Aji}, year={2023}, eprint={2305.14288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ	--- pretty_name: Evaluation run of TheBloke/Llama-2-13B-GPTQ dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ)\ \ 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 4 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_TheBloke__Llama-2-13B-GPTQ\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T16:26:14.370378](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ/blob/main/results_2023-10-27T16-26-14.370378.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\": 0.0020973154362416107,\n\ \ \"em_stderr\": 0.0004685065030368251,\n \"f1\": 0.06011535234899329,\n\ \ \"f1_stderr\": 0.0013639179977941345,\n \"acc\": 0.43730302009426913,\n\ \ \"acc_stderr\": 0.010347143848267699\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368251,\n\ \ \"f1\": 0.06011535234899329,\n \"f1_stderr\": 0.0013639179977941345\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.11296436694465505,\n \ \ \"acc_stderr\": 0.00871933902883308\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702316\n\ \ }\n}\n```" repo_url: https://huggingface.co/TheBloke/Llama-2-13B-GPTQ 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_29T15_04_20.709230 path: - '/details_harness\|arc:challenge\|25_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|arc:challenge\|25_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|arc:challenge\|25_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-08-31T11:12:42.998068.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T16_26_14.370378 path: - '/details_harness\|drop\|3_2023-10-27T16-26-14.370378.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-27T16-26-14.370378.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T16_26_14.370378 path: - '/details_harness\|gsm8k\|5_2023-10-27T16-26-14.370378.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-27T16-26-14.370378.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hellaswag\|10_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hellaswag\|10_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hellaswag\|10_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-29T15:04:20.709230.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-30T10:42:39.395336.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-08-31T11:12:42.998068.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-management\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_29T15_04_20.709230 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '/details_harness\|truthfulqa:mc\|0_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-08-31T11:12:42.998068.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T16_26_14.370378 path: - '/details_harness\|winogrande\|5_2023-10-27T16-26-14.370378.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-27T16-26-14.370378.parquet' - config_name: results data_files: - split: 2023_08_29T15_04_20.709230 path: - results_2023-08-29T15:04:20.709230.parquet - split: 2023_08_30T10_42_39.395336 path: - results_2023-08-30T10:42:39.395336.parquet - split: 2023_08_31T11_12_42.998068 path: - results_2023-08-31T11:12:42.998068.parquet - split: 2023_10_27T16_26_14.370378 path: - results_2023-10-27T16-26-14.370378.parquet - split: latest path: - results_2023-10-27T16-26-14.370378.parquet --- # Dataset Card for Evaluation run of TheBloke/Llama-2-13B-GPTQ ## Dataset Description - Homepage: - Repository: https://huggingface.co/TheBloke/Llama-2-13B-GPTQ - 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 [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) 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 4 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_TheBloke__Llama-2-13B-GPTQ", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T16:26:14.370378](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ/blob/main/results_2023-10-27T16-26-14.370378.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": 0.0020973154362416107, "em_stderr": 0.0004685065030368251, "f1": 0.06011535234899329, "f1_stderr": 0.0013639179977941345, "acc": 0.43730302009426913, "acc_stderr": 0.010347143848267699 }, "harness\|drop\|3": { "em": 0.0020973154362416107, "em_stderr": 0.0004685065030368251, "f1": 0.06011535234899329, "f1_stderr": 0.0013639179977941345 }, "harness\|gsm8k\|5": { "acc": 0.11296436694465505, "acc_stderr": 0.00871933902883308 }, "harness\|winogrande\|5": { "acc": 0.7616416732438832, "acc_stderr": 0.011974948667702316 } } ``` ### 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]
fanwei1/test	--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: image sequence: sequence: sequence: sequence: float32 - name: h264 sequence: sequence: uint8 - name: id sequence: string - name: size sequence: int64 splits: - name: train num_bytes: 296990 num_examples: 1 - name: validation num_bytes: 299714 num_examples: 1 download_size: 435230 dataset_size: 596704 ---
open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0	--- pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)\ \ 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_togethercomputer__GPT-JT-6B-v0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-17T19:26:54.220051](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0/blob/main/results_2023-10-17T19-26-54.220051.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\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.0003314581465219154,\n \"f1\": 0.043061031879194765,\n\ \ \"f1_stderr\": 0.0011437900819203201,\n \"acc\": 0.330058886781919,\n\ \ \"acc_stderr\": 0.008219084533910332\n },\n \"harness\|drop\|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219154,\n\ \ \"f1\": 0.043061031879194765,\n \"f1_stderr\": 0.0011437900819203201\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.012130401819560273,\n \ \ \"acc_stderr\": 0.003015294242890946\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.6479873717442778,\n \"acc_stderr\": 0.013422874824929718\n\ \ }\n}\n```" repo_url: https://huggingface.co/togethercomputer/GPT-JT-6B-v0 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_19T15_42_14.994932 path: - '/details_harness\|arc:challenge\|25_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-07-19T15:42:14.994932.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_17T19_26_54.220051 path: - '/details_harness\|drop\|3_2023-10-17T19-26-54.220051.parquet' - split: latest path: - '/details_harness\|drop\|3_2023-10-17T19-26-54.220051.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_17T19_26_54.220051 path: - '/details_harness\|gsm8k\|5_2023-10-17T19-26-54.220051.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-10-17T19-26-54.220051.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hellaswag\|10_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T15:42:14.994932.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T15_42_14.994932 path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-07-19T15:42:14.994932.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_17T19_26_54.220051 path: - '/details_harness\|winogrande\|5_2023-10-17T19-26-54.220051.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-10-17T19-26-54.220051.parquet' - config_name: results data_files: - split: 2023_07_19T15_42_14.994932 path: - results_2023-07-19T15:42:14.994932.parquet - split: 2023_10_17T19_26_54.220051 path: - results_2023-10-17T19-26-54.220051.parquet - split: latest path: - results_2023-10-17T19-26-54.220051.parquet --- # Dataset Card for Evaluation run of togethercomputer/GPT-JT-6B-v0 ## Dataset Description - Homepage: - Repository: https://huggingface.co/togethercomputer/GPT-JT-6B-v0 - 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 [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0) 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_togethercomputer__GPT-JT-6B-v0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-17T19:26:54.220051](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0/blob/main/results_2023-10-17T19-26-54.220051.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": 0.0010486577181208054, "em_stderr": 0.0003314581465219154, "f1": 0.043061031879194765, "f1_stderr": 0.0011437900819203201, "acc": 0.330058886781919, "acc_stderr": 0.008219084533910332 }, "harness\|drop\|3": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219154, "f1": 0.043061031879194765, "f1_stderr": 0.0011437900819203201 }, "harness\|gsm8k\|5": { "acc": 0.012130401819560273, "acc_stderr": 0.003015294242890946 }, "harness\|winogrande\|5": { "acc": 0.6479873717442778, "acc_stderr": 0.013422874824929718 } } ``` ### 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]
yzhuang/metatree_fri_c0_1000_25	--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 157080 num_examples: 714 - name: validation num_bytes: 62920 num_examples: 286 download_size: 254313 dataset_size: 220000 --- # Dataset Card for "metatree_fri_c0_1000_25" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aliciiavs/chord_images	--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': A '1': A7 '2': Am '3': C '4': D '5': D7 '6': E '7': Em '8': G splits: - name: train num_bytes: 210864168.8 num_examples: 1800 download_size: 205050921 dataset_size: 210864168.8 configs: - config_name: default data_files: - split: train path: data/train-* ---
BubbleJoe/multi_nli_unified_input	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation_matched path: data/validation_matched-* - split: validation_mismatched path: data/validation_mismatched-* dataset_info: features: - name: promptID dtype: int32 - name: pairID dtype: string - name: premise dtype: string - name: premise_binary_parse dtype: string - name: premise_parse dtype: string - name: hypothesis dtype: string - name: hypothesis_binary_parse dtype: string - name: hypothesis_parse dtype: string - name: genre dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: input dtype: string splits: - name: train num_bytes: 487186164 num_examples: 392702 - name: validation_matched num_bytes: 11956580 num_examples: 9815 - name: validation_mismatched num_bytes: 12618412 num_examples: 9832 download_size: 272284496 dataset_size: 511761156 --- # Dataset Card for "multi_nli_unified_input" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jxie/natural_questions	--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 6059360 num_examples: 87925 - name: test num_bytes: 253307 num_examples: 3610 download_size: 0 dataset_size: 6312667 --- # Dataset Card for "natural_questions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aaditya/orca_dpo_pairs-Hindi	--- dataset_info: features: - name: id dtype: string - name: codemix_system dtype: string - name: codemix_question dtype: string - name: codemix_chosen dtype: string - name: codemix_rejected dtype: string - name: codemix_question_type dtype: string - name: en_system dtype: string - name: en_question dtype: string - name: en_chosen dtype: string - name: en_rejected dtype: string splits: - name: train num_bytes: 51127339 num_examples: 10305 download_size: 27467174 dataset_size: 51127339 configs: - config_name: default data_files: - split: train path: data/train-* --- # Summary `aaditya/orca_dpo_pairs-Hindi` is an open source Hindi version dataset of Intel/orca_dpo_pairs This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 # Citation ``` @misc {orca_dpo_hindi, author = { Pal, Ankit }, title = { orca_dpo_pairs-Hindi}, year = 2024, url = { https://huggingface.co/datasets/aaditya/orca_dpo_pairs-Hindi }, doi = { 10.57967/hf/1759 }, publisher = { Hugging Face } } ```
Rasu23/iapp_all_train_test_iter0	--- dataset_info: features: - name: question_id dtype: string - name: article_id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers dtype: string - name: 'Unnamed: 0' dtype: int64 - name: id dtype: string - name: references dtype: string - name: predictions dtype: string splits: - name: train num_bytes: 17356533 num_examples: 5761 - name: test num_bytes: 2199490 num_examples: 739 download_size: 3155434 dataset_size: 19556023 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
micsell/hebrew_kan_sentence100000	--- dataset_info: features: - name: audio dtype: audio - name: id dtype: string - name: language dtype: string - name: sentence dtype: string splits: - name: train num_bytes: 1894199082.0 num_examples: 10000 download_size: 1893355737 dataset_size: 1894199082.0 configs: - config_name: default data_files: - split: train path: data/train-* ---
open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0	--- pretty_name: Evaluation run of PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0](https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-29T11:11:59.721182](https://huggingface.co/datasets/open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0/blob/main/results_2023-12-29T11-11-59.721182.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\": 0.6676932083825828,\n\ \ \"acc_stderr\": 0.03141884754120868,\n \"acc_norm\": 0.6685033288172079,\n\ \ \"acc_norm_stderr\": 0.03206517056378548,\n \"mc1\": 0.45165238678090575,\n\ \ \"mc1_stderr\": 0.01742148030027764,\n \"mc2\": 0.6056804036036146,\n\ \ \"mc2_stderr\": 0.015579014786964863\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042196,\n\ \ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6793467436765585,\n\ \ \"acc_stderr\": 0.004657738398900938,\n \"acc_norm\": 0.8653654650468035,\n\ \ \"acc_norm_stderr\": 0.0034063520713417243\n },\n \"harness\|hendrycksTest-abstract_algebra\|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-anatomy\|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.042320736951515885\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7763157894736842,\n \"acc_stderr\": 0.03391160934343604,\n\ \ \"acc_norm\": 0.7763157894736842,\n \"acc_norm_stderr\": 0.03391160934343604\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|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-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\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\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"\ acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness\|hendrycksTest-college_mathematics\|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.034961014811911786,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.034961014811911786\n },\n \"harness\|hendrycksTest-college_physics\|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\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\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236785,\n\ \ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236785\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.47354497354497355,\n \"acc_stderr\": 0.025715239811346758,\n \"\ acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.025715239811346758\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.8032258064516129,\n\ \ \"acc_stderr\": 0.022616409420742025,\n \"acc_norm\": 0.8032258064516129,\n\ \ \"acc_norm_stderr\": 0.022616409420742025\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\"\ : {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\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\": 0.806060606060606,\n \"acc_stderr\": 0.030874145136562094,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.030874145136562094\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887027,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887027\n\ \ },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\ \ },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.6064814814814815,\n \"acc_stderr\": 0.03331747876370312,\n \"\ acc_norm\": 0.6064814814814815,\n \"acc_norm_stderr\": 0.03331747876370312\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025046,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025046\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.023094329582595698,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.023094329582595698\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.7309417040358744,\n\ \ \"acc_stderr\": 0.02976377940687497,\n \"acc_norm\": 0.7309417040358744,\n\ \ \"acc_norm_stderr\": 0.02976377940687497\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728745,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728745\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097654,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097654\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026622,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026622\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281382,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281382\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.822477650063857,\n\ \ \"acc_stderr\": 0.013664230995834832,\n \"acc_norm\": 0.822477650063857,\n\ \ \"acc_norm_stderr\": 0.013664230995834832\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.39329608938547483,\n\ \ \"acc_stderr\": 0.01633726869427011,\n \"acc_norm\": 0.39329608938547483,\n\ \ \"acc_norm_stderr\": 0.01633726869427011\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.0239291555173513,\n\ \ \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.0239291555173513\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\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262192,\n\ \ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262192\n\ \ },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"\ acc\": 0.5177304964539007,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.5177304964539007,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.49282920469361147,\n\ \ \"acc_stderr\": 0.012768922739553308,\n \"acc_norm\": 0.49282920469361147,\n\ \ \"acc_norm_stderr\": 0.012768922739553308\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.025767252010855956,\n\ \ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.025767252010855956\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.704248366013072,\n \"acc_stderr\": 0.01846315413263281,\n \ \ \"acc_norm\": 0.704248366013072,\n \"acc_norm_stderr\": 0.01846315413263281\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.7795918367346939,\n \"acc_stderr\": 0.02653704531214529,\n\ \ \"acc_norm\": 0.7795918367346939,\n \"acc_norm_stderr\": 0.02653704531214529\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.45165238678090575,\n\ \ \"mc1_stderr\": 0.01742148030027764,\n \"mc2\": 0.6056804036036146,\n\ \ \"mc2_stderr\": 0.015579014786964863\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.8476716653512234,\n \"acc_stderr\": 0.010099208246065597\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.6557998483699773,\n \ \ \"acc_stderr\": 0.013086800426693785\n }\n}\n```" repo_url: https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|arc:challenge\|25_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2023-12-29T11-11-59.721182.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|gsm8k\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hellaswag\|10_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-management\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T11-11-59.721182.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-management\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|truthfulqa:mc\|0_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2023-12-29T11-11-59.721182.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '/details_harness\|winogrande\|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2023-12-29T11-11-59.721182.parquet' - config_name: results data_files: - split: 2023_12_29T11_11_59.721182 path: - results_2023-12-29T11-11-59.721182.parquet - split: latest path: - results_2023-12-29T11-11-59.721182.parquet --- # Dataset Card for Evaluation run of PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0](https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-29T11:11:59.721182](https://huggingface.co/datasets/open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0/blob/main/results_2023-12-29T11-11-59.721182.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": 0.6676932083825828, "acc_stderr": 0.03141884754120868, "acc_norm": 0.6685033288172079, "acc_norm_stderr": 0.03206517056378548, "mc1": 0.45165238678090575, "mc1_stderr": 0.01742148030027764, "mc2": 0.6056804036036146, "mc2_stderr": 0.015579014786964863 }, "harness\|arc:challenge\|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042196, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness\|hellaswag\|10": { "acc": 0.6793467436765585, "acc_stderr": 0.004657738398900938, "acc_norm": 0.8653654650468035, "acc_norm_stderr": 0.0034063520713417243 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343604, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343604 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438662, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438662 }, "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": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6994219653179191, "acc_stderr": 0.034961014811911786, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.034961014811911786 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "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": 0.6297872340425532, "acc_stderr": 0.03156564682236785, "acc_norm": 0.6297872340425532, "acc_norm_stderr": 0.03156564682236785 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6413793103448275, "acc_stderr": 0.039966295748767186, "acc_norm": 0.6413793103448275, "acc_norm_stderr": 0.039966295748767186 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.47354497354497355, "acc_stderr": 0.025715239811346758, "acc_norm": 0.47354497354497355, "acc_norm_stderr": 0.025715239811346758 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.42857142857142855, "acc_stderr": 0.0442626668137991, "acc_norm": 0.42857142857142855, "acc_norm_stderr": 0.0442626668137991 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.8032258064516129, "acc_stderr": 0.022616409420742025, "acc_norm": 0.8032258064516129, "acc_norm_stderr": 0.022616409420742025 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0.704248366013072, "acc_norm_stderr": 0.01846315413263281 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7795918367346939, "acc_stderr": 0.02653704531214529, "acc_norm": 0.7795918367346939, "acc_norm_stderr": 0.02653704531214529 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8557213930348259, "acc_stderr": 0.024845753212306053, "acc_norm": 0.8557213930348259, "acc_norm_stderr": 0.024845753212306053 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.9, "acc_stderr": 0.030151134457776334, "acc_norm": 0.9, "acc_norm_stderr": 0.030151134457776334 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5602409638554217, "acc_stderr": 0.03864139923699122, "acc_norm": 0.5602409638554217, "acc_norm_stderr": 0.03864139923699122 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8070175438596491, "acc_stderr": 0.030267457554898458, "acc_norm": 0.8070175438596491, "acc_norm_stderr": 0.030267457554898458 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.45165238678090575, "mc1_stderr": 0.01742148030027764, "mc2": 0.6056804036036146, "mc2_stderr": 0.015579014786964863 }, "harness\|winogrande\|5": { "acc": 0.8476716653512234, "acc_stderr": 0.010099208246065597 }, "harness\|gsm8k\|5": { "acc": 0.6557998483699773, "acc_stderr": 0.013086800426693785 } } ``` ## 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]
GeorgeGuo/detect	--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - music size_categories: - 10K<n<100K --- This is dataset for test
TinyPixel/elm	--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2542932 num_examples: 1073 download_size: 1390964 dataset_size: 2542932 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "elm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
sick	--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended\|image-flickr-8k - extended\|semeval2012-sts-msr-video task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: sick pretty_name: Sentences Involving Compositional Knowledge dataset_info: features: - name: id dtype: string - name: sentence_A dtype: string - name: sentence_B dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: relatedness_score dtype: float32 - name: entailment_AB dtype: string - name: entailment_BA dtype: string - name: sentence_A_original dtype: string - name: sentence_B_original dtype: string - name: sentence_A_dataset dtype: string - name: sentence_B_dataset dtype: string splits: - name: train num_bytes: 1180530 num_examples: 4439 - name: validation num_bytes: 132913 num_examples: 495 - name: test num_bytes: 1305846 num_examples: 4906 download_size: 217584 dataset_size: 2619289 --- # Dataset Card for sick ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: http://marcobaroni.org/composes/sick.html - Repository: [Needs More Information] - Paper: https://www.aclweb.org/anthology/L14-1314/ - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The dataset is in English. ## Dataset Structure ### Data Instances Example instance: ``` { "entailment_AB": "A_neutral_B", "entailment_BA": "B_neutral_A", "label": 1, "id": "1", "relatedness_score": 4.5, "sentence_A": "A group of kids is playing in a yard and an old man is standing in the background", "sentence_A_dataset": "FLICKR", "sentence_A_original": "A group of children playing in a yard, a man in the background.", "sentence_B": "A group of boys in a yard is playing and a man is standing in the background", "sentence_B_dataset": "FLICKR", "sentence_B_original": "A group of children playing in a yard, a man in the background." } ``` ### Data Fields - pair_ID: sentence pair ID - sentence_A: sentence A - sentence_B: sentence B - label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2) - relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale) - entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B) - entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A) - sentence_A_original: original sentence from which sentence A is derived - sentence_B_original: original sentence from which sentence B is derived - sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL) - sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL) ### Data Splits Train Trial Test 4439 495 4906 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{marelli-etal-2014-sick, title = "A {SICK} cure for the evaluation of compositional distributional semantic models", author = "Marelli, Marco and Menini, Stefano and Baroni, Marco and Bentivogli, Luisa and Bernardi, Raffaella and Zamparelli, Roberto", booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)", month = may, year = "2014", address = "Reykjavik, Iceland", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf", pages = "216--223", } ``` ### Contributions Thanks to [@calpt](https://github.com/calpt) for adding this dataset.
amanrangapur/Fin-Fact	--- license: apache-2.0 task_categories: - text-classification - text-generation language: - en tags: - finance pretty_name: FinFact size_categories: - 1K<n<10K dataset_info: - config_name: generation features: - name: url dtype: string - name: claim dtype: string - name: author dtype: string - name: posted dtype: string # - name: sci_digest # sequence: string # - name: justification # sequence: string # - name: issues # dtype: string # - name: image_data # sequence: # - name: image_src # dtype: string # - name: image_caption # dtype: string # - name: evidence # sequence: # - name: sentence # dtype: string # - name: hrefs # dtype: string # - name: label # dtype: string # - name: visualization_bias # dtype: int32 --- <h1 align="center">Fin-Fact - Financial Fact-Checking Dataset</h1> ## Table of Contents - [Overview](#overview) - [Dataset Description](#dataset-description) - [Dataset Usage](#dataset-usage) - [Leaderboard](#leaderboard) - [Dependencies](#dependencies) - [Run models for paper metrics](#run-models-for-paper-metrics) - [Citation](#citation) - [Contribution](#contribution) - [License](#license) - [Contact](#contact) ## Overview Welcome to the Fin-Fact repository! Fin-Fact is a comprehensive dataset designed specifically for financial fact-checking and explanation generation. This README provides an overview of the dataset, how to use it, and other relevant information. [Click here](https://arxiv.org/abs/2309.08793) to access the paper. ## Dataset Description - Name: Fin-Fact - Purpose: Fact-checking and explanation generation in the financial domain. - Labels: The dataset includes various labels, including Claim, Author, Posted Date, Sci-digest, Justification, Evidence, Evidence href, Image href, Image Caption, Visualisation Bias Label, Issues, and Claim Label. - Size: The dataset consists of 3121 claims spanning multiple financial sectors. - Additional Features: The dataset goes beyond textual claims and incorporates visual elements, including images and their captions. ## Dataset Usage Fin-Fact is a valuable resource for researchers, data scientists, and fact-checkers in the financial domain. Here's how you can use it: 1. Download the Dataset: You can download the Fin-Fact dataset [here](https://github.com/IIT-DM/Fin-Fact/blob/FinFact/finfact.json). 2. Exploratory Data Analysis: Perform exploratory data analysis to understand the dataset's structure, distribution, and any potential biases. 3. Natural Language Processing (NLP) Tasks: Utilize the dataset for various NLP tasks such as fact-checking, claim verification, and explanation generation. 4. Fact Checking Experiments: Train and evaluate machine learning models, including text and image analysis, using the dataset to enhance the accuracy of fact-checking systems. ## Leaderboard ## Dependencies We recommend you create an anaconda environment: `conda create --name finfact python=3.6 conda-build` Then, install Python requirements: `pip install -r requirements.txt` ## Run models for paper metrics We provide scripts let you easily run our dataset on existing state-of-the-art models and re-create the metrics published in paper. You should be able to reproduce our results from the paper by following these instructions. Please post an issue if you're unable to do this. To run existing ANLI models for fact checking. ### Run: 1. BART ```bash python anli.py --model_name 'ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 2. RoBERTa ```bash python anli.py --model_name 'ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 3. ELECTRA ```bash python anli.py --model_name 'ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 4. AlBERT ```bash python anli.py --model_name 'ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 5. XLNET ```bash python anli.py --model_name 'ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 6. GPT-2 ```bash python gpt2_nli.py --model_name 'fractalego/fact-checking' --data_file finfact.json ``` ## Citation ``` @misc{rangapur2023finfact, title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation}, author={Aman Rangapur and Haoran Wang and Kai Shu}, year={2023}, eprint={2309.08793}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ## Contribution We welcome contributions from the community to help improve Fin-Fact. If you have suggestions, bug reports, or want to contribute code or data, please check our [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines. ## License Fin-Fact is released under the [MIT License](/LICENSE). Please review the license before using the dataset. ## Contact For questions, feedback, or inquiries related to Fin-Fact, please contact `arangapur@hawk.iit.edu`. We hope you find Fin-Fact valuable for your research and fact-checking endeavors. Happy fact-checking!
Seanxh/twitter_dataset_1713225737	--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 243707 num_examples: 562 download_size: 78336 dataset_size: 243707 configs: - config_name: default data_files: - split: train path: data/train-* ---
qgallouedec/prj_gia_dataset_metaworld_lever_pull_v2_1111	--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the lever-pull-v2 environment, sample for the policy lever-pull-v2 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia ## Load dataset First, clone it with ```sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_lever_pull_v2_1111 ``` Then, load it with ```python import numpy as np dataset = np.load("prj_gia_dataset_metaworld_lever_pull_v2_1111/dataset.npy", allow_pickle=True).item() print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards']) ```
liuyanchen1015/VALUE_qnli_lexical	--- dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1289179 num_examples: 4844 - name: test num_bytes: 1290872 num_examples: 4829 - name: train num_bytes: 24041472 num_examples: 92638 download_size: 17902736 dataset_size: 26621523 --- # Dataset Card for "VALUE_qnli_lexical" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
traintogpb/aihub-koen-translation-integrated-base-10m	--- task_categories: - translation language: - en - ko size_categories: - 10M<n<100M --- # AI Hub Ko-En Translation Dataset (Integrated) AI Hub의 한-영 번역 관련 데이터셋 8개를 병합한 자료입니다. 병합 시 총 데이터 개수는 10,416,509개 이며, train / validation / test는 8:1:1 비율로 분할되었습니다. - base-10m: 병합 데이터 100% 사용, 총 10,416,509개 - mini-1m: 병합 데이터 10% 사용 (base-10m의 각 세트 내에서 10% 임의 선택), 총 1,041,651개 - tiny-100k: 병합 데이터 1% 사용 (base-10m의 각 세트 내에서 1% 임의 선택), 총 104,165개 ## Subsets 활용한 데이터셋 목록은 다음과 같으며, 데이터셋 이름 옆 번호는 aihubshell에서의 datasetkey입니다. - [전문분야 한영 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=111) (111) - 총 개수: 1,350,000 - 중복 제거 후 개수: 1,350,000 - 사용 칼럼: '한국어', '영어' - [한국어-영어 번역 말뭉치(기술과학)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=124) (124) - 총 개수: 1,344,631 - 중복 제거 후 개수: 1,344,631 - 사용 칼럼: 'ko', 'en' - [한국어-영어 번역 말뭉치(사회과학)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=125) (125) - 총 개수: 1,361,845 - 중복 제거 후 개수: 1,361,825 - 사용 칼럼: 'ko', 'en' - [한국어-영어 번역(병렬) 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=126) (126) - 총 개수: 1,602,418 - 중복 제거 후 개수: 1,599,924 - 사용 칼럼: '원문', '번역문' - [산업정보 연계 주요국 특허 영-한 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=563) (563) - 총 개수: 359,999 - 중복 제거 후 개수: 358,424 - 사용 칼럼: 'astrt_cont_kor', 'astrt_cont_eng' - [일상생활 및 구어체 한-영 번역 병렬 말뭉치 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71265) (71265) - 총 개수: 2,700,345 - 중복 제거 후 개수: 2,486,058 - 사용 칼럼: 'ko', 'en' - [기술과학 분야 한-영 번역 병렬 말뭉치 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71266) (71266) - 총 개수: 1,350,162 - 중복 제거 후 개수: 1,328,987 - 사용 칼럼: 'ko', 'en' - [방송콘텐츠 한국어-영어 번역 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71382) (71382) - 총 개수: 587,084 - 중복 제거 후 개수: 586,660 - 사용 칼럼: '원문', '최종번역문'
Viniciaao/HardLevel	--- license: openrail ---
TrainingDataPro/license_plates	--- license: cc-by-nc-nd-4.0 task_categories: - image-to-text language: - en tags: - finance dataset_info: - config_name: Brazil_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 173536648 num_examples: 72 download_size: 22606962 dataset_size: 173536648 - config_name: Estonia_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 7990452 num_examples: 10 download_size: 7863164 dataset_size: 7990452 - config_name: Finland_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 9650579 num_examples: 10 download_size: 9485725 dataset_size: 9650579 - config_name: Kazakhstan_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 14064541 num_examples: 19 download_size: 7265915 dataset_size: 14064541 - config_name: Kazakhstan_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 6324396 num_examples: 22 download_size: 2852873 dataset_size: 6324396 - config_name: Lithuania_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 8127614 num_examples: 10 download_size: 7940839 dataset_size: 8127614 - config_name: Serbia_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 10000777 num_examples: 10 download_size: 9808356 dataset_size: 10000777 - config_name: Serbia_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 26535839 num_examples: 67 download_size: 4044272 dataset_size: 26535839 - config_name: UAE_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 8236358 num_examples: 10 download_size: 8028800 dataset_size: 8236358 - config_name: UAE_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 41202317 num_examples: 162 download_size: 2666314 dataset_size: 41202317 --- # License Plates Over 1.2 million annotated license plates from vehicles around the world. This dataset is tailored for License Plate Recognition tasks and includes images from both YouTube and PlatesMania. Annotation details are provided in the About section below. # Get the dataset ### This is just an example of the data Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/car-license-plates?utm_source=huggingface&utm_medium=cpc&utm_campaign=license_plates) to discuss your requirements, learn about the price and buy the dataset. # About ## Variables in .csv files: - file_name - filename of the original car photo - license_plate.country - country where the vehicle was captured - bbox - normalized Bounding Box labeling of the car - license_plate.visibility - the visibility type of the license plate - license_plate.id - unique license plate's id - license_plate.mask - normalized coordinates of the license plate - license_plate.rows_count - single-line or double-line number - license_plate.number - recognized text of the license plate - license_plate.serial - only for UAE numbers - license plate series - license_plate.region - only for UAE numbers - license plate subregion - license_plate.color - only for Saudi Arabia - color of the international plate code How it works: go to the folder of the country, CSV-file contains all labeling information about images located in the subfolder "photos" of the corresponding folder. ## [TrainingData](https://trainingdata.pro/data-market/car-license-plates?utm_source=huggingface&utm_medium=cpc&utm_campaign=license_plates) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
AnonymousSub/recipe_RL_data_roberta-base	--- annotations_creators: [] language: - en language_creators: [] license: [] multilinguality: - monolingual pretty_name: recipe RL roberta base size_categories: [] source_datasets: [] tags: [] task_categories: [] task_ids: [] --- # Dataset Description ## Structure - Consists of 5 fields - Each row corresponds to a policy - sequence of actions, given an initial `<START>` state, and corresponding rewards at each step. ## Fields `steps`, `step_attn_masks`, `rewards`, `actions`, `dones` ## Field descriptions - `steps` (List of lists of `Int`s) - tokenized step tokens of all the steps in the policy sequence (here we use the `roberta-base` tokenizer, as `roberta-base` would be used to encode each step of a recipe) - `step_attn_masks` (List of lists of `Int`s) - Attention masks corresponding to `steps` - `rewards` (List of `Float`s) - Sequence of rewards (normalized b/w 0 and 1) assigned per step. - `actions` (List of lists of `Int`s) - Sequence of actions (one-hot encoded, as the action space is discrete). There are `33` different actions possible (we consider the maximum number of steps per recipe = `16`, so the action can vary from `-16` to `+16`; The class label is got by adding 16 to the actual action value) - `dones` (List of `Bool`) - Sequence of flags, conveying if the work is completed when that step is reached, or not. ## Dataset Size - Number of rows = `2255673` - Maximum number of steps per row = `16`
chansung/test	--- license: apache-2.0 ---
Falah/village4kids_2_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 2094 num_examples: 8 download_size: 2965 dataset_size: 2094 --- # Dataset Card for "village4kids_2_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
aisc-team-a1/guidelines	--- license: other license_name: common-crawl license_link: LICENSE task_categories: - text-generation language: - en pretty_name: Clinical Guidelines size_categories: - 10K<n<100K tags: - medical - health dataset_info: features: - name: id dtype: string - name: source dtype: string - name: title dtype: string - name: clean_text dtype: string - name: raw_text dtype: string - name: url dtype: string - name: overview dtype: string splits: - name: train num_bytes: 865223621 num_examples: 37970 download_size: 424262411 dataset_size: 865223621 configs: - config_name: default data_files: - split: train path: data/train-* --- This is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/epfl-llm/guidelines ### 🎉 NEW DROP 🎉 PubMed Guidelines We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas! # Clinical Guidelines The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the [Meditron](https://huggingface.co/epfl-llm/meditron-70b) Large Language Model (LLM). We publicly release a subset of 37K articles from our Guidelines corpus, extracted from 9 of 17 sources that allow content redistribution, namely CCO, CDC, CMA, ICRC, NICE, PubMed, SPOR, WHO and WikiDoc. You can scrape and clean all 17 guideline sources using our code in [epfLLM/meditron](https://github.com/epfLLM/meditron). <img width=75% src="sources.png" alt="Sources of Clinical Practice Guidelines" title="CPG sources"> ## Dataset Details <!-- Provide a longer summary of what this dataset is. --> - Curated by: [EPFL LLM Team](https://huggingface.co/epfl-llm) - Language(s): English only - License: [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) - Repository: [epfLLM/meditron](https://github.com/epfLLM/meditron) - Paper: [MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079) - Knowledge Cutoff: August 2023 ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was curated to provide a high-quality collection of clinical practice guidelines (CPGs) for the medical training of LLMs. Our Clinical Guidelines corpus comprises 48,096 articles from 17 globally recognized sources for clinician and patient-directed guidance across high and low-resource settings, multiple medical domains (internal medicine, pediatrics, oncology, infectious disease, etc.) and multiple geographical locations. ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> Clinical practice guidelines are rigorously researched frameworks designed to guide healthcare practitioners and patients in making evidence-based decisions regarding diagnosis, treatment, and management. They are compiled through a systematic process of collaborative consensus between experts to establish recommendations from the latest evidence on best practices that would maximize benefit in light of practical concerns such as available resources and context. As a super-synthesis of meta-analyses, they sit atop the evidence pyramid and form the basis of actionable evidence-based practice. Clinical guidelines differ based on several factors: - Organizational level: CPGs are produced at various organizational granularities, ranging from global to hospital-level initiatives directed by international professional medical associations to informal consortia, regional or national governmental bodies to individual NGOs and hospitals. - Geographic scope: The geographic scope ranges from global (WHO) to national (CDC, NICE) and regional (Ontario, Melbourne) to institutional (ICRC, Mayo Clinic). This corpus is biased towards English-speaking regions due to its exclusive focus on English content. - Resource level: The corpus also represents health care concerns from high- (Ontario, Melbourne), low- (WHO), and volatile- (ICRC) resource settings. - Audience level: Guidelines also contains a range of technical and conversational vocabulary with target audiences of clinicians or patients (or both), and is sometimes highly specialized within a theme (cancer, pediatrics, infectious disease). - Peer-review: The peer review processes also ranged from UN bodies (WHO), institutional review boards (ICRC), professional associations (AAFP) to publicly crowdsourced knowledge bases (WikiDoc). - Document size: Article length varies widely from very short statements to 100+ page guides. #### 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. --> The dataset is sourced from 17 globally recognized medical entities, covering a wide range of healthcare contexts and audiences. We employed pragmatic selection criteria over medical sources, seeking CPGs that were: - (1) open-access - (2) systematically formatted with homogenous textual structure (i.e., in a format in which automated processes could be deployed without excessive risk of misaligning textual sequences) - (3) in the language predominantly represented by the pre-training corpus of Llama (i.e., English) - (4) covering a breadth of medical sub-domains, audiences (clinician, nurse, patient), and resource settings (high, low, and humanitarian response settings) \| Source \| Full Name \| Tag \| Guidelines \| Words \| Audience \| Country \| Released \| \|-\|-\|-\|-\|-\|-\|-\|-\| \| [AAFP](https://www.aafp.org) \| American Academy of Family Physicians \| `aafp` \| 50 \| 9.4K \| Doctor \| USA \| No \| \| [CCO](https://www.cancercareontario.ca/en/guidelines-advice) \| Cancer Care Ontario \| `cco` \| 87 \| 199K \| Doctor \| Canada \| Yes \| \| [CDC](https://www.cdc.gov/) \| Center for Disease Control and Prevention \| `cdc` \| 621 \| 6.7M \| Doctor \| USA \| Yes \| \| [CMA](https://joulecma.ca/) \| Canadian Medical Association \| `cma` \| 431 \| 1.7M \| Doctor \| Canada \| Yes \| \| [CPS](https://cps.ca) \| Canadian Paediatric Society \| `cps` \| 54 \| 133K \| Doctor \| Canada \| No \| \| [drugs.com](https://www.drugs.com/) \| Drugs.com \| `drugs` \| 6548 \| 4.1M \| Both \| International \| No \| \| [GuidelineCentral](https://www.guidelinecentral.com/) \| GuidelineCentral \| `gc` \| 1029 \| 1M \| Doctor \| Mix \| No \| \| [ICRC](http://icrc.org/) \| International Committee of the Red Cross \| `icrc` \| 49 \| 1.2M \| Doctor \| International \| Yes \| \| [IDSA](https://www.idsociety.org/) \| Infectious Diseases Society of America \| `idsa` \| 47 \| 646K \| Doctor \| USA \| No \| \| [MAGIC](https://magicevidence.org/) \| Making GRADE The Irresistible Choice \| `magic` \| 52 \| 415K \| Doctor \| Mix \| No \| \| [MayoClinic](https://www.mayoclinic.org/) \| MayoClinic \| `mayo` \| 1100 \| 2.2M \| Patient \| USA \| No \| \| [NICE](https://www.nice.org.uk/guidance) \| National Institute for Health and Care Excellence \| `nice` \| 1656 \| 8.1M \| Doctor \| UK \| Yes \| \| [PubMed](https://pubmed.ncbi.nlm.nih.gov) \| PubMed \| `pubmed` \| 1627 \| 10.8M \| Doctor \| Mix \| Yes \| \| [RCH](https://www.rch.org.au/clinicalguide/about_rch_cpgs/welcome_to_the_clinical_practice_guidelines/) \| Royal Children's Hospital Melbourne \| `rch` \| 384 \| 410K \| Doctor \| Australia \| No \| \| [SPOR](https://sporevidencealliance.ca/key-activities/cpg-asset-map/cpg-database/) \| Strategy for Patient-Oriented Research \| `spor` \| 217 \| 1.1M \| Doctor \| Canada \| Yes \| \| [WHO](https://www.who.int/publications/who-guidelines) \| World Health Organization \| `who` \| 223 \| 3.1M \| Both \| International \| Yes \| \| [WikiDoc](https://www.wikidoc.org/) \| WikiDoc \| `wikidoc` \| 33058 \| 34M \| Both \| International \| Yes \| #### 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. --> PDF documents were converted to text using [GROBID](https://github.com/kermitt2/grobid). After extracting the raw text from each source, we cleaned data with an ad-hoc process to exclude irrelevant or repetitive content that did not contribute to the textual content, such as URLs, references, figures, table delimiters, and ill-formatted characters. This filtering procedure was performed differently for each source using a sample of 50 articles. Please note that this procedure is not perfect, as it may have removed useful information or kept superfluous content. We provide the `raw_text` for each article if you would like to perform your own cleaning step. Additionally, the text was standardized to a unified format with hierarchical section headers indicated by `'#'`, homogenous spacing `'\n\n'` separating paragraphs, and normalized lists formatted with `'- '` bullet points. Finally, all samples were deduplicated using title matching, and articles that were too short or not English were filtered out. #### 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. --> As the articles are publicly accessible, no personal or sensitive information is included. ## 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. --> Each row of the dataset represents one clinical practice guideline article, and consists of the following dataset fields (all strings): \| Field \| Description \| Sources with field \| \|-------------\|-------------------------------------------\|------------------------------\| \| `id` \| Unique identifier for each article \| All \| \| `source` \| Source tag (`cco`, `cdc`, `cma`, `icrc`, `nice`, `spor`, `who` or `wikidoc`)\| All \| \| `title` \| Title of the article \| CMA, NICE & WikiDoc \| \| `url` \| URL of the article \| NICE, WikiDoc & PubMed \| \| `raw_text` \| Unprocessed scraped article text \| All \| \| `clean_text`\| Cleaned and formatted article text \| All \| \| `overview` \| Short summary or abstract of the article \| NICE & Pubmed \| ## Uses <!-- Address questions around how the dataset is intended to be used. --> The dataset is intended for use in tasks related to text generation, specifically in the context of clinical practice guidelines. It can be employed for training language models and other natural language processing applications within the healthcare domain. ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> - Redistribution: Please always check redistribution licenses before using the content as these may also evolve over time. To the best of our knowledge, we are following the redistribution licensing of each source and we invite users to inform us if that is not the case. - Malicious use: We do not support any use of this corpus that may be harmful. Creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. please do not create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> - Peer-Review Quality: It is important to understand that while most sources are validated by internationally endorsed professional associations, a large proportion of articles are from Wikidoc which contains crowdsourced content. While edits in Wikidoc are generally restricted to expert review, the process of consensus and oversight is different from the traditional rigor of clinical guidelines. - Representation: This corpus is in English, and over-represents English-speaking regions. While we have included WHO and ICRC guidelines for low-resource settings, further work needs to be done to scrape sources from diverse contexts. - Temporal scope: Guidelines are constantly updated and these represent a snapshot of each in August 2023. Please re-scrape for updated content. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> We warmly invite users to help us build a more representative corpus with high-quality peer-reviewed clinical practice guidelines in various languages and representing the full scope of clinical specialties and geographic regions. We encourage users of this content to be mindful of its current limitations in temporal and geographic scope and we repeat our warning: creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. Please don’t create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice. ## Acknowledgments The availability of open-access clinical practice guidelines (CPG) was critical to this work, and we thank all the societies listed above. A broader representation of geography, medical specialties, and contexts (especially low-resource settings) could be achieved through more standardized CPG formatting practices to ensure reliable textual extraction (e.g., releasing `.txt` or `.html` versions with structured content). We encourage the CPG community to continue to make these documents available (open-access with permissive licenses for incorporation into large language models) and easily usable. ## Authors - Curation: Mary-Anne Hartley - Scraping: Antoine Bonnet, Alexandre Sallinen, Igor Krawczuk, Kyle Matoba - Cleaning: Antoine Bonnet, Alexandre Sallinen ## Citation <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> If you use the Clinical Guidelines corpus, please cite out work: ``` @misc{chen2023meditron70b, title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models}, author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, year={2023}, eprint={2311.16079}, archivePrefix={arXiv}, primaryClass={cs.CL} } @software{epfmedtrn, author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models}, month = November, year = 2023, url = {https://github.com/epfLLM/meditron} } ```
llm-aes/meva_full_rate_explain	--- dataset_info: features: - name: task_id dtype: string - name: worker_id dtype: string - name: human_label dtype: int64 - name: llm_label dtype: int64 - name: generator_1 dtype: string - name: generator_2 dtype: string - name: premise dtype: string splits: - name: train num_bytes: 391250 num_examples: 2000 download_size: 49350 dataset_size: 391250 configs: - config_name: default data_files: - split: train path: data/train-* ---
CyberHarem/rumi_bluearchive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of rumi/朱城ルミ/瑠美 (Blue Archive) This is the dataset of rumi/朱城ルミ/瑠美 (Blue Archive), containing 207 images and their tags. The core tags of this character are `brown_hair, animal_ears, long_hair, breasts, fox_ears, halo, hair_between_eyes, large_breasts, purple_eyes, crossed_bangs, red_halo, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 207 \| 356.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumi_bluearchive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 1200 \| 207 \| 300.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumi_bluearchive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 520 \| 598.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/rumi_bluearchive/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/rumi_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, black_skirt, blush, brown_pantyhose, looking_at_viewer, smile, solo, shirt, side_slit, simple_background, white_background, hand_on_own_hip, open_mouth, thigh_strap, black_pantyhose, gourd, rope_belt, short_sleeves, thighs \| \| 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, black_skirt, pantyhose, smile, solo, blush, looking_at_viewer, shirt, holding, sweat, steam \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blush, solo, covered_nipples, looking_at_viewer, simple_background, smile, sweat, white_background, red_ascot, black_skirt, wet_shirt, see-through_shirt, upper_body \| \| 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, blush, looking_at_viewer, sweat, bra_visible_through_clothes, see-through_shirt, solo, wet_shirt, simple_background, black_bra, black_skirt, hand_on_own_hip, open_mouth, smile, steaming_body, high-waist_skirt, pantyhose, short_sleeves \| \| 4 \| 8 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, looking_at_viewer, solo, blush, simple_background, white_background, one_eye_closed, chinese_clothes, black_pantyhose, sweat, thigh_strap, grin, sitting, upper_body \| \| 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) \| 1boy, 1girl, blush, solo_focus, smile, looking_at_viewer, penis, nipples, pov, breasts_squeezed_together, cum_on_breasts, sweat, censored, huge_breasts, open_mouth, paizuri_under_clothes, shirt \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_skirt \| blush \| brown_pantyhose \| looking_at_viewer \| smile \| solo \| shirt \| side_slit \| simple_background \| white_background \| hand_on_own_hip \| open_mouth \| thigh_strap \| black_pantyhose \| gourd \| rope_belt \| short_sleeves \| thighs \| pantyhose \| holding \| sweat \| steam \| covered_nipples \| red_ascot \| wet_shirt \| see-through_shirt \| upper_body \| bra_visible_through_clothes \| black_bra \| steaming_body \| high-waist_skirt \| one_eye_closed \| chinese_clothes \| grin \| sitting \| 1boy \| solo_focus \| penis \| nipples \| pov \| breasts_squeezed_together \| cum_on_breasts \| censored \| huge_breasts \| paizuri_under_clothes \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------\|:--------\|:------------------\|:--------------------\|:--------\|:-------\|:--------\|:------------\|:--------------------\|:-------------------\|:------------------\|:-------------\|:--------------\|:------------------\|:--------\|:------------\|:----------------\|:---------\|:------------\|:----------\|:--------\|:--------\|:------------------\|:------------\|:------------\|:--------------------\|:-------------\|:------------------------------\|:------------\|:----------------\|:-------------------\|:-----------------\|:------------------\|:-------\|:----------\|:-------\|:-------------\|:--------\|:----------\|:------\|:----------------------------\|:-----------------\|:-----------\|:---------------\|:------------------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 9 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 8 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| X \| X \| X \| \| \| X \| \| X \| X \| \| \| \| \| X \| \| X \| \| X \| \| \| \| X \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 8 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| X \| \| X \| \| X \| \| \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 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 \|
djfelipe/vocall	--- license: openrail ---
1aurent/RxRx1	--- license: cc-by-4.0 size_categories: - 100K<n<1M task_categories: - image-classification tags: - biology - drug - cells configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: array3_d: dtype: uint8 shape: - 512 - 512 - 6 - name: site_id dtype: string - name: well_id dtype: string - name: cell_type dtype: string - name: experiment dtype: string - name: plate dtype: int32 - name: well dtype: string - name: site dtype: int32 - name: well_type dtype: class_label: names: '0': treatment '1': positive_control '2': negative_control - name: sirna dtype: string - name: sirna_id dtype: int32 - name: embeddings sequence: float32 length: 128 splits: - name: train num_bytes: 213139738276 num_examples: 81224 - name: test num_bytes: 116210798412 num_examples: 44286 dataset_size: 329350536688 --- [![DOI](https://zenodo.org/badge/DOI/10.48550/arXiv.2301.05768.svg)](https://doi.org/10.48550/arXiv.2301.05768) # RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods ![](https://cdn-uploads.huggingface.co/production/uploads/6364f1784f773b7e4cede70c/Nex4TagKJ2NFOZjOm26E-.png) Homepage: https://www.rxrx.ai/rxrx1 \ Publication Date: 2019-06 \ License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) \ Citation: ```bibtex @misc{sypetkowski2023rxrx1, title = {RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods}, author = {Maciej Sypetkowski and Morteza Rezanejad and Saber Saberian and Oren Kraus and John Urbanik and James Taylor and Ben Mabey and Mason Victors and Jason Yosinski and Alborz Rezazadeh Sereshkeh and Imran Haque and Berton Earnshaw}, year = {2023}, eprint = {2301.05768}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ``` ## Description High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the technical execution of different experimental batches within such screens confound these observations and can lead to invalid biological conclusions. It is therefore necessary to account for these batch effects when analyzing outcomes. In this paper we describe RxRx1, a biological dataset designed specifically for the systematic study of batch effect correction methods. The dataset consists of 125,510 high-resolution fluorescence microscopy images of human cells under 1,138 genetic perturbations in 51 experimental batches across 4 cell types. Visual inspection of the images alone clearly demonstrates significant batch effects. We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task. Our goal in releasing RxRx1 is to encourage the development of effective experimental batch correction methods that generalize well to unseen experimental batches.
open-llm-leaderboard/details_FelixChao__Gemma-10.2B-Coder	--- pretty_name: Evaluation run of FelixChao/Gemma-10.2B-Coder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FelixChao/Gemma-10.2B-Coder](https://huggingface.co/FelixChao/Gemma-10.2B-Coder)\ \ 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_FelixChao__Gemma-10.2B-Coder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T14:06:19.977620](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Gemma-10.2B-Coder/blob/main/results_2024-03-21T14-06-19.977620.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\": 0.6193276450638524,\n\ \ \"acc_stderr\": 0.032595355839530035,\n \"acc_norm\": 0.6224484569319588,\n\ \ \"acc_norm_stderr\": 0.03325145393228843,\n \"mc1\": 0.34394124847001223,\n\ \ \"mc1_stderr\": 0.016629087514276775,\n \"mc2\": 0.5243704066589732,\n\ \ \"mc2_stderr\": 0.015065637058082886\n },\n \"harness\|arc:challenge\|25\"\ : {\n \"acc\": 0.5597269624573379,\n \"acc_stderr\": 0.014506769524804246,\n\ \ \"acc_norm\": 0.5870307167235495,\n \"acc_norm_stderr\": 0.014388344935398326\n\ \ },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.617307309300936,\n\ \ \"acc_stderr\": 0.004850508945116089,\n \"acc_norm\": 0.8203545110535749,\n\ \ \"acc_norm_stderr\": 0.0038310732859630774\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.5333333333333333,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness\|hendrycksTest-astronomy\|5\"\ : {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952929,\n\ \ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952929\n\ \ },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\"\ : {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319878,\n\ \ \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319878\n\ \ },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566016,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566016\n },\n \"harness\|hendrycksTest-college_chemistry\|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.45,\n\ \ \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\"\ : 0.05\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \ \ \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\"\ : 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness\|hendrycksTest-college_medicine\|5\"\ : {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.03724249595817729,\n\ \ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.03724249595817729\n\ \ },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.39215686274509803,\n\ \ \"acc_stderr\": 0.04858083574266346,\n \"acc_norm\": 0.39215686274509803,\n\ \ \"acc_norm_stderr\": 0.04858083574266346\n },\n \"harness\|hendrycksTest-computer_security\|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\":\ \ 0.6085106382978723,\n \"acc_stderr\": 0.03190701242326812,\n \"\ acc_norm\": 0.6085106382978723,\n \"acc_norm_stderr\": 0.03190701242326812\n\ \ },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.42105263157894735,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055273,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055273\n\ \ },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness\|hendrycksTest-global_facts\|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\"\ : 0.7387096774193549,\n \"acc_stderr\": 0.024993053397764812,\n \"\ acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.024993053397764812\n\ \ },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\"\ : 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n \"\ acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n\ \ },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\"\ : {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.0352439084451178,\n\ \ \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.0352439084451178\n\ \ },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\"\ : 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \ \ \"acc\": 0.5717948717948718,\n \"acc_stderr\": 0.025088301454694834,\n\ \ \"acc_norm\": 0.5717948717948718,\n \"acc_norm_stderr\": 0.025088301454694834\n\ \ },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473072,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473072\n\ \ },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \ \ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.03095663632856655,\n \ \ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.03095663632856655\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\"\ : 0.8018348623853211,\n \"acc_stderr\": 0.017090573804217905,\n \"\ acc_norm\": 0.8018348623853211,\n \"acc_norm_stderr\": 0.017090573804217905\n\ \ },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\"\ : 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\"\ : 0.7745098039215687,\n \"acc_stderr\": 0.029331162294251735,\n \"\ acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.029331162294251735\n\ \ },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"\ acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \ \ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\ \ },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.6457399103139013,\n\ \ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\ \ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness\|hendrycksTest-human_sexuality\|5\"\ : {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467765,\n\ \ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467765\n\ \ },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\":\ \ 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"\ acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n\ \ },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.04738975119274155,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.04738975119274155\n },\n \"harness\|hendrycksTest-management\|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness\|hendrycksTest-medical_genetics\|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899136,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899136\n },\n \"harness\|hendrycksTest-moral_disputes\|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.02599247202930639,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.02599247202930639\n\ \ },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.3743016759776536,\n\ \ \"acc_stderr\": 0.016185444179457175,\n \"acc_norm\": 0.3743016759776536,\n\ \ \"acc_norm_stderr\": 0.016185444179457175\n },\n \"harness\|hendrycksTest-nutrition\|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145894,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145894\n\ \ },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.6527331189710611,\n\ \ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\ \ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness\|hendrycksTest-prehistory\|5\"\ : {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495026,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495026\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\": 0.44654498044328556,\n\ \ \"acc_stderr\": 0.012697046024399687,\n \"acc_norm\": 0.44654498044328556,\n\ \ \"acc_norm_stderr\": 0.012697046024399687\n },\n \"harness\|hendrycksTest-professional_medicine\|5\"\ : {\n \"acc\": 0.5257352941176471,\n \"acc_stderr\": 0.030332578094555026,\n\ \ \"acc_norm\": 0.5257352941176471,\n \"acc_norm_stderr\": 0.030332578094555026\n\ \ },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"\ acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\ \ },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness\|hendrycksTest-security_studies\|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712845,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712845\n\ \ },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786848,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786848\n },\n \"harness\|hendrycksTest-us_foreign_policy\|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.037752516806863715,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.037752516806863715\n \ \ },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.03878626771002361,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.03878626771002361\n },\n \"harness\|hendrycksTest-world_religions\|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368032,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368032\n\ \ },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.34394124847001223,\n\ \ \"mc1_stderr\": 0.016629087514276775,\n \"mc2\": 0.5243704066589732,\n\ \ \"mc2_stderr\": 0.015065637058082886\n },\n \"harness\|winogrande\|5\"\ : {\n \"acc\": 0.7837411207576953,\n \"acc_stderr\": 0.011570614861409357\n\ \ },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.5011372251705838,\n \ \ \"acc_stderr\": 0.013772449096346838\n }\n}\n```" repo_url: https://huggingface.co/FelixChao/Gemma-10.2B-Coder leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|arc:challenge\|25_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|arc:challenge\|25_2024-03-21T14-06-19.977620.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|gsm8k\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|gsm8k\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hellaswag\|10_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hellaswag\|10_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-management\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T14-06-19.977620.parquet' - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-abstract_algebra\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-anatomy\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-astronomy\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-business_ethics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-clinical_knowledge\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_biology\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_medicine\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-college_physics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-computer_security\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-conceptual_physics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-econometrics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-electrical_engineering\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-elementary_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-formal_logic\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-global_facts\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_biology\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_chemistry\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_computer_science\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_european_history\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_geography\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_mathematics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_microeconomics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_physics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_psychology\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_statistics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_us_history\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-high_school_world_history\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_aging\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-human_sexuality\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-international_law\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-jurisprudence\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-logical_fallacies\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-machine_learning\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-management\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-marketing\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-medical_genetics\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-miscellaneous\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_disputes\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-moral_scenarios\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-nutrition\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-philosophy\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-prehistory\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_accounting\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_law\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_medicine\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-professional_psychology\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-public_relations\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-security_studies\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-sociology\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-us_foreign_policy\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-virology\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|hendrycksTest-world_religions\|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|truthfulqa:mc\|0_2024-03-21T14-06-19.977620.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '/details_harness\|winogrande\|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '/details_harness\|winogrande\|5_2024-03-21T14-06-19.977620.parquet' - config_name: results data_files: - split: 2024_03_21T14_06_19.977620 path: - results_2024-03-21T14-06-19.977620.parquet - split: latest path: - results_2024-03-21T14-06-19.977620.parquet --- # Dataset Card for Evaluation run of FelixChao/Gemma-10.2B-Coder <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [FelixChao/Gemma-10.2B-Coder](https://huggingface.co/FelixChao/Gemma-10.2B-Coder) 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_FelixChao__Gemma-10.2B-Coder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T14:06:19.977620](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Gemma-10.2B-Coder/blob/main/results_2024-03-21T14-06-19.977620.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": 0.6193276450638524, "acc_stderr": 0.032595355839530035, "acc_norm": 0.6224484569319588, "acc_norm_stderr": 0.03325145393228843, "mc1": 0.34394124847001223, "mc1_stderr": 0.016629087514276775, "mc2": 0.5243704066589732, "mc2_stderr": 0.015065637058082886 }, "harness\|arc:challenge\|25": { "acc": 0.5597269624573379, "acc_stderr": 0.014506769524804246, "acc_norm": 0.5870307167235495, "acc_norm_stderr": 0.014388344935398326 }, "harness\|hellaswag\|10": { "acc": 0.617307309300936, "acc_stderr": 0.004850508945116089, "acc_norm": 0.8203545110535749, "acc_norm_stderr": 0.0038310732859630774 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.5333333333333333, "acc_stderr": 0.043097329010363554, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.043097329010363554 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952929, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952929 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319878, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319878 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566016, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566016 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6069364161849711, "acc_stderr": 0.03724249595817729, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.03724249595817729 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266346, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266346 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6085106382978723, "acc_stderr": 0.03190701242326812, "acc_norm": 0.6085106382978723, "acc_norm_stderr": 0.03190701242326812 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055273, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055273 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.7387096774193549, "acc_stderr": 0.024993053397764812, "acc_norm": 0.7387096774193549, "acc_norm_stderr": 0.024993053397764812 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.4630541871921182, "acc_stderr": 0.035083705204426656, "acc_norm": 0.4630541871921182, "acc_norm_stderr": 0.035083705204426656 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.7151515151515152, "acc_stderr": 0.0352439084451178, "acc_norm": 0.7151515151515152, "acc_norm_stderr": 0.0352439084451178 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.8232323232323232, "acc_stderr": 0.027178752639044915, "acc_norm": 0.8232323232323232, "acc_norm_stderr": 0.027178752639044915 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.5717948717948718, "acc_stderr": 0.025088301454694834, "acc_norm": 0.5717948717948718, "acc_norm_stderr": 0.025088301454694834 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473072, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473072 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.6512605042016807, "acc_stderr": 0.03095663632856655, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.03095663632856655 }, "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": 0.8018348623853211, "acc_stderr": 0.017090573804217905, "acc_norm": 0.8018348623853211, "acc_norm_stderr": 0.017090573804217905 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.7745098039215687, "acc_stderr": 0.029331162294251735, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.029331162294251735 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.6457399103139013, "acc_stderr": 0.032100621541349864, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.032100621541349864 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.7099236641221374, "acc_stderr": 0.03980066246467765, "acc_norm": 0.7099236641221374, "acc_norm_stderr": 0.03980066246467765 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.04738975119274155, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.04738975119274155 }, "harness\|hendrycksTest-management\|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.8122605363984674, "acc_stderr": 0.013964393769899136, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899136 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.630057803468208, "acc_stderr": 0.02599247202930639, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.02599247202930639 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.3743016759776536, "acc_stderr": 0.016185444179457175, "acc_norm": 0.3743016759776536, "acc_norm_stderr": 0.016185444179457175 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145894, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145894 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.6527331189710611, "acc_stderr": 0.027040745502307336, "acc_norm": 0.6527331189710611, "acc_norm_stderr": 0.027040745502307336 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7098765432098766, "acc_stderr": 0.025251173936495026, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495026 }, "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": 0.44654498044328556, "acc_stderr": 0.012697046024399687, "acc_norm": 0.44654498044328556, "acc_norm_stderr": 0.012697046024399687 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.5257352941176471, "acc_stderr": 0.030332578094555026, "acc_norm": 0.5257352941176471, "acc_norm_stderr": 0.030332578094555026 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.6764705882352942, "acc_stderr": 0.018926082916083383, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.018926082916083383 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.726530612244898, "acc_stderr": 0.02853556033712845, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712845 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786848, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786848 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.83, "acc_stderr": 0.037752516806863715, "acc_norm": 0.83, "acc_norm_stderr": 0.037752516806863715 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.5421686746987951, "acc_stderr": 0.03878626771002361, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.03878626771002361 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368032, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368032 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.34394124847001223, "mc1_stderr": 0.016629087514276775, "mc2": 0.5243704066589732, "mc2_stderr": 0.015065637058082886 }, "harness\|winogrande\|5": { "acc": 0.7837411207576953, "acc_stderr": 0.011570614861409357 }, "harness\|gsm8k\|5": { "acc": 0.5011372251705838, "acc_stderr": 0.013772449096346838 } } ``` ## 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-multi_news-default-e22c67-2252871792	--- type: predictions tags: - autotrain - evaluation datasets: - multi_news eval_info: task: summarization model: pszemraj/long-t5-tglobal-xl-16384-book-summary metrics: [] dataset_name: multi_news dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-xl-16384-book-summary * Dataset: multi_news * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
Falah/arabic_modern_prompts	--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 44164932 num_examples: 100000 download_size: 4499641 dataset_size: 44164932 --- # Dataset Card for "arabic_modern_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
leomiranda02/vozcaiomartins	--- license: openrail ---
joey234/mmlu-human_aging-neg-prepend-verbal	--- configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 6203 num_examples: 5 - name: test num_bytes: 1393366 num_examples: 223 download_size: 173053 dataset_size: 1399569 --- # Dataset Card for "mmlu-human_aging-neg-prepend-verbal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ibranze/araproje_arc_tr_dynamic	--- dataset_info: features: - name: keys dtype: string - name: values sequence: string splits: - name: train num_bytes: 126841 num_examples: 250 download_size: 12407 dataset_size: 126841 configs: - config_name: default data_files: - split: train path: data/train-* ---
Biomedical-TeMU/SPACCC_Tokenizer	--- license: cc-by-4.0 --- # The Tokenizer for Clinical Cases Written in Spanish ## Introduction This repository contains the tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to tokenize biomedical documents, specially clinical cases written in Spanish. This model was created using the Apache OpenNLP machine learning toolkit (https://opennlp.apache.org/), with the release number 1.8.4, released in December 2017. This repository contains the training set, testing set, Gold Standard. ## Prerequisites This software has been compiled with Java SE 1.8 and it should work with recent versions. You can download Java from the following website: https://www.java.com/en/download The executable file already includes the Apache OpenNLP dependencies inside, so the download of this toolkit is not necessary. However, you may download the latest version from this website: https://opennlp.apache.org/download.html The library file we have used to compile is "opennlp-tools-1.8.4.jar". The source code should be able to compile with the latest version of OpenNLP, "opennlp-tools-RELEASE_NUMBER.jar". In case there are compilation or execution errors, please let us know and we will make all the necessary updates. ## Directory structure <pre> exec/ An executable file that can be used to apply the tokenization to your documents. You can find the notes about its execution below in section "Usage". gold_standard/ The clinical cases used as gold standard to evaluate the model's performance. model/ The tokenizationint model, "es-tokenization-model-spaccc.bin", a binary file. src/ The source code to create the model (CreateModelTok.java) and evaluate it (EvaluateModelTok.java). The directory includes an example about how to use the model inside your code (Tokenization.java). File "abbreviations.dat" contains a list of abbreviations, essential to build the model. test_set/ The clinical cases used as test set to evaluate the model's performance. train_set/ The clinical cases used to build the model. We use a single file with all documents present in directory "train_set_docs" concatented. train_set_docs/ The clinical cases used to build the model. For each record the sentences are already splitted. </pre> ## Usage The executable file Tokenizer.jar is the program you need to tokenize the text in your document. For this program, two arguments are needed: (1) the text file to tokenize, and (2) the model file (es-tokenization-model-spaccc.bin). The program will display all tokens in the terminal, with one token per line. From the `exec` folder, type the following command in your terminal: <pre> $ java -jar Tokenizer.jar INPUT_FILE MODEL_FILE </pre> ## Examples Assuming you have the executable file, the input file and the model file in the same directory: <pre> $ java -jar Tokenizer.jar file.txt es-tokenizer-model-spaccc.bin </pre> ## Model creation To create this tokenization model, we used the following training parameters (class TrainingParameters in OpenNLP) to get the best performance: - Number of iterations: 1500. - Cutoff parameter: 4. - Trainer type parameter: EventTrainer.EVENT_VALUE. - Algorithm: Maximum Entropy (ModelType.MAXENT.name()). Meanwhile, we used the following parameters for the tokenizer builder (class TokenizerFactory in OpenNLP) to get the best performance: - Language code: es (for Spanish). - Abbreviation dictionary: file "abbreviations.dat" (included in the `src/` directory). - Use alphanumeric optimization: false - Alphanumeric pattern: null ## Model evaluation After tuning the model using different values for each parameter mentioned above, we got the best performance with the values mentioned above. \| \| Value \| \| ----------------------------------------: \| :------ \| \| Number of tokens in the gold standard \| 38247 \| \| Number of tokens generated \| 38227 \| \| Number of words correctly tokenized \| 38182 \| \| Number of words wrongly tokenized \| 35 \| \| Number of tokens missed \| 30 \| \| Precision \| 99.88% \| \| Recall \| 99.83% \| \| F-Measure \| 99.85%\| Table 1: Evaluation statistics for the tokenization model. ## Contact Ander Intxaurrondo (ander.intxaurrondo@bsc.es) ## License <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)
mHossain/final_train_v2_450000	--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 9110645.1 num_examples: 27000 - name: test num_bytes: 1012293.9 num_examples: 3000 download_size: 4438870 dataset_size: 10122939.0 --- # Dataset Card for "final_train_v2_450000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
judgeou/nai3-artist-collection	--- license: cc-by-sa-4.0 ---
manojpatil/training	--- language: - en size_categories: - 1K<n<10K task_categories: - summarization tags: - legal pretty_name: abc ---
CyberHarem/furutaka_azurlane	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of furutaka/古鷹/古鹰 (Azur Lane) This is the dataset of furutaka/古鷹/古鹰 (Azur Lane), containing 34 images and their tags. The core tags of this character are `long_hair, brown_hair, breasts, blue_eyes, large_breasts, animal_ears, bangs, between_breasts, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:----------\|:-------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 34 \| 36.16 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 34 \| 23.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 90 \| 52.65 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 34 \| 33.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 90 \| 70.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-stage3-p480-1200.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/furutaka_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 34 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, retrofit_(azur_lane), solo, detached_sleeves, looking_at_viewer, pleated_skirt, sailor_collar, blush, navel, midriff, thighhighs, crop_top, simple_background, black_skirt, miniskirt, open_mouth, gloves, white_background, wide_sleeves, smile, armpits \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| retrofit_(azur_lane) \| solo \| detached_sleeves \| looking_at_viewer \| pleated_skirt \| sailor_collar \| blush \| navel \| midriff \| thighhighs \| crop_top \| simple_background \| black_skirt \| miniskirt \| open_mouth \| gloves \| white_background \| wide_sleeves \| smile \| armpits \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------------------\|:-------\|:-------------------\|:--------------------\|:----------------\|:----------------\|:--------\|:--------\|:----------\|:-------------\|:-----------\|:--------------------\|:--------------\|:------------\|:-------------\|:---------\|:-------------------\|:---------------\|:--------\|:----------\| \| 0 \| 34 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
AdapterOcean/biology_dataset_standardized_cluster_3_alpaca	--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 22853614 num_examples: 7463 download_size: 0 dataset_size: 22853614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "biology_dataset_standardized_cluster_3_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/textvqa_valid_google_flan_t5_xxl_mode_OCR_VQA_Q_rices_ns_5000	--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 934914 num_examples: 5000 download_size: 334889 dataset_size: 934914 configs: - config_name: default data_files: - split: fewshot_0 path: data/fewshot_0-* ---
benayas/snips_chatgpt_20pct_v2	--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1053502 num_examples: 13084 download_size: 423959 dataset_size: 1053502 configs: - config_name: default data_files: - split: train path: data/train-* ---
TheFinAI/en-acronym	--- dataset_info: features: - name: query dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: test num_bytes: 142136 num_examples: 1527 download_size: 48264 dataset_size: 142136 configs: - config_name: default data_files: - split: test path: data/test-* ---
unknownX/Eddie_cartoon	--- license: other ---
ProfQu/MinecraftRecipes	--- license: mit --- # MinecraftRecipes This dataset contains all crafting table recipes from Minecraft, the recipes were taken straight from the Minecraft source and put here. The data was generated from [this repository](https://github.com/ProfessorQu/MCCraftingRecipes).
CyberHarem/nanao_yuriko_theidolmstermillionlive	--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nanao_yuriko/七尾百合子/나나오유리코 (THE iDOLM@STER: Million Live!) This is the dataset of nanao_yuriko/七尾百合子/나나오유리코 (THE iDOLM@STER: Million Live!), containing 500 images and their tags. The core tags of this character are `blue_hair, yellow_eyes, short_hair, breasts, braid, bangs, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 568.69 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 356.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1212 \| 759.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 514.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1212 \| 1.00 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/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/nanao_yuriko_theidolmstermillionlive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| Tags \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 0 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, smile, looking_at_viewer, blush, open_mouth, book \| \| 1 \| 26 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, solo, blush, looking_at_viewer, open_mouth, frilled_bikini, navel, hair_flower, smile, cleavage, outdoors, collarbone, green_bikini \| \| 2 \| 18 \| ![](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) \| 1boy, 1girl, blush, hetero, open_mouth, solo_focus, nipples, pussy, sex, vaginal, penis, lying, navel, completely_nude, mosaic_censoring, spread_legs \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1boy, 1girl, blush, fellatio, hetero, nude, penis, solo_focus, looking_at_viewer, pov, ass, mosaic_censoring, cum_in_mouth \| \| 4 \| 9 \| ![](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, serafuku, solo, looking_at_viewer, white_shirt, blush, navel, pleated_skirt, red_neckerchief, short_sleeves, fingerless_gloves, white_background, white_skirt, black_gloves, cape, closed_mouth, midriff, simple_background, black_thighhighs, medium_hair, white_sailor_collar \| \| 5 \| 7 \| ![](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, blush, collarbone, solo, cleavage, upper_body, looking_at_viewer, simple_background, white_background, armpits, arms_up, bow_bra, navel, small_breasts, smile, underwear_only \| \| 6 \| 8 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, blush, looking_at_viewer, open_mouth, solo, :d, bow, detached_sleeves, white_dress, bare_shoulders, collarbone, frilled_dress, hair_ribbon, puffy_short_sleeves, sleeveless_dress, yellow_ribbon, blue_sky, day, mini_hat, outdoors, sailor_dress, white_headwear, wrist_cuffs, argyle, choker, cloud, ribbon_braid, sparkle, standing, tilted_headwear, white_sailor_collar, belt_buckle, blue_thighhighs, feathers, holding, outstretched_arm, pleated_dress, shiny_hair, white_background, white_sleeves \| \| 7 \| 6 \| ![](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, detached_collar, looking_at_viewer, rabbit_ears, solo, wrist_cuffs, cleavage, fake_animal_ears, playboy_bunny, bare_shoulders, black_bowtie, blush, sitting, strapless_leotard, black_pantyhose, closed_mouth, covered_navel, cowboy_shot, holding_tray, rabbit_tail, smile, wine_glass \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| smile \| looking_at_viewer \| blush \| open_mouth \| book \| frilled_bikini \| navel \| hair_flower \| cleavage \| outdoors \| collarbone \| green_bikini \| 1boy \| hetero \| solo_focus \| nipples \| pussy \| sex \| vaginal \| penis \| lying \| completely_nude \| mosaic_censoring \| spread_legs \| fellatio \| nude \| pov \| ass \| cum_in_mouth \| serafuku \| white_shirt \| pleated_skirt \| red_neckerchief \| short_sleeves \| fingerless_gloves \| white_background \| white_skirt \| black_gloves \| cape \| closed_mouth \| midriff \| simple_background \| black_thighhighs \| medium_hair \| white_sailor_collar \| upper_body \| armpits \| arms_up \| bow_bra \| small_breasts \| underwear_only \| :d \| bow \| detached_sleeves \| white_dress \| bare_shoulders \| frilled_dress \| hair_ribbon \| puffy_short_sleeves \| sleeveless_dress \| yellow_ribbon \| blue_sky \| day \| mini_hat \| sailor_dress \| white_headwear \| wrist_cuffs \| argyle \| choker \| cloud \| ribbon_braid \| sparkle \| standing \| tilted_headwear \| belt_buckle \| blue_thighhighs \| feathers \| holding \| outstretched_arm \| pleated_dress \| shiny_hair \| white_sleeves \| detached_collar \| rabbit_ears \| fake_animal_ears \| playboy_bunny \| black_bowtie \| sitting \| strapless_leotard \| black_pantyhose \| covered_navel \| cowboy_shot \| holding_tray \| rabbit_tail \| wine_glass \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------\|:--------------------\|:--------\|:-------------\|:-------\|:-----------------\|:--------\|:--------------\|:-----------\|:-----------\|:-------------\|:---------------\|:-------\|:---------\|:-------------\|:----------\|:--------\|:------\|:----------\|:--------\|:--------\|:------------------\|:-------------------\|:--------------\|:-----------\|:-------\|:------\|:------\|:---------------\|:-----------\|:--------------\|:----------------\|:------------------\|:----------------\|:--------------------\|:-------------------\|:--------------\|:---------------\|:-------\|:---------------\|:----------\|:--------------------\|:-------------------\|:--------------\|:----------------------\|:-------------\|:----------\|:----------\|:----------\|:----------------\|:-----------------\|:-----\|:------\|:-------------------\|:--------------\|:-----------------\|:----------------\|:--------------\|:----------------------\|:-------------------\|:----------------\|:-----------\|:------\|:-----------\|:---------------\|:-----------------\|:--------------\|:---------\|:---------\|:--------\|:---------------\|:----------\|:-----------\|:------------------\|:--------------\|:------------------\|:-----------\|:----------\|:-------------------\|:----------------\|:-------------\|:----------------\|:------------------\|:--------------\|:-------------------\|:----------------\|:---------------\|:----------\|:--------------------\|:------------------\|:----------------\|:--------------\|:---------------\|:--------------\|:-------------\| \| 0 \| 25 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 26 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 18 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| X \| \| \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 9 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 8 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 6 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| X \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|
Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT-finetune	--- language: - en license: - mit multilinguality: - monolingual --- The LXMERT text finetune data used to train visual features for the adaption of vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?". The data has been created from the data made available by the [LXMERT repo](https://github.com/airsplay/lxmert).
RiniPL/Dementia_Dataset	--- license: ecl-2.0 task_categories: - image-classification language: - en tags: - code pretty_name: Dementia ---
rbeauchamp/augmented_images_perplexity	--- dataset_info: features: - name: prompt dtype: string - name: image_path dtype: string - name: image dtype: image splits: - name: train num_bytes: 1000689543.812 num_examples: 1052 download_size: 1001267552 dataset_size: 1000689543.812 --- # Dataset Card for "augmented_images_perplexity" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bezzam/DigiCam-Mirflickr-MultiMask-10K	--- license: mit dataset_info: features: - name: lensless dtype: image - name: lensed dtype: image - name: mask_label dtype: int64 splits: - name: train num_bytes: 4048333123.5 num_examples: 8500 - name: test num_bytes: 716310780.5 num_examples: 1500 download_size: 4765086529 dataset_size: 4764643904.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---
Denissilva88/NERI	--- license: openrail ---
serbog/esco_occupations_details_multilingual	--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: el struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: lt struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: code dtype: string - name: uk struct: - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: ga struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: sv struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: cs struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: bg struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: 'no' struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: en struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: lv struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: ar struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: es struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: et struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: fi struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: sk struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: da struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: nl struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: is struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: sl struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: hr struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: pl struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: it struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: de struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: url dtype: string - name: mt struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: hu struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: fr struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: pt struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: ro struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string splits: - name: train num_bytes: 52470213 num_examples: 3629 download_size: 22696020 dataset_size: 52470213 --- # Dataset Card for "esco_occupations_details_multilingual" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
freshpearYoon/train_free_13	--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 9604588872 num_examples: 10000 download_size: 1312729438 dataset_size: 9604588872 configs: - config_name: default data_files: - split: train path: data/train-* ---
andersonbcdefg/micropile	--- dataset_info: features: - name: text dtype: string - name: __id dtype: int64 splits: - name: train num_bytes: 5544284 num_examples: 1000 download_size: 2933209 dataset_size: 5544284 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "micropile" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmrau/cqadubstack-gaming-qrels	--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 60520 num_examples: 2263 download_size: 32524 dataset_size: 60520 --- # Dataset Card for "cqadubstack-gaming-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
MouhsineGT/new_fr_200_excel	--- license: unknown ---

liuyanchen1015/MULTI_VALUE_rte_yall

--- dataset_info: features: - name: sentence1 dtype: string - name: sentence2 dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: test num_bytes: 22293 num_examples: 42 - name: train num_bytes: 17700 num_examples: 34 download_size: 36392 dataset_size: 39993 --- # Dataset Card for "MULTI_VALUE_rte_yall" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Klarks/naruto

--- license: afl-3.0 ---

BevenRozario/job_desc_3k

--- dataset_info: features: - name: text dtype: string splits: - name: train_dataset num_bytes: 5388093.9 num_examples: 2700 - name: eval_dataset num_bytes: 598677.1 num_examples: 300 download_size: 1640958 dataset_size: 5986771.0 configs: - config_name: default data_files: - split: train_dataset path: data/train_dataset-* - split: eval_dataset path: data/eval_dataset-* ---

Tongjilibo/BD_Knowledge_Extraction

--- license: apache-2.0 --- # 百度关系提取数据集 - 官网: http://ai.baidu.com/broad/download?dataset=sked

Betilo/dalva98

--- license: openrail ---

AdapterOcean/dollyaug-standardized_cluster_2_std

--- dataset_info: features: - name: message dtype: string - name: message_type dtype: string - name: message_id dtype: int64 - name: conversation_id dtype: int64 - name: cluster dtype: float64 - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 2928085 num_examples: 3106 download_size: 1723057 dataset_size: 2928085 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "dollyaug-standardized_cluster_2_std" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CyberHarem/sajou_yukimi_idolmastercinderellagirls

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of sajou_yukimi/佐城雪美 (THE iDOLM@STER: Cinderella Girls) This is the dataset of sajou_yukimi/佐城雪美 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `long_hair, red_eyes, bangs, blue_hair, blunt_bangs`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:------------|:----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 572.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 350.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1177 | 740.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 513.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1177 | 1007.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sajou_yukimi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sajou_yukimi_idolmastercinderellagirls', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 7 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_skirt, black_thighhighs, long_sleeves, looking_at_viewer, red_ribbon, solo, frilled_skirt, neck_ribbon, white_background, white_shirt, blush, closed_mouth, simple_background, frilled_sleeves, very_long_hair, zettai_ryouiki, smile, wide_sleeves | | 1 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, black_skirt, blush, frilled_skirt, juliet_sleeves, simple_background, solo, very_long_hair, white_background, white_shirt, black_thighhighs, looking_at_viewer, black_hair, red_ribbon, braid, wide_sleeves, striped_panties, closed_mouth, neck_ribbon, small_breasts, feet_out_of_frame, skirt_lift, :<, flying_sweatdrops, garter_straps, lifted_by_self | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, black_thighhighs, blush, cat_ears, cat_girl, cat_tail, kemonomimi_mode, looking_at_viewer, paw_gloves, red_ribbon, shadow, solo, very_long_hair, white_background, wide_sleeves, all_fours, black_skirt, braid, frilled_skirt, neck_ribbon, pleated_skirt, simple_background, white_shirt, black_gloves, black_hair, juliet_sleeves, no_shoes, parted_lips, tail_raised, triangle_mouth, full_body, garter_straps | | 3 | 25 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, solo, smile, dress, frills, lolita_hairband, looking_at_viewer, blush, ribbon, black_pantyhose, gothic_lolita, bow, sitting | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, enmaided, looking_at_viewer, maid_apron, maid_headdress, solo, smile, cat_ears, long_sleeves, white_apron, black_dress, black_footwear, frilled_dress, full_body, blue_bow, bowtie, fake_animal_ears, mary_janes, white_background | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, black_hair, blush, cat_ears, open_mouth, solo, looking_at_viewer, paw_pose, heart, tail, flat_chest, navel, small_breasts, swimsuit | | 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, blush, looking_at_viewer, navel, side-tie_bikini_bottom, solo, simple_background, white_background, cat_ears, cat_tail, front-tie_top, white_bikini, ass_visible_through_thighs, breasts, cameltoe, flat_chest, micro_bikini, open_mouth, thigh_gap | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1girl, blush, looking_at_viewer, solo, collarbone, school_swimsuit, simple_background, white_background, flat_chest, smile, blue_one-piece_swimsuit, closed_mouth, covered_navel, sitting, small_breasts, wet | | 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, frills, hair_bow, kimono, smile, solo, black_gloves, looking_at_viewer, wide_sleeves, black_cat, hair_rings, sitting, twin_braids, blush, floral_print, pantyhose, striped | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1girl, blush, looking_at_viewer, obi, smile, solo, hair_flower, wide_sleeves, floral_print, long_sleeves, black_hair, print_kimono, sidelocks | | 10 | 6 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-sample4.png) | bare_shoulders, blush, detached_collar, playboy_bunny, rabbit_ears, 1girl, black_hairband, black_leotard, fake_animal_ears, looking_at_viewer, small_breasts, solo, strapless_leotard, very_long_hair, white_background, black_bowtie, brown_pantyhose, covered_navel, fishnet_pantyhose, garter_straps, simple_background, white_collar, bare_arms, closed_mouth, rabbit_tail, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_skirt | black_thighhighs | long_sleeves | looking_at_viewer | red_ribbon | solo | frilled_skirt | neck_ribbon | white_background | white_shirt | blush | closed_mouth | simple_background | frilled_sleeves | very_long_hair | zettai_ryouiki | smile | wide_sleeves | juliet_sleeves | black_hair | braid | striped_panties | small_breasts | feet_out_of_frame | skirt_lift | :< | flying_sweatdrops | garter_straps | lifted_by_self | cat_ears | cat_girl | cat_tail | kemonomimi_mode | paw_gloves | shadow | all_fours | pleated_skirt | black_gloves | no_shoes | parted_lips | tail_raised | triangle_mouth | full_body | dress | frills | lolita_hairband | ribbon | black_pantyhose | gothic_lolita | bow | sitting | enmaided | maid_apron | maid_headdress | white_apron | black_dress | black_footwear | frilled_dress | blue_bow | bowtie | fake_animal_ears | mary_janes | open_mouth | paw_pose | heart | tail | flat_chest | navel | swimsuit | side-tie_bikini_bottom | front-tie_top | white_bikini | ass_visible_through_thighs | breasts | cameltoe | micro_bikini | thigh_gap | collarbone | school_swimsuit | blue_one-piece_swimsuit | covered_navel | wet | hair_bow | kimono | black_cat | hair_rings | twin_braids | floral_print | pantyhose | striped | obi | hair_flower | print_kimono | sidelocks | bare_shoulders | detached_collar | playboy_bunny | rabbit_ears | black_hairband | black_leotard | strapless_leotard | black_bowtie | brown_pantyhose | fishnet_pantyhose | white_collar | bare_arms | rabbit_tail | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:-------------------|:---------------|:--------------------|:-------------|:-------|:----------------|:--------------|:-------------------|:--------------|:--------|:---------------|:--------------------|:------------------|:-----------------|:-----------------|:--------|:---------------|:-----------------|:-------------|:--------|:------------------|:----------------|:--------------------|:-------------|:-----|:--------------------|:----------------|:-----------------|:-----------|:-----------|:-----------|:------------------|:-------------|:---------|:------------|:----------------|:---------------|:-----------|:--------------|:--------------|:-----------------|:------------|:--------|:---------|:------------------|:---------|:------------------|:----------------|:------|:----------|:-----------|:-------------|:-----------------|:--------------|:--------------|:-----------------|:----------------|:-----------|:---------|:-------------------|:-------------|:-------------|:-----------|:--------|:-------|:-------------|:--------|:-----------|:-------------------------|:----------------|:---------------|:-----------------------------|:----------|:-----------|:---------------|:------------|:-------------|:------------------|:--------------------------|:----------------|:------|:-----------|:---------|:------------|:-------------|:--------------|:---------------|:------------|:----------|:------|:--------------|:---------------|:------------|:-----------------|:------------------|:----------------|:--------------|:-----------------|:----------------|:--------------------|:---------------|:------------------|:--------------------|:---------------|:------------|:--------------| | 0 | 7 | ![](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 | 15 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | X | X | X | X | X | X | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | X | X | X | X | X | X | X | X | | X | | X | | | X | X | X | X | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 25 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | X | | X | | | X | | X | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | | X | | X | | | | | X | | | | | | | | | X | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | | | X | | X | | | X | | X | X | X | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | X | | X | | | | | X | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | 10 | 6 | ![](samples/10/clu10-sample0.png) | ![](samples/10/clu10-sample1.png) | ![](samples/10/clu10-sample2.png) | ![](samples/10/clu10-sample3.png) | ![](samples/10/clu10-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 |

ricahrd/McPedrinho

--- license: openrail ---

learnanything/ranking

--- license: unknown ---

NBayer/test_6_rows

--- license: openrail ---

nandovallec/df_ps_train_extra

--- license: apache-2.0 ---

parksimon0808/prm800k-llama-verifier

--- dataset_info: features: - name: texts dtype: string - name: input_ids sequence: int32 - name: labels sequence: int64 splits: - name: train num_bytes: 4528067256 num_examples: 1052294 - name: test num_bytes: 145143622 num_examples: 32408 download_size: 353282233 dataset_size: 4673210878 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # Dataset Card for "prm800k-llama" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_AA051610__A11P

--- pretty_name: Evaluation run of AA051610/A11P dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [AA051610/A11P](https://huggingface.co/AA051610/A11P) 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_AA051610__A11P\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-11T02:59:53.573351](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__A11P/blob/main/results_2023-12-11T02-59-53.573351.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\": 0.7024107525999363,\n\ \ \"acc_stderr\": 0.030362293861859797,\n \"acc_norm\": 0.7062972608094896,\n\ \ \"acc_norm_stderr\": 0.030951825247607496,\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.5644074616941972,\n\ \ \"mc2_stderr\": 0.015397066221595713\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6040955631399317,\n \"acc_stderr\": 0.014291228393536587,\n\ \ \"acc_norm\": 0.6254266211604096,\n \"acc_norm_stderr\": 0.014144193471893449\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6191993626767576,\n\ \ \"acc_stderr\": 0.004845912857338663,\n \"acc_norm\": 0.8253335988846843,\n\ \ \"acc_norm_stderr\": 0.003789055487003176\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\ \ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\ \ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8355263157894737,\n \"acc_stderr\": 0.030167533468632726,\n\ \ \"acc_norm\": 0.8355263157894737,\n \"acc_norm_stderr\": 0.030167533468632726\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\ \ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.027134291628741706,\n\ \ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.027134291628741706\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7916666666666666,\n\ \ \"acc_stderr\": 0.03396116205845335,\n \"acc_norm\": 0.7916666666666666,\n\ \ \"acc_norm_stderr\": 0.03396116205845335\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \ \ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.62,\n\ \ \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\ \ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\ \ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\ \ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.8,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.8,\n\ \ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.7063829787234043,\n \"acc_stderr\": 0.02977164271249123,\n\ \ \"acc_norm\": 0.7063829787234043,\n \"acc_norm_stderr\": 0.02977164271249123\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5526315789473685,\n\ \ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.5526315789473685,\n\ \ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.7310344827586207,\n \"acc_stderr\": 0.036951833116502325,\n\ \ \"acc_norm\": 0.7310344827586207,\n \"acc_norm_stderr\": 0.036951833116502325\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.5476190476190477,\n \"acc_stderr\": 0.025634258115554955,\n \"\ acc_norm\": 0.5476190476190477,\n \"acc_norm_stderr\": 0.025634258115554955\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \ \ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8548387096774194,\n\ \ \"acc_stderr\": 0.02003956362805328,\n \"acc_norm\": 0.8548387096774194,\n\ \ \"acc_norm_stderr\": 0.02003956362805328\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.034991131376767445,\n\ \ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.034991131376767445\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\ : 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.806060606060606,\n \"acc_stderr\": 0.03087414513656208,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.03087414513656208\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8787878787878788,\n \"acc_stderr\": 0.02325315795194208,\n \"\ acc_norm\": 0.8787878787878788,\n \"acc_norm_stderr\": 0.02325315795194208\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9222797927461139,\n \"acc_stderr\": 0.019321805557223144,\n\ \ \"acc_norm\": 0.9222797927461139,\n \"acc_norm_stderr\": 0.019321805557223144\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7692307692307693,\n \"acc_stderr\": 0.02136202772522272,\n \ \ \"acc_norm\": 0.7692307692307693,\n \"acc_norm_stderr\": 0.02136202772522272\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \ \ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7857142857142857,\n \"acc_stderr\": 0.026653531596715484,\n\ \ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.026653531596715484\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.40397350993377484,\n \"acc_stderr\": 0.040064856853653415,\n \"\ acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.040064856853653415\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8825688073394495,\n \"acc_stderr\": 0.01380278022737734,\n \"\ acc_norm\": 0.8825688073394495,\n \"acc_norm_stderr\": 0.01380278022737734\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\ : 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\ \ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8970588235294118,\n\ \ \"acc_stderr\": 0.021328337570804365,\n \"acc_norm\": 0.8970588235294118,\n\ \ \"acc_norm_stderr\": 0.021328337570804365\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\ : {\n \"acc\": 0.8734177215189873,\n \"acc_stderr\": 0.021644195727955173,\n\ \ \"acc_norm\": 0.8734177215189873,\n \"acc_norm_stderr\": 0.021644195727955173\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\ \ \"acc_stderr\": 0.02779017706438359,\n \"acc_norm\": 0.7802690582959642,\n\ \ \"acc_norm_stderr\": 0.02779017706438359\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951539,\n\ \ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951539\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"\ acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8703703703703703,\n\ \ \"acc_stderr\": 0.032472243899179465,\n \"acc_norm\": 0.8703703703703703,\n\ \ \"acc_norm_stderr\": 0.032472243899179465\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.8282208588957055,\n \"acc_stderr\": 0.029634717272371047,\n\ \ \"acc_norm\": 0.8282208588957055,\n \"acc_norm_stderr\": 0.029634717272371047\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6160714285714286,\n\ \ \"acc_stderr\": 0.04616143075028546,\n \"acc_norm\": 0.6160714285714286,\n\ \ \"acc_norm_stderr\": 0.04616143075028546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\ \ \"acc_stderr\": 0.019119892798924978,\n \"acc_norm\": 0.905982905982906,\n\ \ \"acc_norm_stderr\": 0.019119892798924978\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \ \ \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653695\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8850574712643678,\n\ \ \"acc_stderr\": 0.01140572072459397,\n \"acc_norm\": 0.8850574712643678,\n\ \ \"acc_norm_stderr\": 0.01140572072459397\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7774566473988439,\n \"acc_stderr\": 0.02239421566194282,\n\ \ \"acc_norm\": 0.7774566473988439,\n \"acc_norm_stderr\": 0.02239421566194282\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42793296089385474,\n\ \ \"acc_stderr\": 0.01654788799741611,\n \"acc_norm\": 0.42793296089385474,\n\ \ \"acc_norm_stderr\": 0.01654788799741611\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7810457516339869,\n \"acc_stderr\": 0.02367908986180772,\n\ \ \"acc_norm\": 0.7810457516339869,\n \"acc_norm_stderr\": 0.02367908986180772\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7717041800643086,\n\ \ \"acc_stderr\": 0.02383930331139819,\n \"acc_norm\": 0.7717041800643086,\n\ \ \"acc_norm_stderr\": 0.02383930331139819\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262185,\n\ \ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262185\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5425531914893617,\n \"acc_stderr\": 0.029719281272236844,\n \ \ \"acc_norm\": 0.5425531914893617,\n \"acc_norm_stderr\": 0.029719281272236844\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5338983050847458,\n\ \ \"acc_stderr\": 0.012740853872949839,\n \"acc_norm\": 0.5338983050847458,\n\ \ \"acc_norm_stderr\": 0.012740853872949839\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7132352941176471,\n \"acc_stderr\": 0.027472274473233818,\n\ \ \"acc_norm\": 0.7132352941176471,\n \"acc_norm_stderr\": 0.027472274473233818\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7483660130718954,\n \"acc_stderr\": 0.01755581809132227,\n \ \ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.01755581809132227\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139968,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139968\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8656716417910447,\n\ \ \"acc_stderr\": 0.024112678240900826,\n \"acc_norm\": 0.8656716417910447,\n\ \ \"acc_norm_stderr\": 0.024112678240900826\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \ \ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\ \ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\ \ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061445,\n\ \ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061445\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41370869033047736,\n\ \ \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.5644074616941972,\n\ \ \"mc2_stderr\": 0.015397066221595713\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7987371744277821,\n \"acc_stderr\": 0.011268519971577684\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.604245640636846,\n \ \ \"acc_stderr\": 0.013469823701048815\n }\n}\n```" repo_url: https://huggingface.co/AA051610/A11P leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|arc:challenge|25_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-11T02-59-53.573351.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|gsm8k|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hellaswag|10_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-11T02-59-53.573351.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-11T02-59-53.573351.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-11T02-59-53.573351.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_11T02_59_53.573351 path: - '**/details_harness|winogrande|5_2023-12-11T02-59-53.573351.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-11T02-59-53.573351.parquet' - config_name: results data_files: - split: 2023_12_11T02_59_53.573351 path: - results_2023-12-11T02-59-53.573351.parquet - split: latest path: - results_2023-12-11T02-59-53.573351.parquet --- # Dataset Card for Evaluation run of AA051610/A11P ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/AA051610/A11P - **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 [AA051610/A11P](https://huggingface.co/AA051610/A11P) 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_AA051610__A11P", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-11T02:59:53.573351](https://huggingface.co/datasets/open-llm-leaderboard/details_AA051610__A11P/blob/main/results_2023-12-11T02-59-53.573351.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": 0.7024107525999363, "acc_stderr": 0.030362293861859797, "acc_norm": 0.7062972608094896, "acc_norm_stderr": 0.030951825247607496, "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.5644074616941972, "mc2_stderr": 0.015397066221595713 }, "harness|arc:challenge|25": { "acc": 0.6040955631399317, "acc_stderr": 0.014291228393536587, "acc_norm": 0.6254266211604096, "acc_norm_stderr": 0.014144193471893449 }, "harness|hellaswag|10": { "acc": 0.6191993626767576, "acc_stderr": 0.004845912857338663, "acc_norm": 0.8253335988846843, "acc_norm_stderr": 0.003789055487003176 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6148148148148148, "acc_stderr": 0.04203921040156279, "acc_norm": 0.6148148148148148, "acc_norm_stderr": 0.04203921040156279 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8355263157894737, "acc_stderr": 0.030167533468632726, "acc_norm": 0.8355263157894737, "acc_norm_stderr": 0.030167533468632726 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7358490566037735, "acc_stderr": 0.027134291628741706, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.027134291628741706 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7916666666666666, "acc_stderr": 0.03396116205845335, "acc_norm": 0.7916666666666666, "acc_norm_stderr": 0.03396116205845335 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145633, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145633 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6242774566473989, "acc_stderr": 0.036928207672648664, "acc_norm": 0.6242774566473989, "acc_norm_stderr": 0.036928207672648664 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4411764705882353, "acc_stderr": 0.049406356306056595, "acc_norm": 0.4411764705882353, "acc_norm_stderr": 0.049406356306056595 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.8, "acc_stderr": 0.040201512610368445, "acc_norm": 0.8, "acc_norm_stderr": 0.040201512610368445 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.7063829787234043, "acc_stderr": 0.02977164271249123, "acc_norm": 0.7063829787234043, "acc_norm_stderr": 0.02977164271249123 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5526315789473685, "acc_stderr": 0.04677473004491199, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.04677473004491199 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7310344827586207, "acc_stderr": 0.036951833116502325, "acc_norm": 0.7310344827586207, "acc_norm_stderr": 0.036951833116502325 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.5476190476190477, "acc_stderr": 0.025634258115554955, "acc_norm": 0.5476190476190477, "acc_norm_stderr": 0.025634258115554955 }, "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": 0.46, "acc_stderr": 0.05009082659620332, "acc_norm": 0.46, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8548387096774194, "acc_stderr": 0.02003956362805328, "acc_norm": 0.8548387096774194, "acc_norm_stderr": 0.02003956362805328 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5517241379310345, "acc_stderr": 0.034991131376767445, "acc_norm": 0.5517241379310345, "acc_norm_stderr": 0.034991131376767445 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.73, "acc_stderr": 0.044619604333847394, "acc_norm": 0.73, "acc_norm_stderr": 0.044619604333847394 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.806060606060606, "acc_stderr": 0.03087414513656208, "acc_norm": 0.806060606060606, "acc_norm_stderr": 0.03087414513656208 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8787878787878788, "acc_stderr": 0.02325315795194208, "acc_norm": 0.8787878787878788, "acc_norm_stderr": 0.02325315795194208 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9222797927461139, "acc_stderr": 0.019321805557223144, "acc_norm": 0.9222797927461139, "acc_norm_stderr": 0.019321805557223144 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7692307692307693, "acc_stderr": 0.02136202772522272, "acc_norm": 0.7692307692307693, "acc_norm_stderr": 0.02136202772522272 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3592592592592593, "acc_stderr": 0.029252905927251976, "acc_norm": 0.3592592592592593, "acc_norm_stderr": 0.029252905927251976 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7857142857142857, "acc_stderr": 0.026653531596715484, "acc_norm": 0.7857142857142857, "acc_norm_stderr": 0.026653531596715484 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.40397350993377484, "acc_stderr": 0.040064856853653415, "acc_norm": 0.40397350993377484, "acc_norm_stderr": 0.040064856853653415 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8825688073394495, "acc_stderr": 0.01380278022737734, "acc_norm": 0.8825688073394495, "acc_norm_stderr": 0.01380278022737734 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5277777777777778, "acc_stderr": 0.0340470532865388, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8970588235294118, "acc_stderr": 0.021328337570804365, "acc_norm": 0.8970588235294118, "acc_norm_stderr": 0.021328337570804365 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8734177215189873, "acc_stderr": 0.021644195727955173, "acc_norm": 0.8734177215189873, "acc_norm_stderr": 0.021644195727955173 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7802690582959642, "acc_stderr": 0.02779017706438359, "acc_norm": 0.7802690582959642, "acc_norm_stderr": 0.02779017706438359 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8549618320610687, "acc_stderr": 0.03088466108951539, "acc_norm": 0.8549618320610687, "acc_norm_stderr": 0.03088466108951539 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8703703703703703, "acc_stderr": 0.032472243899179465, "acc_norm": 0.8703703703703703, "acc_norm_stderr": 0.032472243899179465 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8282208588957055, "acc_stderr": 0.029634717272371047, "acc_norm": 0.8282208588957055, "acc_norm_stderr": 0.029634717272371047 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6160714285714286, "acc_stderr": 0.04616143075028546, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.04616143075028546 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.0376017800602662, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.0376017800602662 }, "harness|hendrycksTest-marketing|5": { "acc": 0.905982905982906, "acc_stderr": 0.019119892798924978, "acc_norm": 0.905982905982906, "acc_norm_stderr": 0.019119892798924978 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8850574712643678, "acc_stderr": 0.01140572072459397, "acc_norm": 0.8850574712643678, "acc_norm_stderr": 0.01140572072459397 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7774566473988439, "acc_stderr": 0.02239421566194282, "acc_norm": 0.7774566473988439, "acc_norm_stderr": 0.02239421566194282 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.42793296089385474, "acc_stderr": 0.01654788799741611, "acc_norm": 0.42793296089385474, "acc_norm_stderr": 0.01654788799741611 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7810457516339869, "acc_stderr": 0.02367908986180772, "acc_norm": 0.7810457516339869, "acc_norm_stderr": 0.02367908986180772 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7717041800643086, "acc_stderr": 0.02383930331139819, "acc_norm": 0.7717041800643086, "acc_norm_stderr": 0.02383930331139819 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7808641975308642, "acc_stderr": 0.023016705640262185, "acc_norm": 0.7808641975308642, "acc_norm_stderr": 0.023016705640262185 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5425531914893617, "acc_stderr": 0.029719281272236844, "acc_norm": 0.5425531914893617, "acc_norm_stderr": 0.029719281272236844 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5338983050847458, "acc_stderr": 0.012740853872949839, "acc_norm": 0.5338983050847458, "acc_norm_stderr": 0.012740853872949839 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7132352941176471, "acc_stderr": 0.027472274473233818, "acc_norm": 0.7132352941176471, "acc_norm_stderr": 0.027472274473233818 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7483660130718954, "acc_stderr": 0.01755581809132227, "acc_norm": 0.7483660130718954, "acc_norm_stderr": 0.01755581809132227 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7181818181818181, "acc_stderr": 0.043091187099464585, "acc_norm": 0.7181818181818181, "acc_norm_stderr": 0.043091187099464585 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.746938775510204, "acc_stderr": 0.02783302387139968, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.02783302387139968 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8656716417910447, "acc_stderr": 0.024112678240900826, "acc_norm": 0.8656716417910447, "acc_norm_stderr": 0.024112678240900826 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.02876234912646613, "acc_norm": 0.91, "acc_norm_stderr": 0.02876234912646613 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.038695433234721015, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.038695433234721015 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.847953216374269, "acc_stderr": 0.027539122889061445, "acc_norm": 0.847953216374269, "acc_norm_stderr": 0.027539122889061445 }, "harness|truthfulqa:mc|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.5644074616941972, "mc2_stderr": 0.015397066221595713 }, "harness|winogrande|5": { "acc": 0.7987371744277821, "acc_stderr": 0.011268519971577684 }, "harness|gsm8k|5": { "acc": 0.604245640636846, "acc_stderr": 0.013469823701048815 } } ``` ### 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]

kgr123/quality_counter_2000

--- dataset_info: features: - name: context dtype: string - name: word dtype: string - name: claim dtype: string - name: label dtype: int64 splits: - name: test num_bytes: 11265605 num_examples: 1929 - name: train num_bytes: 11155926 num_examples: 1935 - name: validation num_bytes: 11367894 num_examples: 1941 download_size: 7642748 dataset_size: 33789425 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* ---

mstz/fairbelief

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

distilled-from-one-sec-cv12/chunk_17

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1420681296 num_examples: 276828 download_size: 1451360616 dataset_size: 1420681296 --- # Dataset Card for "chunk_17" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TIGER-Lab/Subject_Driven_Image_Editing

--- dataset_info: features: - name: uid dtype: int64 - name: image dtype: image - name: subject dtype: string - name: subject_image_0 dtype: image - name: subject_image_1 dtype: image - name: subject_image_2 dtype: image splits: - name: eval num_bytes: 154799894.0 num_examples: 154 - name: extra num_bytes: 66230300.0 num_examples: 66 download_size: 49158277 dataset_size: 221030194.0 --- # Dataset Card for "Subject_Driven_Image_Editing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Doutran/myllinhaset

--- license: openrail ---

open-llm-leaderboard/details_BarryFutureman__ChatMarc-YesAnotherMerge-7B

--- pretty_name: Evaluation run of BarryFutureman/ChatMarc-YesAnotherMerge-7B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [BarryFutureman/ChatMarc-YesAnotherMerge-7B](https://huggingface.co/BarryFutureman/ChatMarc-YesAnotherMerge-7B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 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_BarryFutureman__ChatMarc-YesAnotherMerge-7B\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-01-24T05:34:18.479696](https://huggingface.co/datasets/open-llm-leaderboard/details_BarryFutureman__ChatMarc-YesAnotherMerge-7B/blob/main/results_2024-01-24T05-34-18.479696.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\": 0.6560146863596611,\n\ \ \"acc_stderr\": 0.03206476346441959,\n \"acc_norm\": 0.6553348227714214,\n\ \ \"acc_norm_stderr\": 0.03273507303552595,\n \"mc1\": 0.5618115055079559,\n\ \ \"mc1_stderr\": 0.017369236164404406,\n \"mc2\": 0.700398174715358,\n\ \ \"mc2_stderr\": 0.015160702701664436\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.7022184300341296,\n \"acc_stderr\": 0.013363080107244484,\n\ \ \"acc_norm\": 0.7278156996587031,\n \"acc_norm_stderr\": 0.013006600406423702\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7228639713204541,\n\ \ \"acc_stderr\": 0.004466695023677836,\n \"acc_norm\": 0.8838876717785302,\n\ \ \"acc_norm_stderr\": 0.003197048476003638\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\": 0.6444444444444445,\n\ \ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\ \ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\ \ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\ \ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \ \ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.7169811320754716,\n \"acc_stderr\": 0.027724236492700914,\n\ \ \"acc_norm\": 0.7169811320754716,\n \"acc_norm_stderr\": 0.027724236492700914\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\ \ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\ \ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"\ acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\ \ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\ \ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\ \ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n\ \ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\ \ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\ \ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \ \ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\ \ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\ acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\ \ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\ \ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \ \ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\ \ \"acc_stderr\": 0.023904914311782655,\n \"acc_norm\": 0.7709677419354839,\n\ \ \"acc_norm_stderr\": 0.023904914311782655\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\ \ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\ : 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\ \ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\ acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9119170984455959,\n \"acc_stderr\": 0.02045374660160103,\n\ \ \"acc_norm\": 0.9119170984455959,\n \"acc_norm_stderr\": 0.02045374660160103\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \ \ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \ \ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.03068473711513536,\n \ \ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.03068473711513536\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\"\ : 0.8440366972477065,\n \"acc_stderr\": 0.015555802713590172,\n \"\ acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.015555802713590172\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455335,\n \"\ acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455335\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \ \ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\ \ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\ \ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\ \ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\ acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\ \ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\ \ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n\ \ \"acc_stderr\": 0.046695106638751906,\n \"acc_norm\": 0.4107142857142857,\n\ \ \"acc_norm_stderr\": 0.046695106638751906\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\ \ \"acc_stderr\": 0.021262719400406974,\n \"acc_norm\": 0.8803418803418803,\n\ \ \"acc_norm_stderr\": 0.021262719400406974\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \ \ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8339719029374202,\n\ \ \"acc_stderr\": 0.0133064782430663,\n \"acc_norm\": 0.8339719029374202,\n\ \ \"acc_norm_stderr\": 0.0133064782430663\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\ \ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4335195530726257,\n\ \ \"acc_stderr\": 0.016574027219517635,\n \"acc_norm\": 0.4335195530726257,\n\ \ \"acc_norm_stderr\": 0.016574027219517635\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\ \ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\ \ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\ \ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042103,\n\ \ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042103\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5141843971631206,\n \"acc_stderr\": 0.02981549448368206,\n \ \ \"acc_norm\": 0.5141843971631206,\n \"acc_norm_stderr\": 0.02981549448368206\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47131681877444587,\n\ \ \"acc_stderr\": 0.012749206007657476,\n \"acc_norm\": 0.47131681877444587,\n\ \ \"acc_norm_stderr\": 0.012749206007657476\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\ \ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \ \ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\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\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\ \ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\ \ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\ \ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \ \ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\ \ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\ \ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640044,\n\ \ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640044\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5618115055079559,\n\ \ \"mc1_stderr\": 0.017369236164404406,\n \"mc2\": 0.700398174715358,\n\ \ \"mc2_stderr\": 0.015160702701664436\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785722\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6997725549658832,\n \ \ \"acc_stderr\": 0.012625423152283034\n }\n}\n```" repo_url: https://huggingface.co/BarryFutureman/ChatMarc-YesAnotherMerge-7B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|arc:challenge|25_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-01-24T05-34-18.479696.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|gsm8k|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hellaswag|10_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-management|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-01-24T05-34-18.479696.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-management|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-virology|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T05-34-18.479696.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|truthfulqa:mc|0_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-01-24T05-34-18.479696.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_01_24T05_34_18.479696 path: - '**/details_harness|winogrande|5_2024-01-24T05-34-18.479696.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-01-24T05-34-18.479696.parquet' - config_name: results data_files: - split: 2024_01_24T05_34_18.479696 path: - results_2024-01-24T05-34-18.479696.parquet - split: latest path: - results_2024-01-24T05-34-18.479696.parquet --- # Dataset Card for Evaluation run of BarryFutureman/ChatMarc-YesAnotherMerge-7B  Dataset automatically created during the evaluation run of model [BarryFutureman/ChatMarc-YesAnotherMerge-7B](https://huggingface.co/BarryFutureman/ChatMarc-YesAnotherMerge-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 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_BarryFutureman__ChatMarc-YesAnotherMerge-7B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-24T05:34:18.479696](https://huggingface.co/datasets/open-llm-leaderboard/details_BarryFutureman__ChatMarc-YesAnotherMerge-7B/blob/main/results_2024-01-24T05-34-18.479696.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": 0.6560146863596611, "acc_stderr": 0.03206476346441959, "acc_norm": 0.6553348227714214, "acc_norm_stderr": 0.03273507303552595, "mc1": 0.5618115055079559, "mc1_stderr": 0.017369236164404406, "mc2": 0.700398174715358, "mc2_stderr": 0.015160702701664436 }, "harness|arc:challenge|25": { "acc": 0.7022184300341296, "acc_stderr": 0.013363080107244484, "acc_norm": 0.7278156996587031, "acc_norm_stderr": 0.013006600406423702 }, "harness|hellaswag|10": { "acc": 0.7228639713204541, "acc_stderr": 0.004466695023677836, "acc_norm": 0.8838876717785302, "acc_norm_stderr": 0.003197048476003638 }, "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": 0.6444444444444445, "acc_stderr": 0.04135176749720385, "acc_norm": 0.6444444444444445, "acc_norm_stderr": 0.04135176749720385 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.66, "acc_stderr": 0.04760952285695238, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695238 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7169811320754716, "acc_stderr": 0.027724236492700914, "acc_norm": 0.7169811320754716, "acc_norm_stderr": 0.027724236492700914 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7847222222222222, "acc_stderr": 0.03437079344106135, "acc_norm": 0.7847222222222222, "acc_norm_stderr": 0.03437079344106135 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6763005780346821, "acc_stderr": 0.0356760379963917, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.0356760379963917 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4215686274509804, "acc_stderr": 0.04913595201274498, "acc_norm": 0.4215686274509804, "acc_norm_stderr": 0.04913595201274498 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909283, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5829787234042553, "acc_stderr": 0.03223276266711712, "acc_norm": 0.5829787234042553, "acc_norm_stderr": 0.03223276266711712 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5862068965517241, "acc_stderr": 0.04104269211806232, "acc_norm": 0.5862068965517241, "acc_norm_stderr": 0.04104269211806232 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.42328042328042326, "acc_stderr": 0.02544636563440678, "acc_norm": 0.42328042328042326, "acc_norm_stderr": 0.02544636563440678 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7709677419354839, "acc_stderr": 0.023904914311782655, "acc_norm": 0.7709677419354839, "acc_norm_stderr": 0.023904914311782655 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5073891625615764, "acc_stderr": 0.035176035403610105, "acc_norm": 0.5073891625615764, "acc_norm_stderr": 0.035176035403610105 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7878787878787878, "acc_stderr": 0.03192271569548301, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.03192271569548301 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7878787878787878, "acc_stderr": 0.029126522834586815, "acc_norm": 0.7878787878787878, "acc_norm_stderr": 0.029126522834586815 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9119170984455959, "acc_stderr": 0.02045374660160103, "acc_norm": 0.9119170984455959, "acc_norm_stderr": 0.02045374660160103 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6794871794871795, "acc_stderr": 0.02366129639396428, "acc_norm": 0.6794871794871795, "acc_norm_stderr": 0.02366129639396428 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.02897264888484427, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.02897264888484427 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6638655462184874, "acc_stderr": 0.03068473711513536, "acc_norm": 0.6638655462184874, "acc_norm_stderr": 0.03068473711513536 }, "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": 0.8440366972477065, "acc_stderr": 0.015555802713590172, "acc_norm": 0.8440366972477065, "acc_norm_stderr": 0.015555802713590172 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5046296296296297, "acc_stderr": 0.03409825519163572, "acc_norm": 0.5046296296296297, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8431372549019608, "acc_stderr": 0.02552472232455335, "acc_norm": 0.8431372549019608, "acc_norm_stderr": 0.02552472232455335 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8016877637130801, "acc_stderr": 0.02595502084162113, "acc_norm": 0.8016877637130801, "acc_norm_stderr": 0.02595502084162113 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.695067264573991, "acc_stderr": 0.030898610882477515, "acc_norm": 0.695067264573991, "acc_norm_stderr": 0.030898610882477515 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7933884297520661, "acc_stderr": 0.03695980128098824, "acc_norm": 0.7933884297520661, "acc_norm_stderr": 0.03695980128098824 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7870370370370371, "acc_stderr": 0.0395783547198098, "acc_norm": 0.7870370370370371, "acc_norm_stderr": 0.0395783547198098 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.046695106638751906, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.046695106638751906 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8803418803418803, "acc_stderr": 0.021262719400406974, "acc_norm": 0.8803418803418803, "acc_norm_stderr": 0.021262719400406974 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8339719029374202, "acc_stderr": 0.0133064782430663, "acc_norm": 0.8339719029374202, "acc_norm_stderr": 0.0133064782430663 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7398843930635838, "acc_stderr": 0.023618678310069363, "acc_norm": 0.7398843930635838, "acc_norm_stderr": 0.023618678310069363 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4335195530726257, "acc_stderr": 0.016574027219517635, "acc_norm": 0.4335195530726257, "acc_norm_stderr": 0.016574027219517635 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7156862745098039, "acc_stderr": 0.02582916327275748, "acc_norm": 0.7156862745098039, "acc_norm_stderr": 0.02582916327275748 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7530864197530864, "acc_stderr": 0.023993501709042103, "acc_norm": 0.7530864197530864, "acc_norm_stderr": 0.023993501709042103 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5141843971631206, "acc_stderr": 0.02981549448368206, "acc_norm": 0.5141843971631206, "acc_norm_stderr": 0.02981549448368206 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.47131681877444587, "acc_stderr": 0.012749206007657476, "acc_norm": 0.47131681877444587, "acc_norm_stderr": 0.012749206007657476 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6727941176470589, "acc_stderr": 0.028501452860396556, "acc_norm": 0.6727941176470589, "acc_norm_stderr": 0.028501452860396556 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6813725490196079, "acc_stderr": 0.01885008469646872, "acc_norm": 0.6813725490196079, "acc_norm_stderr": 0.01885008469646872 }, "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": 0.746938775510204, "acc_stderr": 0.027833023871399677, "acc_norm": 0.746938775510204, "acc_norm_stderr": 0.027833023871399677 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8308457711442786, "acc_stderr": 0.02650859065623327, "acc_norm": 0.8308457711442786, "acc_norm_stderr": 0.02650859065623327 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.85, "acc_stderr": 0.03588702812826371, "acc_norm": 0.85, "acc_norm_stderr": 0.03588702812826371 }, "harness|hendrycksTest-virology|5": { "acc": 0.5481927710843374, "acc_stderr": 0.03874371556587953, "acc_norm": 0.5481927710843374, "acc_norm_stderr": 0.03874371556587953 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.029547741687640044, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.029547741687640044 }, "harness|truthfulqa:mc|0": { "mc1": 0.5618115055079559, "mc1_stderr": 0.017369236164404406, "mc2": 0.700398174715358, "mc2_stderr": 0.015160702701664436 }, "harness|winogrande|5": { "acc": 0.8389897395422258, "acc_stderr": 0.010329712832785722 }, "harness|gsm8k|5": { "acc": 0.6997725549658832, "acc_stderr": 0.012625423152283034 } } ``` ## 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]

coastalcph/fm-updates-llama2-chat-7b

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: query struct: - name: label dtype: string - name: objects list: - name: aliases sequence: string - name: label dtype: string - name: qid dtype: string - name: qid dtype: string - name: rel_id dtype: string - name: relation dtype: string - name: prediction struct: - name: predictions list: - name: answer dtype: string - name: first_token_probability dtype: float64 - name: per_token_probability sequence: float64 - name: perplexity dtype: float64 - name: query dtype: string - name: f1 dtype: float64 - name: relation dtype: string - name: type dtype: string - name: original_answer dtype: string - name: updates sequence: string splits: - name: test num_bytes: 2983210.8077126252 num_examples: 6414 download_size: 1236982 dataset_size: 2983210.8077126252 --- # Dataset Card for "fm-updates-llama2-chat-7b" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

intanm/indonesian-financial-topic-classification-dataset

--- license: apache-2.0 task_categories: - text-classification language: - id tags: - finance size_categories: - 10K<n<100K --- Translated version of https://huggingface.co/datasets/zeroshot/twitter-financial-news-topic topics = { "LABEL_0": "Analyst Update", "LABEL_1": "Fed | Central Banks", "LABEL_2": "Company | Product News", "LABEL_3": "Treasuries | Corporate Debt", "LABEL_4": "Dividend", "LABEL_5": "Earnings", "LABEL_6": "Energy | Oil", "LABEL_7": "Financials", "LABEL_8": "Currencies", "LABEL_9": "General News | Opinion", "LABEL_10": "Gold | Metals | Materials", "LABEL_11": "IPO", "LABEL_12": "Legal | Regulation", "LABEL_13": "M&A | Investments", "LABEL_14": "Macro", "LABEL_15": "Markets", "LABEL_16": "Politics", "LABEL_17": "Personnel Change", "LABEL_18": "Stock Commentary", "LABEL_19": "Stock Movement", }

argilla/ultrafeedback_binarized_full

--- dataset_info: features: - name: source dtype: string - name: instruction dtype: string - name: best_response struct: - name: annotations struct: - name: helpfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: honesty struct: - name: Rating dtype: string - name: Rationale dtype: string - name: instruction_following struct: - name: Rating dtype: string - name: Rationale dtype: string - name: truthfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: critique dtype: string - name: custom_system_prompt dtype: string - name: model dtype: string - name: overall_score dtype: float64 - name: principle dtype: string - name: response dtype: string - name: best_model dtype: string - name: best_score dtype: float64 - name: random_response struct: - name: annotations struct: - name: helpfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: honesty struct: - name: Rating dtype: string - name: Rationale dtype: string - name: instruction_following struct: - name: Rating dtype: string - name: Rationale dtype: string - name: truthfulness struct: - name: Rating dtype: string - name: Rationale dtype: string - name: Rationale For Rating dtype: string - name: Type sequence: string - name: critique dtype: string - name: custom_system_prompt dtype: string - name: model dtype: string - name: overall_score dtype: float64 - name: principle dtype: string - name: response dtype: string - name: random_model dtype: string - name: random_score dtype: float64 - name: correct_answers sequence: string - name: incorrect_answers sequence: string splits: - name: train num_bytes: 447221757 num_examples: 63967 download_size: 199896433 dataset_size: 447221757 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "ultrafeedback_binarized_full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yangyz1230/H3K79me3_not_filtered

--- dataset_info: features: - name: name dtype: string - name: sequence dtype: string - name: chrom dtype: string - name: start dtype: int64 - name: end dtype: int64 - name: strand dtype: string - name: label dtype: int64 splits: - name: train num_bytes: 439373 num_examples: 799 - name: test num_bytes: 44840 num_examples: 82 download_size: 232580 dataset_size: 484213 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

irds/beir_msmarco

--- pretty_name: '`beir/msmarco`' viewer: false source_datasets: [] task_categories: - text-retrieval --- # Dataset Card for `beir/msmarco` The `beir/msmarco` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package. For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/msmarco). # Data This dataset provides: - `docs` (documents, i.e., the corpus); count=8,841,823 - `queries` (i.e., topics); count=509,962 This dataset is used by: [`beir_msmarco_dev`](https://huggingface.co/datasets/irds/beir_msmarco_dev), [`beir_msmarco_test`](https://huggingface.co/datasets/irds/beir_msmarco_test), [`beir_msmarco_train`](https://huggingface.co/datasets/irds/beir_msmarco_train) ## Usage ```python from datasets import load_dataset docs = load_dataset('irds/beir_msmarco', 'docs') for record in docs: record # {'doc_id': ..., 'text': ...} queries = load_dataset('irds/beir_msmarco', 'queries') for record in queries: record # {'query_id': ..., 'text': ...} ``` Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the data in 🤗 Dataset format. ## Citation Information ``` @inproceedings{Bajaj2016Msmarco, title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset}, author={Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang}, booktitle={InCoCo@NIPS}, year={2016} } @article{Thakur2021Beir, title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models", author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna", journal= "arXiv preprint arXiv:2104.08663", month = "4", year = "2021", url = "https://arxiv.org/abs/2104.08663", } ```

ConvLab/sgd2

--- language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: SGD-X v2 size_categories: - 10K<n<100K task_categories: - conversational --- # Dataset Card for SGD-X v2 - **Repository:** https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/tree/master/sgd_x - **Paper:** https://arxiv.org/pdf/2110.06800.pdf - **Leaderboard:** None - **Who transforms the dataset:** Qi Zhu(zhuq96 at gmail dot com) To use this dataset, you need to install [ConvLab-3](https://github.com/ConvLab/ConvLab-3) platform first. Then you can load the dataset via: ``` from convlab.util import load_dataset, load_ontology, load_database dataset = load_dataset('sgd2') ontology = load_ontology('sgd2') database = load_database('sgd2') ``` For more usage please refer to [here](https://github.com/ConvLab/ConvLab-3/tree/master/data/unified_datasets). ### Dataset Summary The **Schema-Guided Dialogue (SGD)** dataset consists of over 20k annotated multi-domain, task-oriented conversations between a human and a virtual assistant. These conversations involve interactions with services and APIs spanning 20 domains, such as banks, events, media, calendar, travel, and weather. For most of these domains, the dataset contains multiple different APIs, many of which have overlapping functionalities but different interfaces, which reflects common real-world scenarios. The wide range of available annotations can be used for intent prediction, slot filling, dialogue state tracking, policy imitation learning, language generation, and user simulation learning, among other tasks for developing large-scale virtual assistants. Additionally, the dataset contains unseen domains and services in the evaluation set to quantify the performance in zero-shot or few-shot settings. The **SGD-X** dataset consists of 5 linguistic variants of every schema in the original SGD dataset. Linguistic variants were written by hundreds of paid crowd-workers. In the SGD-X directory, v1 represents the variant closest to the original schemas and v5 the farthest in terms of linguistic distance. To evaluate model performance on SGD-X schemas, dialogues must be converted using the script generate_sgdx_dialogues.py. - **How to get the transformed data from original data:** - Download [dstc8-schema-guided-dialogue-master.zip](https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/archive/refs/heads/master.zip). - Modified `sgd_x/generate_sgdx_dialogues.py` as https://github.com/google-research-datasets/dstc8-schema-guided-dialogue/issues/57 - Run `python -m sgd_x.generate_sgdx_dialogues` under `dstc8-schema-guided-dialogue-master` dir which need tensorflow installed. - Run `python preprocess.py` in the current directory. - **Main changes of the transformation:** - Lower case original `act` as `intent`. - Add `count` slot for each domain, non-categorical, find span by text matching. - Categorize `dialogue acts` according to the `intent`. - Concatenate multiple values using `|`. - Retain `active_intent`, `requested_slots`, `service_call`. - **Annotations:** - dialogue acts, state, db_results, service_call, active_intent, requested_slots. ### Supported Tasks and Leaderboards NLU, DST, Policy, NLG, E2E ### Languages English ### Data Splits | split | dialogues | utterances | avg_utt | avg_tokens | avg_domains | cat slot match(state) | cat slot match(goal) | cat slot match(dialogue act) | non-cat slot span(dialogue act) | |------------|-------------|--------------|-----------|--------------|---------------|-------------------------|------------------------|--------------------------------|-----------------------------------| | train | 16142 | 329964 | 20.44 | 9.75 | 1.84 | 100 | - | 100 | 100 | | validation | 2482 | 48726 | 19.63 | 9.66 | 1.84 | 100 | - | 100 | 100 | | test | 4201 | 84594 | 20.14 | 10.4 | 2.02 | 100 | - | 100 | 100 | | all | 22825 | 463284 | 20.3 | 9.86 | 1.87 | 100 | - | 100 | 100 | 45 domains: ['Banks_12', 'Buses_12', 'Buses_22', 'Calendar_12', 'Events_12', 'Events_22', 'Flights_12', 'Flights_22', 'Homes_12', 'Hotels_12', 'Hotels_22', 'Hotels_32', 'Media_12', 'Movies_12', 'Music_12', 'Music_22', 'RentalCars_12', 'RentalCars_22', 'Restaurants_12', 'RideSharing_12', 'RideSharing_22', 'Services_12', 'Services_22', 'Services_32', 'Travel_12', 'Weather_12', 'Alarm_12', 'Banks_22', 'Flights_32', 'Hotels_42', 'Media_22', 'Movies_22', 'Restaurants_22', 'Services_42', 'Buses_32', 'Events_32', 'Flights_42', 'Homes_22', 'Media_32', 'Messaging_12', 'Movies_32', 'Music_32', 'Payment_12', 'RentalCars_32', 'Trains_12'] - **cat slot match**: how many values of categorical slots are in the possible values of ontology in percentage. - **non-cat slot span**: how many values of non-categorical slots have span annotation in percentage. ### Citation ``` @inproceedings{lee2022sgd, title={SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems}, author={Lee, Harrison and Gupta, Raghav and Rastogi, Abhinav and Cao, Yuan and Zhang, Bin and Wu, Yonghui}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={36}, number={10}, pages={10938--10946}, year={2022} } ``` ### Licensing Information [**CC BY-SA 4.0**](https://creativecommons.org/licenses/by-sa/4.0/)

g-ronimo/oasst2_top1_en_answers-mixtral

--- dataset_info: features: - name: conversation list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 14166584 num_examples: 5419 download_size: 7059605 dataset_size: 14166584 configs: - config_name: default data_files: - split: train path: data/train-* license: apache-2.0 tags: - synthetic --- * Top 1% conversations of https://huggingface.co/datasets/OpenAssistant/oasst2 * language-filtered: en * generated using https://github.com/blancsw/deep_4_all/blob/main/datasets/oasst/convert.py * assistant answers replaced with answers by [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) * _Note_: This is an unfiltered dataset, it for sure contains very bad answers.

distilled-from-one-sec-cv12/chunk_79

--- dataset_info: features: - name: logits sequence: float32 - name: mfcc sequence: sequence: float64 splits: - name: train num_bytes: 1303912900 num_examples: 254075 download_size: 1332936637 dataset_size: 1303912900 --- # Dataset Card for "chunk_79" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

amitness/logits-maltese-512

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: input_ids sequence: int32 - name: token_type_ids sequence: int8 - name: attention_mask sequence: int8 - name: labels sequence: int64 - name: teacher_logits sequence: sequence: float64 - name: teacher_indices sequence: sequence: int64 - name: teacher_mask_indices sequence: int64 splits: - name: train num_bytes: 230200052 num_examples: 12655 download_size: 84312982 dataset_size: 230200052 --- # Dataset Card for "logits-maltese-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Vishwanath0912/qa_en_hi

--- license: mit ---

sumitpardhiya/Face-Mask-Detection

--- license: apache-2.0 --- The dataset contains three folders: Train, Test, and Validation. Each of these folders includes two subfolders—one for mask images and another for images without masks.

EleutherAI/quirky_modularaddition_increment0_bob_hard

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: alice_label dtype: bool - name: bob_label dtype: bool - name: difficulty dtype: int64 - name: statement dtype: string - name: choices sequence: string - name: character dtype: string - name: label dtype: bool splits: - name: train num_bytes: 3563112.95803125 num_examples: 48087 - name: validation num_bytes: 75436.0905 num_examples: 1018 - name: test num_bytes: 73418.235 num_examples: 991 download_size: 1104505 dataset_size: 3711967.28353125 --- # Dataset Card for "quirky_modularaddition_increment0_bob_hard" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kenilshah35/dictation-test

--- dataset_info: features: - name: audio dtype: audio - name: text dtype: string splits: - name: train num_bytes: 8894736.0 num_examples: 19 download_size: 4493848 dataset_size: 8894736.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

StanBienaives/jade-considerants

--- language: - fr ---

open-llm-leaderboard/details_vistagi__Mixtral-8x7b-v0.1-dpo

--- pretty_name: Evaluation run of vistagi/Mixtral-8x7b-v0.1-dpo dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [vistagi/Mixtral-8x7b-v0.1-dpo](https://huggingface.co/vistagi/Mixtral-8x7b-v0.1-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_vistagi__Mixtral-8x7b-v0.1-dpo\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-02-18T06:28:07.647994](https://huggingface.co/datasets/open-llm-leaderboard/details_vistagi__Mixtral-8x7b-v0.1-dpo/blob/main/results_2024-02-18T06-28-07.647994.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\": 0.7134552339034452,\n\ \ \"acc_stderr\": 0.030055997546363594,\n \"acc_norm\": 0.7181597948300631,\n\ \ \"acc_norm_stderr\": 0.030631631253278484,\n \"mc1\": 0.31456548347613217,\n\ \ \"mc1_stderr\": 0.016255241993179185,\n \"mc2\": 0.4674384125733044,\n\ \ \"mc2_stderr\": 0.01414272854245227\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6322525597269625,\n \"acc_stderr\": 0.01409099561816849,\n\ \ \"acc_norm\": 0.6655290102389079,\n \"acc_norm_stderr\": 0.013787460322441374\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6694881497709619,\n\ \ \"acc_stderr\": 0.004694360968929403,\n \"acc_norm\": 0.8639713204540929,\n\ \ \"acc_norm_stderr\": 0.0034211839093201673\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \ \ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6962962962962963,\n\ \ \"acc_stderr\": 0.03972552884785137,\n \"acc_norm\": 0.6962962962962963,\n\ \ \"acc_norm_stderr\": 0.03972552884785137\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.031103182383123387,\n\ \ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.031103182383123387\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.7886792452830189,\n \"acc_stderr\": 0.025125766484827845,\n\ \ \"acc_norm\": 0.7886792452830189,\n \"acc_norm_stderr\": 0.025125766484827845\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8472222222222222,\n\ \ \"acc_stderr\": 0.030085743248565666,\n \"acc_norm\": 0.8472222222222222,\n\ \ \"acc_norm_stderr\": 0.030085743248565666\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\ : 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\ \ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \ \ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \ \ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n\ \ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\ \ \"acc_norm_stderr\": 0.03476599607516478\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.049512182523962625,\n\ \ \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.049512182523962625\n\ \ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\ \ 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n\ \ \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\ : {\n \"acc\": 0.6808510638297872,\n \"acc_stderr\": 0.03047297336338004,\n\ \ \"acc_norm\": 0.6808510638297872,\n \"acc_norm_stderr\": 0.03047297336338004\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6491228070175439,\n\ \ \"acc_stderr\": 0.04489539350270698,\n \"acc_norm\": 0.6491228070175439,\n\ \ \"acc_norm_stderr\": 0.04489539350270698\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03960933549451208,\n\ \ \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03960933549451208\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.47619047619047616,\n \"acc_stderr\": 0.025722097064388525,\n \"\ acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.025722097064388525\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5634920634920635,\n\ \ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.5634920634920635,\n\ \ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \ \ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.8354838709677419,\n \"acc_stderr\": 0.021090847745939313,\n \"\ acc_norm\": 0.8354838709677419,\n \"acc_norm_stderr\": 0.021090847745939313\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.6354679802955665,\n \"acc_stderr\": 0.0338640574606209,\n \"acc_norm\"\ : 0.6354679802955665,\n \"acc_norm_stderr\": 0.0338640574606209\n },\n\ \ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\ : 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\ \ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.8242424242424242,\n \"acc_stderr\": 0.02972094300622445,\n\ \ \"acc_norm\": 0.8242424242424242,\n \"acc_norm_stderr\": 0.02972094300622445\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240524,\n\ \ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240524\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530613,\n\ \ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530613\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \ \ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.7899159663865546,\n \"acc_stderr\": 0.026461398717471874,\n\ \ \"acc_norm\": 0.7899159663865546,\n \"acc_norm_stderr\": 0.026461398717471874\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248437,\n \"\ acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248437\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\ acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6342592592592593,\n \"acc_stderr\": 0.03284738857647206,\n \"\ acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.03284738857647206\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568624,\n \"\ acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568624\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8860759493670886,\n \"acc_stderr\": 0.020681745135884562,\n \ \ \"acc_norm\": 0.8860759493670886,\n \"acc_norm_stderr\": 0.020681745135884562\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7757847533632287,\n\ \ \"acc_stderr\": 0.027991534258519517,\n \"acc_norm\": 0.7757847533632287,\n\ \ \"acc_norm_stderr\": 0.027991534258519517\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494732,\n\ \ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494732\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8760330578512396,\n \"acc_stderr\": 0.03008309871603521,\n \"\ acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.03008309871603521\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\ \ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\ \ \"acc_norm_stderr\": 0.036809181416738807\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\": 0.5625,\n\ \ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.5625,\n \ \ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.033932957297610096,\n\ \ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.033932957297610096\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\ \ \"acc_stderr\": 0.018315891685625852,\n \"acc_norm\": 0.9145299145299145,\n\ \ \"acc_norm_stderr\": 0.018315891685625852\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \ \ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.04093601807403326\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8735632183908046,\n\ \ \"acc_stderr\": 0.011884488905895555,\n \"acc_norm\": 0.8735632183908046,\n\ \ \"acc_norm_stderr\": 0.011884488905895555\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.8034682080924855,\n \"acc_stderr\": 0.021393961404363844,\n\ \ \"acc_norm\": 0.8034682080924855,\n \"acc_norm_stderr\": 0.021393961404363844\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40558659217877097,\n\ \ \"acc_stderr\": 0.016421670506339175,\n \"acc_norm\": 0.40558659217877097,\n\ \ \"acc_norm_stderr\": 0.016421670506339175\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.8202614379084967,\n \"acc_stderr\": 0.02198603218206415,\n\ \ \"acc_norm\": 0.8202614379084967,\n \"acc_norm_stderr\": 0.02198603218206415\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7845659163987139,\n\ \ \"acc_stderr\": 0.023350225475471442,\n \"acc_norm\": 0.7845659163987139,\n\ \ \"acc_norm_stderr\": 0.023350225475471442\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.8487654320987654,\n \"acc_stderr\": 0.019935086092149883,\n\ \ \"acc_norm\": 0.8487654320987654,\n \"acc_norm_stderr\": 0.019935086092149883\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5283687943262412,\n \"acc_stderr\": 0.02977945095730305,\n \ \ \"acc_norm\": 0.5283687943262412,\n \"acc_norm_stderr\": 0.02977945095730305\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5325945241199479,\n\ \ \"acc_stderr\": 0.012743072942653368,\n \"acc_norm\": 0.5325945241199479,\n\ \ \"acc_norm_stderr\": 0.012743072942653368\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.8014705882352942,\n \"acc_stderr\": 0.024231013370541087,\n\ \ \"acc_norm\": 0.8014705882352942,\n \"acc_norm_stderr\": 0.024231013370541087\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.7826797385620915,\n \"acc_stderr\": 0.016684820929148587,\n \ \ \"acc_norm\": 0.7826797385620915,\n \"acc_norm_stderr\": 0.016684820929148587\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\ \ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\ \ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7918367346938775,\n \"acc_stderr\": 0.025991117672813292,\n\ \ \"acc_norm\": 0.7918367346938775,\n \"acc_norm_stderr\": 0.025991117672813292\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\ \ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\ \ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.93,\n \"acc_stderr\": 0.0256432399976243,\n \ \ \"acc_norm\": 0.93,\n \"acc_norm_stderr\": 0.0256432399976243\n },\n\ \ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5120481927710844,\n\ \ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\ \ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\ \ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31456548347613217,\n\ \ \"mc1_stderr\": 0.016255241993179185,\n \"mc2\": 0.4674384125733044,\n\ \ \"mc2_stderr\": 0.01414272854245227\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156885\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5617892342683851,\n \ \ \"acc_stderr\": 0.013666915917255069\n }\n}\n```" repo_url: https://huggingface.co/vistagi/Mixtral-8x7b-v0.1-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_18T06_28_07.647994 path: - '**/details_harness|arc:challenge|25_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-02-18T06-28-07.647994.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|gsm8k|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hellaswag|10_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-management|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-02-18T06-28-07.647994.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-management|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-virology|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-02-18T06-28-07.647994.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|truthfulqa:mc|0_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-02-18T06-28-07.647994.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_02_18T06_28_07.647994 path: - '**/details_harness|winogrande|5_2024-02-18T06-28-07.647994.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-02-18T06-28-07.647994.parquet' - config_name: results data_files: - split: 2024_02_18T06_28_07.647994 path: - results_2024-02-18T06-28-07.647994.parquet - split: latest path: - results_2024-02-18T06-28-07.647994.parquet --- # Dataset Card for Evaluation run of vistagi/Mixtral-8x7b-v0.1-dpo  Dataset automatically created during the evaluation run of model [vistagi/Mixtral-8x7b-v0.1-dpo](https://huggingface.co/vistagi/Mixtral-8x7b-v0.1-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_vistagi__Mixtral-8x7b-v0.1-dpo", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-18T06:28:07.647994](https://huggingface.co/datasets/open-llm-leaderboard/details_vistagi__Mixtral-8x7b-v0.1-dpo/blob/main/results_2024-02-18T06-28-07.647994.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": 0.7134552339034452, "acc_stderr": 0.030055997546363594, "acc_norm": 0.7181597948300631, "acc_norm_stderr": 0.030631631253278484, "mc1": 0.31456548347613217, "mc1_stderr": 0.016255241993179185, "mc2": 0.4674384125733044, "mc2_stderr": 0.01414272854245227 }, "harness|arc:challenge|25": { "acc": 0.6322525597269625, "acc_stderr": 0.01409099561816849, "acc_norm": 0.6655290102389079, "acc_norm_stderr": 0.013787460322441374 }, "harness|hellaswag|10": { "acc": 0.6694881497709619, "acc_stderr": 0.004694360968929403, "acc_norm": 0.8639713204540929, "acc_norm_stderr": 0.0034211839093201673 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252606, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252606 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6962962962962963, "acc_stderr": 0.03972552884785137, "acc_norm": 0.6962962962962963, "acc_norm_stderr": 0.03972552884785137 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.8223684210526315, "acc_stderr": 0.031103182383123387, "acc_norm": 0.8223684210526315, "acc_norm_stderr": 0.031103182383123387 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.75, "acc_stderr": 0.04351941398892446, "acc_norm": 0.75, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7886792452830189, "acc_stderr": 0.025125766484827845, "acc_norm": 0.7886792452830189, "acc_norm_stderr": 0.025125766484827845 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8472222222222222, "acc_stderr": 0.030085743248565666, "acc_norm": 0.8472222222222222, "acc_norm_stderr": 0.030085743248565666 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.61, "acc_stderr": 0.04902071300001975, "acc_norm": 0.61, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.47, "acc_stderr": 0.05016135580465919, "acc_norm": 0.47, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7052023121387283, "acc_stderr": 0.03476599607516478, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.03476599607516478 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.45098039215686275, "acc_stderr": 0.049512182523962625, "acc_norm": 0.45098039215686275, "acc_norm_stderr": 0.049512182523962625 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6808510638297872, "acc_stderr": 0.03047297336338004, "acc_norm": 0.6808510638297872, "acc_norm_stderr": 0.03047297336338004 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.6491228070175439, "acc_stderr": 0.04489539350270698, "acc_norm": 0.6491228070175439, "acc_norm_stderr": 0.04489539350270698 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03960933549451208, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03960933549451208 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.47619047619047616, "acc_stderr": 0.025722097064388525, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.025722097064388525 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5634920634920635, "acc_stderr": 0.04435932892851466, "acc_norm": 0.5634920634920635, "acc_norm_stderr": 0.04435932892851466 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8354838709677419, "acc_stderr": 0.021090847745939313, "acc_norm": 0.8354838709677419, "acc_norm_stderr": 0.021090847745939313 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6354679802955665, "acc_stderr": 0.0338640574606209, "acc_norm": 0.6354679802955665, "acc_norm_stderr": 0.0338640574606209 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8242424242424242, "acc_stderr": 0.02972094300622445, "acc_norm": 0.8242424242424242, "acc_norm_stderr": 0.02972094300622445 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8686868686868687, "acc_stderr": 0.024063156416822516, "acc_norm": 0.8686868686868687, "acc_norm_stderr": 0.024063156416822516 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9378238341968912, "acc_stderr": 0.017426974154240524, "acc_norm": 0.9378238341968912, "acc_norm_stderr": 0.017426974154240524 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7128205128205128, "acc_stderr": 0.022939925418530613, "acc_norm": 0.7128205128205128, "acc_norm_stderr": 0.022939925418530613 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.37037037037037035, "acc_stderr": 0.02944316932303154, "acc_norm": 0.37037037037037035, "acc_norm_stderr": 0.02944316932303154 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.7899159663865546, "acc_stderr": 0.026461398717471874, "acc_norm": 0.7899159663865546, "acc_norm_stderr": 0.026461398717471874 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248437, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8844036697247707, "acc_stderr": 0.01370874953417264, "acc_norm": 0.8844036697247707, "acc_norm_stderr": 0.01370874953417264 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6342592592592593, "acc_stderr": 0.03284738857647206, "acc_norm": 0.6342592592592593, "acc_norm_stderr": 0.03284738857647206 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8578431372549019, "acc_stderr": 0.024509803921568624, "acc_norm": 0.8578431372549019, "acc_norm_stderr": 0.024509803921568624 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8860759493670886, "acc_stderr": 0.020681745135884562, "acc_norm": 0.8860759493670886, "acc_norm_stderr": 0.020681745135884562 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7757847533632287, "acc_stderr": 0.027991534258519517, "acc_norm": 0.7757847533632287, "acc_norm_stderr": 0.027991534258519517 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.816793893129771, "acc_stderr": 0.03392770926494732, "acc_norm": 0.816793893129771, "acc_norm_stderr": 0.03392770926494732 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8760330578512396, "acc_stderr": 0.03008309871603521, "acc_norm": 0.8760330578512396, "acc_norm_stderr": 0.03008309871603521 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8240740740740741, "acc_stderr": 0.036809181416738807, "acc_norm": 0.8240740740740741, "acc_norm_stderr": 0.036809181416738807 }, "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": 0.5625, "acc_stderr": 0.04708567521880525, "acc_norm": 0.5625, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.033932957297610096, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.033932957297610096 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9145299145299145, "acc_stderr": 0.018315891685625852, "acc_norm": 0.9145299145299145, "acc_norm_stderr": 0.018315891685625852 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.79, "acc_stderr": 0.04093601807403326, "acc_norm": 0.79, "acc_norm_stderr": 0.04093601807403326 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8735632183908046, "acc_stderr": 0.011884488905895555, "acc_norm": 0.8735632183908046, "acc_norm_stderr": 0.011884488905895555 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8034682080924855, "acc_stderr": 0.021393961404363844, "acc_norm": 0.8034682080924855, "acc_norm_stderr": 0.021393961404363844 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.40558659217877097, "acc_stderr": 0.016421670506339175, "acc_norm": 0.40558659217877097, "acc_norm_stderr": 0.016421670506339175 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8202614379084967, "acc_stderr": 0.02198603218206415, "acc_norm": 0.8202614379084967, "acc_norm_stderr": 0.02198603218206415 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7845659163987139, "acc_stderr": 0.023350225475471442, "acc_norm": 0.7845659163987139, "acc_norm_stderr": 0.023350225475471442 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8487654320987654, "acc_stderr": 0.019935086092149883, "acc_norm": 0.8487654320987654, "acc_norm_stderr": 0.019935086092149883 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5283687943262412, "acc_stderr": 0.02977945095730305, "acc_norm": 0.5283687943262412, "acc_norm_stderr": 0.02977945095730305 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5325945241199479, "acc_stderr": 0.012743072942653368, "acc_norm": 0.5325945241199479, "acc_norm_stderr": 0.012743072942653368 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8014705882352942, "acc_stderr": 0.024231013370541087, "acc_norm": 0.8014705882352942, "acc_norm_stderr": 0.024231013370541087 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7826797385620915, "acc_stderr": 0.016684820929148587, "acc_norm": 0.7826797385620915, "acc_norm_stderr": 0.016684820929148587 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7918367346938775, "acc_stderr": 0.025991117672813292, "acc_norm": 0.7918367346938775, "acc_norm_stderr": 0.025991117672813292 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.93, "acc_stderr": 0.0256432399976243, "acc_norm": 0.93, "acc_norm_stderr": 0.0256432399976243 }, "harness|hendrycksTest-virology|5": { "acc": 0.5120481927710844, "acc_stderr": 0.03891364495835817, "acc_norm": 0.5120481927710844, "acc_norm_stderr": 0.03891364495835817 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8771929824561403, "acc_stderr": 0.02517298435015575, "acc_norm": 0.8771929824561403, "acc_norm_stderr": 0.02517298435015575 }, "harness|truthfulqa:mc|0": { "mc1": 0.31456548347613217, "mc1_stderr": 0.016255241993179185, "mc2": 0.4674384125733044, "mc2_stderr": 0.01414272854245227 }, "harness|winogrande|5": { "acc": 0.8153117600631413, "acc_stderr": 0.010905978112156885 }, "harness|gsm8k|5": { "acc": 0.5617892342683851, "acc_stderr": 0.013666915917255069 } } ``` ## 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]

mtkinit/SuperDataset18293

--- pretty_name: SuperDataset18293 tags: - uci - world --- # SuperDataset18293 Created from AIOD platform

kalcho100/flippy_combined_dataset

--- dataset_info: features: - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 925982000.8 num_examples: 677240 - name: test num_bytes: 231495500.2 num_examples: 169310 download_size: 623484715 dataset_size: 1157477501.0 --- # Dataset Card for "flippy_combined_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B

--- pretty_name: Evaluation run of TurkuNLP/gpt3-finnish-13B dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TurkuNLP/gpt3-finnish-13B](https://huggingface.co/TurkuNLP/gpt3-finnish-13B)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 3 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the 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_TurkuNLP__gpt3-finnish-13B_public\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-11-08T08:19:11.789658](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public/blob/main/results_2023-11-08T08-19-11.789658.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\": 0.010906040268456376,\n\ \ \"em_stderr\": 0.0010636334198497977,\n \"f1\": 0.0586136744966444,\n\ \ \"f1_stderr\": 0.001583703669300269,\n \"acc\": 0.29157154884622954,\n\ \ \"acc_stderr\": 0.007692758773767466\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.010906040268456376,\n \"em_stderr\": 0.0010636334198497977,\n\ \ \"f1\": 0.0586136744966444,\n \"f1_stderr\": 0.001583703669300269\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \ \ \"acc_stderr\": 0.0015145735612245414\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.580110497237569,\n \"acc_stderr\": 0.013870943986310391\n\ \ }\n}\n```" repo_url: https://huggingface.co/TurkuNLP/gpt3-finnish-13B leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_drop_3 data_files: - split: 2023_11_08T08_19_11.789658 path: - '**/details_harness|drop|3_2023-11-08T08-19-11.789658.parquet' - split: latest path: - '**/details_harness|drop|3_2023-11-08T08-19-11.789658.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_11_08T08_19_11.789658 path: - '**/details_harness|gsm8k|5_2023-11-08T08-19-11.789658.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-11-08T08-19-11.789658.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_11_08T08_19_11.789658 path: - '**/details_harness|winogrande|5_2023-11-08T08-19-11.789658.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-11-08T08-19-11.789658.parquet' - config_name: results data_files: - split: 2023_11_08T08_19_11.789658 path: - results_2023-11-08T08-19-11.789658.parquet - split: latest path: - results_2023-11-08T08-19-11.789658.parquet --- # Dataset Card for Evaluation run of TurkuNLP/gpt3-finnish-13B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TurkuNLP/gpt3-finnish-13B - **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 [TurkuNLP/gpt3-finnish-13B](https://huggingface.co/TurkuNLP/gpt3-finnish-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_TurkuNLP__gpt3-finnish-13B_public", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-11-08T08:19:11.789658](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public/blob/main/results_2023-11-08T08-19-11.789658.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": 0.010906040268456376, "em_stderr": 0.0010636334198497977, "f1": 0.0586136744966444, "f1_stderr": 0.001583703669300269, "acc": 0.29157154884622954, "acc_stderr": 0.007692758773767466 }, "harness|drop|3": { "em": 0.010906040268456376, "em_stderr": 0.0010636334198497977, "f1": 0.0586136744966444, "f1_stderr": 0.001583703669300269 }, "harness|gsm8k|5": { "acc": 0.003032600454890068, "acc_stderr": 0.0015145735612245414 }, "harness|winogrande|5": { "acc": 0.580110497237569, "acc_stderr": 0.013870943986310391 } } ``` ### 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]

mboth/waermeVerteilen-200-undersampled

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* - split: valid path: data/valid-* dataset_info: features: - name: Datatype dtype: string - name: Beschreibung dtype: string - name: Name dtype: string - name: Unit dtype: string - name: text dtype: string - name: Grundfunktion dtype: string - name: ZweiteGrundfunktion dtype: string - name: label dtype: class_label: names: '0': Druckhaltestation '1': HeizkreisAllgemein '2': Heizkurve '3': Kaeltemengenzaehler '4': Pumpe '5': Raum '6': Regler '7': Ruecklauf '8': Uebertrager '9': Ventil '10': Vorlauf '11': Waermemengenzaehler '12': Warmwasserbereitung splits: - name: train num_bytes: 407710.65048052603 num_examples: 1916 - name: test num_bytes: 423002 num_examples: 1978 - name: valid num_bytes: 423002 num_examples: 1978 download_size: 411048 dataset_size: 1253714.650480526 --- # Dataset Card for "waermeVerteilen-200-undersampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rojpav/mini-croupier

--- license: apache-2.0 ---

communityai/aptchat-code-math-0.5k

--- dataset_info: features: - name: category dtype: string - name: total_tokens dtype: int64 - name: conversations list: - name: content dtype: string - name: role dtype: string splits: - name: train num_bytes: 7006904.0 num_examples: 581 download_size: 2903792 dataset_size: 7006904.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

vwxyzjn/ultrafeedback_binarized_1710204240

--- dataset_info: features: - name: prompt dtype: string - name: prompt_id dtype: string - name: chosen list: - name: content dtype: string - name: role dtype: string - name: rejected list: - name: content dtype: string - name: role dtype: string - name: messages list: - name: content dtype: string - name: role dtype: string - name: score_chosen dtype: float64 - name: score_rejected dtype: float64 - name: query list: - name: content dtype: string - name: role dtype: string - name: query_token sequence: int64 - name: query_token_len dtype: int64 - name: query_chosen_token sequence: int64 - name: query_chosen_token_len dtype: int64 - name: chosen_token sequence: int64 - name: chosen_token_len dtype: int64 - name: query_rejected_token sequence: int64 - name: query_rejected_token_len dtype: int64 - name: rejected_token sequence: int64 - name: rejected_token_len dtype: int64 splits: - name: train_prefs num_bytes: 975839605.6968021 num_examples: 24122 - name: test_prefs num_bytes: 31713753.975 num_examples: 786 download_size: 113334298 dataset_size: 1007553359.6718022 configs: - config_name: default data_files: - split: train_prefs path: data/train_prefs-* - split: test_prefs path: data/test_prefs-* ---

SEACrowd/kopi_nllb

--- tags: - self-supervised-pretraining language: - ind - jav - ace - ban - bjn - min - sun --- # kopi_nllb KopI(Korpus Perayapan Indonesia)-NLLB, is Indonesian family language(aceh,bali,banjar,indonesia,jawa,minang,sunda) only extracted from NLLB Dataset, allenai/nllb each language set also filtered using some some deduplicate technique such as exact hash(md5) dedup technique and minhash LSH neardup ## Dataset Usage Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`. ## Citation ``` Hefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv https://arxiv.org/abs/2205.12654, 2022. NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv https://arxiv.org/abs/2207.04672, 2022. ``` ## License ODC_C ## Homepage [https://huggingface.co/datasets/munggok/KoPI-NLLB](https://huggingface.co/datasets/munggok/KoPI-NLLB) ### NusaCatalogue For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)

coref-data/gen_winograd_raw

--- license: cc-by-nd-4.0 --- # gen_winograd - Project: https://ufal.mff.cuni.cz/corefud - Data source: https://github.com/mbzuai-nlp/gen-X/tree/bf1c0adb4b4def03cdf419c18b2948695bc1fab8 ## Details English Winograd generated by GPT-4 ## Citation ``` @misc{whitehouse2023llmpowered, title={LLM-powered Data Augmentation for Enhanced Crosslingual Performance}, author={Chenxi Whitehouse and Monojit Choudhury and Alham Fikri Aji}, year={2023}, eprint={2305.14288}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ

--- pretty_name: Evaluation run of TheBloke/Llama-2-13B-GPTQ dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ)\ \ 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 4 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_TheBloke__Llama-2-13B-GPTQ\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-27T16:26:14.370378](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ/blob/main/results_2023-10-27T16-26-14.370378.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\": 0.0020973154362416107,\n\ \ \"em_stderr\": 0.0004685065030368251,\n \"f1\": 0.06011535234899329,\n\ \ \"f1_stderr\": 0.0013639179977941345,\n \"acc\": 0.43730302009426913,\n\ \ \"acc_stderr\": 0.010347143848267699\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368251,\n\ \ \"f1\": 0.06011535234899329,\n \"f1_stderr\": 0.0013639179977941345\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11296436694465505,\n \ \ \"acc_stderr\": 0.00871933902883308\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702316\n\ \ }\n}\n```" repo_url: https://huggingface.co/TheBloke/Llama-2-13B-GPTQ 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_29T15_04_20.709230 path: - '**/details_harness|arc:challenge|25_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|arc:challenge|25_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|arc:challenge|25_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-08-31T11:12:42.998068.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_27T16_26_14.370378 path: - '**/details_harness|drop|3_2023-10-27T16-26-14.370378.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-27T16-26-14.370378.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_27T16_26_14.370378 path: - '**/details_harness|gsm8k|5_2023-10-27T16-26-14.370378.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-27T16-26-14.370378.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hellaswag|10_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hellaswag|10_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hellaswag|10_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-29T15:04:20.709230.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-30T10:42:39.395336.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-management|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-08-31T11:12:42.998068.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-management|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-management|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-management|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-virology|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-08-31T11:12:42.998068.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_08_29T15_04_20.709230 path: - '**/details_harness|truthfulqa:mc|0_2023-08-29T15:04:20.709230.parquet' - split: 2023_08_30T10_42_39.395336 path: - '**/details_harness|truthfulqa:mc|0_2023-08-30T10:42:39.395336.parquet' - split: 2023_08_31T11_12_42.998068 path: - '**/details_harness|truthfulqa:mc|0_2023-08-31T11:12:42.998068.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-08-31T11:12:42.998068.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_27T16_26_14.370378 path: - '**/details_harness|winogrande|5_2023-10-27T16-26-14.370378.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-27T16-26-14.370378.parquet' - config_name: results data_files: - split: 2023_08_29T15_04_20.709230 path: - results_2023-08-29T15:04:20.709230.parquet - split: 2023_08_30T10_42_39.395336 path: - results_2023-08-30T10:42:39.395336.parquet - split: 2023_08_31T11_12_42.998068 path: - results_2023-08-31T11:12:42.998068.parquet - split: 2023_10_27T16_26_14.370378 path: - results_2023-10-27T16-26-14.370378.parquet - split: latest path: - results_2023-10-27T16-26-14.370378.parquet --- # Dataset Card for Evaluation run of TheBloke/Llama-2-13B-GPTQ ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/TheBloke/Llama-2-13B-GPTQ - **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 [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) 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 4 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_TheBloke__Llama-2-13B-GPTQ", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-27T16:26:14.370378](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Llama-2-13B-GPTQ/blob/main/results_2023-10-27T16-26-14.370378.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": 0.0020973154362416107, "em_stderr": 0.0004685065030368251, "f1": 0.06011535234899329, "f1_stderr": 0.0013639179977941345, "acc": 0.43730302009426913, "acc_stderr": 0.010347143848267699 }, "harness|drop|3": { "em": 0.0020973154362416107, "em_stderr": 0.0004685065030368251, "f1": 0.06011535234899329, "f1_stderr": 0.0013639179977941345 }, "harness|gsm8k|5": { "acc": 0.11296436694465505, "acc_stderr": 0.00871933902883308 }, "harness|winogrande|5": { "acc": 0.7616416732438832, "acc_stderr": 0.011974948667702316 } } ``` ### 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]

fanwei1/test

--- license: mit configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* dataset_info: features: - name: image sequence: sequence: sequence: sequence: float32 - name: h264 sequence: sequence: uint8 - name: id sequence: string - name: size sequence: int64 splits: - name: train num_bytes: 296990 num_examples: 1 - name: validation num_bytes: 299714 num_examples: 1 download_size: 435230 dataset_size: 596704 ---

open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0

--- pretty_name: Evaluation run of togethercomputer/GPT-JT-6B-v0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0)\ \ 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_togethercomputer__GPT-JT-6B-v0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-10-17T19:26:54.220051](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0/blob/main/results_2023-10-17T19-26-54.220051.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\": 0.0010486577181208054,\n\ \ \"em_stderr\": 0.0003314581465219154,\n \"f1\": 0.043061031879194765,\n\ \ \"f1_stderr\": 0.0011437900819203201,\n \"acc\": 0.330058886781919,\n\ \ \"acc_stderr\": 0.008219084533910332\n },\n \"harness|drop|3\": {\n\ \ \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.0003314581465219154,\n\ \ \"f1\": 0.043061031879194765,\n \"f1_stderr\": 0.0011437900819203201\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \ \ \"acc_stderr\": 0.003015294242890946\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.6479873717442778,\n \"acc_stderr\": 0.013422874824929718\n\ \ }\n}\n```" repo_url: https://huggingface.co/togethercomputer/GPT-JT-6B-v0 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_19T15_42_14.994932 path: - '**/details_harness|arc:challenge|25_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-07-19T15:42:14.994932.parquet' - config_name: harness_drop_3 data_files: - split: 2023_10_17T19_26_54.220051 path: - '**/details_harness|drop|3_2023-10-17T19-26-54.220051.parquet' - split: latest path: - '**/details_harness|drop|3_2023-10-17T19-26-54.220051.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_10_17T19_26_54.220051 path: - '**/details_harness|gsm8k|5_2023-10-17T19-26-54.220051.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-10-17T19-26-54.220051.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hellaswag|10_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:42:14.994932.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_07_19T15_42_14.994932 path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:42:14.994932.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-07-19T15:42:14.994932.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_10_17T19_26_54.220051 path: - '**/details_harness|winogrande|5_2023-10-17T19-26-54.220051.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-10-17T19-26-54.220051.parquet' - config_name: results data_files: - split: 2023_07_19T15_42_14.994932 path: - results_2023-07-19T15:42:14.994932.parquet - split: 2023_10_17T19_26_54.220051 path: - results_2023-10-17T19-26-54.220051.parquet - split: latest path: - results_2023-10-17T19-26-54.220051.parquet --- # Dataset Card for Evaluation run of togethercomputer/GPT-JT-6B-v0 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/togethercomputer/GPT-JT-6B-v0 - **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 [togethercomputer/GPT-JT-6B-v0](https://huggingface.co/togethercomputer/GPT-JT-6B-v0) 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_togethercomputer__GPT-JT-6B-v0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-17T19:26:54.220051](https://huggingface.co/datasets/open-llm-leaderboard/details_togethercomputer__GPT-JT-6B-v0/blob/main/results_2023-10-17T19-26-54.220051.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": 0.0010486577181208054, "em_stderr": 0.0003314581465219154, "f1": 0.043061031879194765, "f1_stderr": 0.0011437900819203201, "acc": 0.330058886781919, "acc_stderr": 0.008219084533910332 }, "harness|drop|3": { "em": 0.0010486577181208054, "em_stderr": 0.0003314581465219154, "f1": 0.043061031879194765, "f1_stderr": 0.0011437900819203201 }, "harness|gsm8k|5": { "acc": 0.012130401819560273, "acc_stderr": 0.003015294242890946 }, "harness|winogrande|5": { "acc": 0.6479873717442778, "acc_stderr": 0.013422874824929718 } } ``` ### 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]

yzhuang/metatree_fri_c0_1000_25

--- dataset_info: features: - name: id dtype: int64 - name: X sequence: float64 - name: y dtype: int64 splits: - name: train num_bytes: 157080 num_examples: 714 - name: validation num_bytes: 62920 num_examples: 286 download_size: 254313 dataset_size: 220000 --- # Dataset Card for "metatree_fri_c0_1000_25" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aliciiavs/chord_images

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': A '1': A7 '2': Am '3': C '4': D '5': D7 '6': E '7': Em '8': G splits: - name: train num_bytes: 210864168.8 num_examples: 1800 download_size: 205050921 dataset_size: 210864168.8 configs: - config_name: default data_files: - split: train path: data/train-* ---

BubbleJoe/multi_nli_unified_input

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation_matched path: data/validation_matched-* - split: validation_mismatched path: data/validation_mismatched-* dataset_info: features: - name: promptID dtype: int32 - name: pairID dtype: string - name: premise dtype: string - name: premise_binary_parse dtype: string - name: premise_parse dtype: string - name: hypothesis dtype: string - name: hypothesis_binary_parse dtype: string - name: hypothesis_parse dtype: string - name: genre dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: input dtype: string splits: - name: train num_bytes: 487186164 num_examples: 392702 - name: validation_matched num_bytes: 11956580 num_examples: 9815 - name: validation_mismatched num_bytes: 12618412 num_examples: 9832 download_size: 272284496 dataset_size: 511761156 --- # Dataset Card for "multi_nli_unified_input" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jxie/natural_questions

--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: answer dtype: string splits: - name: train num_bytes: 6059360 num_examples: 87925 - name: test num_bytes: 253307 num_examples: 3610 download_size: 0 dataset_size: 6312667 --- # Dataset Card for "natural_questions" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aaditya/orca_dpo_pairs-Hindi

--- dataset_info: features: - name: id dtype: string - name: codemix_system dtype: string - name: codemix_question dtype: string - name: codemix_chosen dtype: string - name: codemix_rejected dtype: string - name: codemix_question_type dtype: string - name: en_system dtype: string - name: en_question dtype: string - name: en_chosen dtype: string - name: en_rejected dtype: string splits: - name: train num_bytes: 51127339 num_examples: 10305 download_size: 27467174 dataset_size: 51127339 configs: - config_name: default data_files: - split: train path: data/train-* --- # Summary `aaditya/orca_dpo_pairs-Hindi` is an open source Hindi version dataset of Intel/orca_dpo_pairs This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode). Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 # Citation ``` @misc {orca_dpo_hindi, author = { Pal, Ankit }, title = { orca_dpo_pairs-Hindi}, year = 2024, url = { https://huggingface.co/datasets/aaditya/orca_dpo_pairs-Hindi }, doi = { 10.57967/hf/1759 }, publisher = { Hugging Face } } ```

Rasu23/iapp_all_train_test_iter0

--- dataset_info: features: - name: question_id dtype: string - name: article_id dtype: string - name: title dtype: string - name: context dtype: string - name: question dtype: string - name: answers dtype: string - name: 'Unnamed: 0' dtype: int64 - name: id dtype: string - name: references dtype: string - name: predictions dtype: string splits: - name: train num_bytes: 17356533 num_examples: 5761 - name: test num_bytes: 2199490 num_examples: 739 download_size: 3155434 dataset_size: 19556023 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

micsell/hebrew_kan_sentence100000

--- dataset_info: features: - name: audio dtype: audio - name: id dtype: string - name: language dtype: string - name: sentence dtype: string splits: - name: train num_bytes: 1894199082.0 num_examples: 10000 download_size: 1893355737 dataset_size: 1894199082.0 configs: - config_name: default data_files: - split: train path: data/train-* ---

open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0

--- pretty_name: Evaluation run of PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0](https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0)\ \ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\ \nThe dataset is composed of 63 configuration, each one coresponding to one of the\ \ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\ \ found as a specific split in each configuration, the split being named using the\ \ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\ \nAn additional configuration \"results\" store all the aggregated results of the\ \ run (and is used to compute and display the aggregated metrics on the [Open LLM\ \ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\ \nTo load the details from a run, you can for instance do the following:\n```python\n\ from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2023-12-29T11:11:59.721182](https://huggingface.co/datasets/open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0/blob/main/results_2023-12-29T11-11-59.721182.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\": 0.6676932083825828,\n\ \ \"acc_stderr\": 0.03141884754120868,\n \"acc_norm\": 0.6685033288172079,\n\ \ \"acc_norm_stderr\": 0.03206517056378548,\n \"mc1\": 0.45165238678090575,\n\ \ \"mc1_stderr\": 0.01742148030027764,\n \"mc2\": 0.6056804036036146,\n\ \ \"mc2_stderr\": 0.015579014786964863\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.6313993174061433,\n \"acc_stderr\": 0.014097810678042196,\n\ \ \"acc_norm\": 0.6612627986348123,\n \"acc_norm_stderr\": 0.013830568927974332\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6793467436765585,\n\ \ \"acc_stderr\": 0.004657738398900938,\n \"acc_norm\": 0.8653654650468035,\n\ \ \"acc_norm_stderr\": 0.0034063520713417243\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6,\n \ \ \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \"\ acc_norm_stderr\": 0.042320736951515885\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7763157894736842,\n \"acc_stderr\": 0.03391160934343604,\n\ \ \"acc_norm\": 0.7763157894736842,\n \"acc_norm_stderr\": 0.03391160934343604\n\ \ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\ : {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\ \ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\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\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \ \ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \ \ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\ acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\ : 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\ : {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \ \ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\ \ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6994219653179191,\n\ \ \"acc_stderr\": 0.034961014811911786,\n \"acc_norm\": 0.6994219653179191,\n\ \ \"acc_norm_stderr\": 0.034961014811911786\n },\n \"harness|hendrycksTest-college_physics|5\"\ : {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082636,\n\ \ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082636\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\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236785,\n\ \ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236785\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\ \ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\ \ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\ \ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.47354497354497355,\n \"acc_stderr\": 0.025715239811346758,\n \"\ acc_norm\": 0.47354497354497355,\n \"acc_norm_stderr\": 0.025715239811346758\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\ \ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\ \ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \ \ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8032258064516129,\n\ \ \"acc_stderr\": 0.022616409420742025,\n \"acc_norm\": 0.8032258064516129,\n\ \ \"acc_norm_stderr\": 0.022616409420742025\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\ : {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\ \ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\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\": 0.806060606060606,\n \"acc_stderr\": 0.030874145136562094,\n\ \ \"acc_norm\": 0.806060606060606,\n \"acc_norm_stderr\": 0.030874145136562094\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\ acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328973,\n\ \ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328973\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \ \ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.362962962962963,\n \"acc_stderr\": 0.02931820364520686,\n \ \ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.02931820364520686\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887027,\n\ \ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887027\n\ \ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\ : 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"\ acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n\ \ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\ : 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\ acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.6064814814814815,\n \"acc_stderr\": 0.03331747876370312,\n \"\ acc_norm\": 0.6064814814814815,\n \"acc_norm_stderr\": 0.03331747876370312\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025046,\n \"\ acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025046\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.8523206751054853,\n \"acc_stderr\": 0.023094329582595698,\n \ \ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.023094329582595698\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7309417040358744,\n\ \ \"acc_stderr\": 0.02976377940687497,\n \"acc_norm\": 0.7309417040358744,\n\ \ \"acc_norm_stderr\": 0.02976377940687497\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728745,\n\ \ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728745\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097654,\n \"\ acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097654\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\ \ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\ \ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n\ \ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026622,\n\ \ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026622\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\ \ \"acc_stderr\": 0.021586494001281382,\n \"acc_norm\": 0.8760683760683761,\n\ \ \"acc_norm_stderr\": 0.021586494001281382\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.822477650063857,\n\ \ \"acc_stderr\": 0.013664230995834832,\n \"acc_norm\": 0.822477650063857,\n\ \ \"acc_norm_stderr\": 0.013664230995834832\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.02335736578587403,\n\ \ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.02335736578587403\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39329608938547483,\n\ \ \"acc_stderr\": 0.01633726869427011,\n \"acc_norm\": 0.39329608938547483,\n\ \ \"acc_norm_stderr\": 0.01633726869427011\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.0239291555173513,\n\ \ \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.0239291555173513\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\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262192,\n\ \ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262192\n\ \ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\ acc\": 0.5177304964539007,\n \"acc_stderr\": 0.02980873964223777,\n \ \ \"acc_norm\": 0.5177304964539007,\n \"acc_norm_stderr\": 0.02980873964223777\n\ \ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.49282920469361147,\n\ \ \"acc_stderr\": 0.012768922739553308,\n \"acc_norm\": 0.49282920469361147,\n\ \ \"acc_norm_stderr\": 0.012768922739553308\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.025767252010855956,\n\ \ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.025767252010855956\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.704248366013072,\n \"acc_stderr\": 0.01846315413263281,\n \ \ \"acc_norm\": 0.704248366013072,\n \"acc_norm_stderr\": 0.01846315413263281\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\ \ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\ \ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.7795918367346939,\n \"acc_stderr\": 0.02653704531214529,\n\ \ \"acc_norm\": 0.7795918367346939,\n \"acc_norm_stderr\": 0.02653704531214529\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\ \ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\ \ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \ \ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\ \ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\ \ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\ \ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.45165238678090575,\n\ \ \"mc1_stderr\": 0.01742148030027764,\n \"mc2\": 0.6056804036036146,\n\ \ \"mc2_stderr\": 0.015579014786964863\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.8476716653512234,\n \"acc_stderr\": 0.010099208246065597\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6557998483699773,\n \ \ \"acc_stderr\": 0.013086800426693785\n }\n}\n```" repo_url: https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|arc:challenge|25_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2023-12-29T11-11-59.721182.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|gsm8k|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hellaswag|10_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-management|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-virology|5_2023-12-29T11-11-59.721182.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-management|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-virology|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T11-11-59.721182.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|truthfulqa:mc|0_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2023-12-29T11-11-59.721182.parquet' - config_name: harness_winogrande_5 data_files: - split: 2023_12_29T11_11_59.721182 path: - '**/details_harness|winogrande|5_2023-12-29T11-11-59.721182.parquet' - split: latest path: - '**/details_harness|winogrande|5_2023-12-29T11-11-59.721182.parquet' - config_name: results data_files: - split: 2023_12_29T11_11_59.721182 path: - results_2023-12-29T11-11-59.721182.parquet - split: latest path: - results_2023-12-29T11-11-59.721182.parquet --- # Dataset Card for Evaluation run of PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0  Dataset automatically created during the evaluation run of model [PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0](https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-12-29T11:11:59.721182](https://huggingface.co/datasets/open-llm-leaderboard/details_PracticeLLM__SOLAR-tail-10.7B-Merge-v1.0/blob/main/results_2023-12-29T11-11-59.721182.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": 0.6676932083825828, "acc_stderr": 0.03141884754120868, "acc_norm": 0.6685033288172079, "acc_norm_stderr": 0.03206517056378548, "mc1": 0.45165238678090575, "mc1_stderr": 0.01742148030027764, "mc2": 0.6056804036036146, "mc2_stderr": 0.015579014786964863 }, "harness|arc:challenge|25": { "acc": 0.6313993174061433, "acc_stderr": 0.014097810678042196, "acc_norm": 0.6612627986348123, "acc_norm_stderr": 0.013830568927974332 }, "harness|hellaswag|10": { "acc": 0.6793467436765585, "acc_stderr": 0.004657738398900938, "acc_norm": 0.8653654650468035, "acc_norm_stderr": 0.0034063520713417243 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6, "acc_stderr": 0.042320736951515885, "acc_norm": 0.6, "acc_norm_stderr": 0.042320736951515885 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343604, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343604 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6981132075471698, "acc_stderr": 0.028254200344438662, "acc_norm": 0.6981132075471698, "acc_norm_stderr": 0.028254200344438662 }, "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": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.0479372485441102, "acc_norm": 0.35, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6994219653179191, "acc_stderr": 0.034961014811911786, "acc_norm": 0.6994219653179191, "acc_norm_stderr": 0.034961014811911786 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.37254901960784315, "acc_stderr": 0.04810840148082636, "acc_norm": 0.37254901960784315, "acc_norm_stderr": 0.04810840148082636 }, "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": 0.6297872340425532, "acc_stderr": 0.03156564682236785, "acc_norm": 0.6297872340425532, "acc_norm_stderr": 0.03156564682236785 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 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}, "harness|truthfulqa:mc|0": { "mc1": 0.45165238678090575, "mc1_stderr": 0.01742148030027764, "mc2": 0.6056804036036146, "mc2_stderr": 0.015579014786964863 }, "harness|winogrande|5": { "acc": 0.8476716653512234, "acc_stderr": 0.010099208246065597 }, "harness|gsm8k|5": { "acc": 0.6557998483699773, "acc_stderr": 0.013086800426693785 } } ``` ## 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]

GeorgeGuo/detect

--- license: apache-2.0 task_categories: - text-classification language: - zh tags: - music size_categories: - 10K<n<100K --- This is dataset for test

TinyPixel/elm

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2542932 num_examples: 1073 download_size: 1390964 dataset_size: 2542932 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "elm" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sick

--- annotations_creators: - crowdsourced language_creators: - crowdsourced language: - en license: - cc-by-nc-sa-3.0 multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|image-flickr-8k - extended|semeval2012-sts-msr-video task_categories: - text-classification task_ids: - natural-language-inference paperswithcode_id: sick pretty_name: Sentences Involving Compositional Knowledge dataset_info: features: - name: id dtype: string - name: sentence_A dtype: string - name: sentence_B dtype: string - name: label dtype: class_label: names: '0': entailment '1': neutral '2': contradiction - name: relatedness_score dtype: float32 - name: entailment_AB dtype: string - name: entailment_BA dtype: string - name: sentence_A_original dtype: string - name: sentence_B_original dtype: string - name: sentence_A_dataset dtype: string - name: sentence_B_dataset dtype: string splits: - name: train num_bytes: 1180530 num_examples: 4439 - name: validation num_bytes: 132913 num_examples: 495 - name: test num_bytes: 1305846 num_examples: 4906 download_size: 217584 dataset_size: 2619289 --- # Dataset Card for sick ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** http://marcobaroni.org/composes/sick.html - **Repository:** [Needs More Information] - **Paper:** https://www.aclweb.org/anthology/L14-1314/ - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Needs More Information] ### Dataset Summary Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The dataset is in English. ## Dataset Structure ### Data Instances Example instance: ``` { "entailment_AB": "A_neutral_B", "entailment_BA": "B_neutral_A", "label": 1, "id": "1", "relatedness_score": 4.5, "sentence_A": "A group of kids is playing in a yard and an old man is standing in the background", "sentence_A_dataset": "FLICKR", "sentence_A_original": "A group of children playing in a yard, a man in the background.", "sentence_B": "A group of boys in a yard is playing and a man is standing in the background", "sentence_B_dataset": "FLICKR", "sentence_B_original": "A group of children playing in a yard, a man in the background." } ``` ### Data Fields - pair_ID: sentence pair ID - sentence_A: sentence A - sentence_B: sentence B - label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2) - relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale) - entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B) - entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A) - sentence_A_original: original sentence from which sentence A is derived - sentence_B_original: original sentence from which sentence B is derived - sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL) - sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL) ### Data Splits Train Trial Test 4439 495 4906 ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information ``` @inproceedings{marelli-etal-2014-sick, title = "A {SICK} cure for the evaluation of compositional distributional semantic models", author = "Marelli, Marco and Menini, Stefano and Baroni, Marco and Bentivogli, Luisa and Bernardi, Raffaella and Zamparelli, Roberto", booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)", month = may, year = "2014", address = "Reykjavik, Iceland", publisher = "European Language Resources Association (ELRA)", url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf", pages = "216--223", } ``` ### Contributions Thanks to [@calpt](https://github.com/calpt) for adding this dataset.

amanrangapur/Fin-Fact

--- license: apache-2.0 task_categories: - text-classification - text-generation language: - en tags: - finance pretty_name: FinFact size_categories: - 1K<n<10K dataset_info: - config_name: generation features: - name: url dtype: string - name: claim dtype: string - name: author dtype: string - name: posted dtype: string # - name: sci_digest # sequence: string # - name: justification # sequence: string # - name: issues # dtype: string # - name: image_data # sequence: # - name: image_src # dtype: string # - name: image_caption # dtype: string # - name: evidence # sequence: # - name: sentence # dtype: string # - name: hrefs # dtype: string # - name: label # dtype: string # - name: visualization_bias # dtype: int32 --- <h1 align="center">Fin-Fact - Financial Fact-Checking Dataset</h1> ## Table of Contents - [Overview](#overview) - [Dataset Description](#dataset-description) - [Dataset Usage](#dataset-usage) - [Leaderboard](#leaderboard) - [Dependencies](#dependencies) - [Run models for paper metrics](#run-models-for-paper-metrics) - [Citation](#citation) - [Contribution](#contribution) - [License](#license) - [Contact](#contact) ## Overview Welcome to the Fin-Fact repository! Fin-Fact is a comprehensive dataset designed specifically for financial fact-checking and explanation generation. This README provides an overview of the dataset, how to use it, and other relevant information. [Click here](https://arxiv.org/abs/2309.08793) to access the paper. ## Dataset Description - **Name**: Fin-Fact - **Purpose**: Fact-checking and explanation generation in the financial domain. - **Labels**: The dataset includes various labels, including Claim, Author, Posted Date, Sci-digest, Justification, Evidence, Evidence href, Image href, Image Caption, Visualisation Bias Label, Issues, and Claim Label. - **Size**: The dataset consists of 3121 claims spanning multiple financial sectors. - **Additional Features**: The dataset goes beyond textual claims and incorporates visual elements, including images and their captions. ## Dataset Usage Fin-Fact is a valuable resource for researchers, data scientists, and fact-checkers in the financial domain. Here's how you can use it: 1. **Download the Dataset**: You can download the Fin-Fact dataset [here](https://github.com/IIT-DM/Fin-Fact/blob/FinFact/finfact.json). 2. **Exploratory Data Analysis**: Perform exploratory data analysis to understand the dataset's structure, distribution, and any potential biases. 3. **Natural Language Processing (NLP) Tasks**: Utilize the dataset for various NLP tasks such as fact-checking, claim verification, and explanation generation. 4. **Fact Checking Experiments**: Train and evaluate machine learning models, including text and image analysis, using the dataset to enhance the accuracy of fact-checking systems. ## Leaderboard ## Dependencies We recommend you create an anaconda environment: `conda create --name finfact python=3.6 conda-build` Then, install Python requirements: `pip install -r requirements.txt` ## Run models for paper metrics We provide scripts let you easily run our dataset on existing state-of-the-art models and re-create the metrics published in paper. You should be able to reproduce our results from the paper by following these instructions. Please post an issue if you're unable to do this. To run existing ANLI models for fact checking. ### Run: 1. BART ```bash python anli.py --model_name 'ynie/bart-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 2. RoBERTa ```bash python anli.py --model_name 'ynie/roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 3. ELECTRA ```bash python anli.py --model_name 'ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 4. AlBERT ```bash python anli.py --model_name 'ynie/albert-xxlarge-v2-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 5. XLNET ```bash python anli.py --model_name 'ynie/xlnet-large-cased-snli_mnli_fever_anli_R1_R2_R3-nli' --data_file finfact.json --threshold 0.5 ``` 6. GPT-2 ```bash python gpt2_nli.py --model_name 'fractalego/fact-checking' --data_file finfact.json ``` ## Citation ``` @misc{rangapur2023finfact, title={Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation}, author={Aman Rangapur and Haoran Wang and Kai Shu}, year={2023}, eprint={2309.08793}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ## Contribution We welcome contributions from the community to help improve Fin-Fact. If you have suggestions, bug reports, or want to contribute code or data, please check our [CONTRIBUTING.md](CONTRIBUTING.md) file for guidelines. ## License Fin-Fact is released under the [MIT License](/LICENSE). Please review the license before using the dataset. ## Contact For questions, feedback, or inquiries related to Fin-Fact, please contact `arangapur@hawk.iit.edu`. We hope you find Fin-Fact valuable for your research and fact-checking endeavors. Happy fact-checking!

Seanxh/twitter_dataset_1713225737

--- dataset_info: features: - name: id dtype: string - name: tweet_content dtype: string - name: user_name dtype: string - name: user_id dtype: string - name: created_at dtype: string - name: url dtype: string - name: favourite_count dtype: int64 - name: scraped_at dtype: string - name: image_urls dtype: string splits: - name: train num_bytes: 243707 num_examples: 562 download_size: 78336 dataset_size: 243707 configs: - config_name: default data_files: - split: train path: data/train-* ---

qgallouedec/prj_gia_dataset_metaworld_lever_pull_v2_1111

--- library_name: gia tags: - deep-reinforcement-learning - reinforcement-learning - gia - multi-task - multi-modal - imitation-learning - offline-reinforcement-learning --- An imitation learning environment for the lever-pull-v2 environment, sample for the policy lever-pull-v2 This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia ## Load dataset First, clone it with ```sh git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_lever_pull_v2_1111 ``` Then, load it with ```python import numpy as np dataset = np.load("prj_gia_dataset_metaworld_lever_pull_v2_1111/dataset.npy", allow_pickle=True).item() print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards']) ```

liuyanchen1015/VALUE_qnli_lexical

--- dataset_info: features: - name: question dtype: string - name: sentence dtype: string - name: label dtype: int64 - name: idx dtype: int64 - name: value_score dtype: int64 splits: - name: dev num_bytes: 1289179 num_examples: 4844 - name: test num_bytes: 1290872 num_examples: 4829 - name: train num_bytes: 24041472 num_examples: 92638 download_size: 17902736 dataset_size: 26621523 --- # Dataset Card for "VALUE_qnli_lexical" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

traintogpb/aihub-koen-translation-integrated-base-10m

--- task_categories: - translation language: - en - ko size_categories: - 10M<n<100M --- # AI Hub Ko-En Translation Dataset (Integrated) AI Hub의 한-영 번역 관련 데이터셋 8개를 병합한 자료입니다. 병합 시 총 데이터 개수는 10,416,509개 이며, train / validation / test는 8:1:1 비율로 분할되었습니다. - base-10m: 병합 데이터 100% 사용, 총 10,416,509개 - mini-1m: 병합 데이터 10% 사용 (base-10m의 각 세트 내에서 10% 임의 선택), 총 1,041,651개 - tiny-100k: 병합 데이터 1% 사용 (base-10m의 각 세트 내에서 1% 임의 선택), 총 104,165개 ## Subsets 활용한 데이터셋 목록은 다음과 같으며, 데이터셋 이름 옆 번호는 aihubshell에서의 datasetkey입니다. - [전문분야 한영 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=111) (111) - 총 개수: 1,350,000 - 중복 제거 후 개수: 1,350,000 - 사용 칼럼: '한국어', '영어' - [한국어-영어 번역 말뭉치(기술과학)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=124) (124) - 총 개수: 1,344,631 - 중복 제거 후 개수: 1,344,631 - 사용 칼럼: 'ko', 'en' - [한국어-영어 번역 말뭉치(사회과학)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=125) (125) - 총 개수: 1,361,845 - 중복 제거 후 개수: 1,361,825 - 사용 칼럼: 'ko', 'en' - [한국어-영어 번역(병렬) 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=126) (126) - 총 개수: 1,602,418 - 중복 제거 후 개수: 1,599,924 - 사용 칼럼: '원문', '번역문' - [산업정보 연계 주요국 특허 영-한 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=563) (563) - 총 개수: 359,999 - 중복 제거 후 개수: 358,424 - 사용 칼럼: 'astrt_cont_kor', 'astrt_cont_eng' - [일상생활 및 구어체 한-영 번역 병렬 말뭉치 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71265) (71265) - 총 개수: 2,700,345 - 중복 제거 후 개수: 2,486,058 - 사용 칼럼: 'ko', 'en' - [기술과학 분야 한-영 번역 병렬 말뭉치 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71266) (71266) - 총 개수: 1,350,162 - 중복 제거 후 개수: 1,328,987 - 사용 칼럼: 'ko', 'en' - [방송콘텐츠 한국어-영어 번역 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=71382) (71382) - 총 개수: 587,084 - 중복 제거 후 개수: 586,660 - 사용 칼럼: '원문', '최종번역문'

Viniciaao/HardLevel

--- license: openrail ---

TrainingDataPro/license_plates

--- license: cc-by-nc-nd-4.0 task_categories: - image-to-text language: - en tags: - finance dataset_info: - config_name: Brazil_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 173536648 num_examples: 72 download_size: 22606962 dataset_size: 173536648 - config_name: Estonia_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 7990452 num_examples: 10 download_size: 7863164 dataset_size: 7990452 - config_name: Finland_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 9650579 num_examples: 10 download_size: 9485725 dataset_size: 9650579 - config_name: Kazakhstan_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 14064541 num_examples: 19 download_size: 7265915 dataset_size: 14064541 - config_name: Kazakhstan_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 6324396 num_examples: 22 download_size: 2852873 dataset_size: 6324396 - config_name: Lithuania_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 8127614 num_examples: 10 download_size: 7940839 dataset_size: 8127614 - config_name: Serbia_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 10000777 num_examples: 10 download_size: 9808356 dataset_size: 10000777 - config_name: Serbia_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 26535839 num_examples: 67 download_size: 4044272 dataset_size: 26535839 - config_name: UAE_platesmania features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 8236358 num_examples: 10 download_size: 8028800 dataset_size: 8236358 - config_name: UAE_youtube features: - name: image dtype: image - name: labeled_image dtype: image - name: bbox dtype: string - name: license_plate.id dtype: string - name: license_plate.visibility dtype: string - name: license_plate.rows_count dtype: uint8 - name: license_plate.number dtype: string - name: license_plate.serial dtype: string - name: license_plate.country dtype: string - name: license_plate.mask dtype: string splits: - name: train num_bytes: 41202317 num_examples: 162 download_size: 2666314 dataset_size: 41202317 --- # License Plates Over **1.2 million** annotated license plates from vehicles around the world. This dataset is tailored for **License Plate Recognition tasks** and includes images from both YouTube and PlatesMania. Annotation details are provided in the About section below. # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/car-license-plates?utm_source=huggingface&utm_medium=cpc&utm_campaign=license_plates) to discuss your requirements, learn about the price and buy the dataset. # About ## Variables in .csv files: - **file_name** - filename of the original car photo - **license_plate.country** - country where the vehicle was captured - **bbox** - normalized Bounding Box labeling of the car - **license_plate.visibility** - the visibility type of the license plate - **license_plate.id** - unique license plate's id - **license_plate.mask** - normalized coordinates of the license plate - **license_plate.rows_count** - single-line or double-line number - **license_plate.number** - recognized text of the license plate - **license_plate.serial** - only for UAE numbers - license plate series - **license_plate.region** - only for UAE numbers - license plate subregion - **license_plate.color** - only for Saudi Arabia - color of the international plate code **How it works**: *go to the folder of the country, CSV-file contains all labeling information about images located in the subfolder "photos" of the corresponding folder.* ## [**TrainingData**](https://trainingdata.pro/data-market/car-license-plates?utm_source=huggingface&utm_medium=cpc&utm_campaign=license_plates) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

AnonymousSub/recipe_RL_data_roberta-base

--- annotations_creators: [] language: - en language_creators: [] license: [] multilinguality: - monolingual pretty_name: recipe RL roberta base size_categories: [] source_datasets: [] tags: [] task_categories: [] task_ids: [] --- # Dataset Description ## Structure - Consists of 5 fields - Each row corresponds to a policy - sequence of actions, given an initial `<START>` state, and corresponding rewards at each step. ## Fields `steps`, `step_attn_masks`, `rewards`, `actions`, `dones` ## Field descriptions - `steps` (List of lists of `Int`s) - tokenized step tokens of all the steps in the policy sequence (here we use the `roberta-base` tokenizer, as `roberta-base` would be used to encode each step of a recipe) - `step_attn_masks` (List of lists of `Int`s) - Attention masks corresponding to `steps` - `rewards` (List of `Float`s) - Sequence of rewards (normalized b/w 0 and 1) assigned per step. - `actions` (List of lists of `Int`s) - Sequence of actions (one-hot encoded, as the action space is discrete). There are `33` different actions possible (we consider the maximum number of steps per recipe = `16`, so the action can vary from `-16` to `+16`; The class label is got by adding 16 to the actual action value) - `dones` (List of `Bool`) - Sequence of flags, conveying if the work is completed when that step is reached, or not. ## Dataset Size - Number of rows = `2255673` - Maximum number of steps per row = `16`

chansung/test

--- license: apache-2.0 ---

Falah/village4kids_2_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 2094 num_examples: 8 download_size: 2965 dataset_size: 2094 --- # Dataset Card for "village4kids_2_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

aisc-team-a1/guidelines

--- license: other license_name: common-crawl license_link: LICENSE task_categories: - text-generation language: - en pretty_name: Clinical Guidelines size_categories: - 10K<n<100K tags: - medical - health dataset_info: features: - name: id dtype: string - name: source dtype: string - name: title dtype: string - name: clean_text dtype: string - name: raw_text dtype: string - name: url dtype: string - name: overview dtype: string splits: - name: train num_bytes: 865223621 num_examples: 37970 download_size: 424262411 dataset_size: 865223621 configs: - config_name: default data_files: - split: train path: data/train-* --- *This is a dataset repository made for the AISC class at Harvard Medical School. Please find the original dataset repository here: https://huggingface.co/datasets/epfl-llm/guidelines* ### 🎉 **NEW DROP** 🎉 PubMed Guidelines We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas! # Clinical Guidelines The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the [Meditron](https://huggingface.co/epfl-llm/meditron-70b) Large Language Model (LLM). We publicly release a subset of 37K articles from our Guidelines corpus, extracted from 9 of 17 sources that allow content redistribution, namely CCO, CDC, CMA, ICRC, NICE, PubMed, SPOR, WHO and WikiDoc. You can scrape and clean all 17 guideline sources using our code in [epfLLM/meditron](https://github.com/epfLLM/meditron). <img width=75% src="sources.png" alt="Sources of Clinical Practice Guidelines" title="CPG sources"> ## Dataset Details  - **Curated by:** [EPFL LLM Team](https://huggingface.co/epfl-llm) - **Language(s):** English only - **License:** [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) - **Repository:** [epfLLM/meditron](https://github.com/epfLLM/meditron) - **Paper:** *[MediTron-70B: Scaling Medical Pretraining for Large Language Models](https://arxiv.org/abs/2311.16079)* - **Knowledge Cutoff**: August 2023 ## Dataset Creation ### Curation Rationale  The dataset was curated to provide a high-quality collection of clinical practice guidelines (CPGs) for the medical training of LLMs. Our Clinical Guidelines corpus comprises 48,096 articles from 17 globally recognized sources for clinician and patient-directed guidance across high and low-resource settings, multiple medical domains (internal medicine, pediatrics, oncology, infectious disease, etc.) and multiple geographical locations. ### Source Data  Clinical practice guidelines are rigorously researched frameworks designed to guide healthcare practitioners and patients in making evidence-based decisions regarding diagnosis, treatment, and management. They are compiled through a systematic process of collaborative consensus between experts to establish recommendations from the latest evidence on best practices that would maximize benefit in light of practical concerns such as available resources and context. As a super-synthesis of meta-analyses, they sit atop the *evidence pyramid* and form the basis of actionable evidence-based practice. Clinical guidelines differ based on several factors: - **Organizational level**: CPGs are produced at various organizational granularities, ranging from global to hospital-level initiatives directed by international professional medical associations to informal consortia, regional or national governmental bodies to individual NGOs and hospitals. - **Geographic scope**: The geographic scope ranges from global (WHO) to national (CDC, NICE) and regional (Ontario, Melbourne) to institutional (ICRC, Mayo Clinic). This corpus is biased towards English-speaking regions due to its exclusive focus on English content. - **Resource level**: The corpus also represents health care concerns from high- (Ontario, Melbourne), low- (WHO), and volatile- (ICRC) resource settings. - **Audience level**: Guidelines also contains a range of technical and conversational vocabulary with target audiences of clinicians or patients (or both), and is sometimes highly specialized within a theme (cancer, pediatrics, infectious disease). - **Peer-review**: The peer review processes also ranged from UN bodies (WHO), institutional review boards (ICRC), professional associations (AAFP) to publicly crowdsourced knowledge bases (WikiDoc). - **Document size**: Article length varies widely from very short statements to 100+ page guides. #### Who are the source data producers?  The dataset is sourced from 17 globally recognized medical entities, covering a wide range of healthcare contexts and audiences. We employed pragmatic selection criteria over medical sources, seeking CPGs that were: - (1) open-access - (2) systematically formatted with homogenous textual structure (i.e., in a format in which automated processes could be deployed without excessive risk of misaligning textual sequences) - (3) in the language predominantly represented by the pre-training corpus of Llama (i.e., English) - (4) covering a breadth of medical sub-domains, audiences (clinician, nurse, patient), and resource settings (high, low, and humanitarian response settings) | Source | Full Name | Tag | Guidelines | Words | Audience | Country | Released | |-|-|-|-|-|-|-|-| | **[AAFP](https://www.aafp.org)** | American Academy of Family Physicians | `aafp` | 50 | 9.4K | Doctor | USA | No | | **[CCO](https://www.cancercareontario.ca/en/guidelines-advice)** | Cancer Care Ontario | `cco` | 87 | 199K | Doctor | Canada | **Yes** | | **[CDC](https://www.cdc.gov/)** | Center for Disease Control and Prevention | `cdc` | 621 | 6.7M | Doctor | USA | **Yes** | | **[CMA](https://joulecma.ca/)** | Canadian Medical Association | `cma` | 431 | 1.7M | Doctor | Canada | **Yes** | | **[CPS](https://cps.ca)** | Canadian Paediatric Society | `cps` | 54 | 133K | Doctor | Canada | No | | **[drugs.com](https://www.drugs.com/)** | Drugs.com | `drugs` | 6548 | 4.1M | Both | International | No | | **[GuidelineCentral](https://www.guidelinecentral.com/)** | GuidelineCentral | `gc` | 1029 | 1M | Doctor | Mix | No | | **[ICRC](http://icrc.org/)** | International Committee of the Red Cross | `icrc` | 49 | 1.2M | Doctor | International | **Yes** | | **[IDSA](https://www.idsociety.org/)** | Infectious Diseases Society of America | `idsa` | 47 | 646K | Doctor | USA | No | | **[MAGIC](https://magicevidence.org/)** | Making GRADE The Irresistible Choice | `magic` | 52 | 415K | Doctor | Mix | No | | **[MayoClinic](https://www.mayoclinic.org/)** | MayoClinic | `mayo` | 1100 | 2.2M | Patient | USA | No | | **[NICE](https://www.nice.org.uk/guidance)** | National Institute for Health and Care Excellence | `nice` | 1656 | 8.1M | Doctor | UK | **Yes** | | **[PubMed](https://pubmed.ncbi.nlm.nih.gov)** | PubMed | `pubmed` | 1627 | 10.8M | Doctor | Mix | **Yes** | | **[RCH](https://www.rch.org.au/clinicalguide/about_rch_cpgs/welcome_to_the_clinical_practice_guidelines/)** | Royal Children's Hospital Melbourne | `rch` | 384 | 410K | Doctor | Australia | No | | **[SPOR](https://sporevidencealliance.ca/key-activities/cpg-asset-map/cpg-database/)** | Strategy for Patient-Oriented Research | `spor` | 217 | 1.1M | Doctor | Canada | **Yes** | | **[WHO](https://www.who.int/publications/who-guidelines)** | World Health Organization | `who` | 223 | 3.1M | Both | International | **Yes** | | **[WikiDoc](https://www.wikidoc.org/)** | WikiDoc | `wikidoc` | 33058 | 34M | Both | International | **Yes** | #### Data Collection and Processing  PDF documents were converted to text using [GROBID](https://github.com/kermitt2/grobid). After extracting the raw text from each source, we cleaned data with an ad-hoc process to exclude irrelevant or repetitive content that did not contribute to the textual content, such as URLs, references, figures, table delimiters, and ill-formatted characters. This filtering procedure was performed differently for each source using a sample of 50 articles. Please note that this procedure is not perfect, as it may have removed useful information or kept superfluous content. We provide the `raw_text` for each article if you would like to perform your own cleaning step. Additionally, the text was standardized to a unified format with hierarchical section headers indicated by `'#'`, homogenous spacing `'\n\n'` separating paragraphs, and normalized lists formatted with `'- '` bullet points. Finally, all samples were deduplicated using title matching, and articles that were too short or not English were filtered out. #### Personal and Sensitive Information  As the articles are publicly accessible, no personal or sensitive information is included. ## Dataset Structure  Each row of the dataset represents one clinical practice guideline article, and consists of the following dataset fields (all strings): | Field | Description | Sources with field | |-------------|-------------------------------------------|------------------------------| | `id` | Unique identifier for each article | All | | `source` | Source tag (`cco`, `cdc`, `cma`, `icrc`, `nice`, `spor`, `who` or `wikidoc`)| All | | `title` | Title of the article | CMA, NICE & WikiDoc | | `url` | URL of the article | NICE, WikiDoc & PubMed | | `raw_text` | Unprocessed scraped article text | All | | `clean_text`| Cleaned and formatted article text | All | | `overview` | Short summary or abstract of the article | NICE & Pubmed | ## Uses  The dataset is intended for use in tasks related to text generation, specifically in the context of clinical practice guidelines. It can be employed for training language models and other natural language processing applications within the healthcare domain. ### Out-of-Scope Use  - **Redistribution**: Please always check redistribution licenses before using the content as these may also evolve over time. To the best of our knowledge, we are following the redistribution licensing of each source and we invite users to inform us if that is not the case. - **Malicious use**: We do not support any use of this corpus that may be harmful. Creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. please do not create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice. ## Bias, Risks, and Limitations  - **Peer-Review Quality**: It is important to understand that while most sources are validated by internationally endorsed professional associations, a large proportion of articles are from Wikidoc which contains crowdsourced content. While edits in Wikidoc are generally restricted to expert review, the process of consensus and oversight is different from the traditional rigor of clinical guidelines. - **Representation**: This corpus is in English, and over-represents English-speaking regions. While we have included WHO and ICRC guidelines for low-resource settings, further work needs to be done to scrape sources from diverse contexts. - **Temporal scope**: Guidelines are constantly updated and these represent a snapshot of each in August 2023. Please re-scrape for updated content. ### Recommendations  We warmly invite users to help us build a more representative corpus with high-quality peer-reviewed clinical practice guidelines in various languages and representing the full scope of clinical specialties and geographic regions. We encourage users of this content to be mindful of its current limitations in temporal and geographic scope and we repeat our warning: creating tools that provide clinical advice is commendable, but extremely dangerous if not done with the appropriate care. Such tools need to be validated for safety and utility by medical professionals in randomized controlled trials. i.e. Please don’t create cowboy health apps that fool vulnerable users into thinking they are receiving validated advice. ## Acknowledgments The availability of open-access clinical practice guidelines (CPG) was critical to this work, and we thank all the societies listed above. A broader representation of geography, medical specialties, and contexts (especially low-resource settings) could be achieved through more standardized CPG formatting practices to ensure reliable textual extraction (e.g., releasing `.txt` or `.html` versions with structured content). We encourage the CPG community to continue to make these documents available (open-access with permissive licenses for incorporation into large language models) and easily usable. ## Authors - **Curation**: Mary-Anne Hartley - **Scraping**: Antoine Bonnet, Alexandre Sallinen, Igor Krawczuk, Kyle Matoba - **Cleaning**: Antoine Bonnet, Alexandre Sallinen ## Citation  If you use the Clinical Guidelines corpus, please cite out work: ``` @misc{chen2023meditron70b, title={MEDITRON-70B: Scaling Medical Pretraining for Large Language Models}, author={Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, year={2023}, eprint={2311.16079}, archivePrefix={arXiv}, primaryClass={cs.CL} } @software{epfmedtrn, author = {Zeming Chen and Alejandro Hernández-Cano and Angelika Romanou and Antoine Bonnet and Kyle Matoba and Francesco Salvi and Matteo Pagliardini and Simin Fan and Andreas Köpf and Amirkeivan Mohtashami and Alexandre Sallinen and Alireza Sakhaeirad and Vinitra Swamy and Igor Krawczuk and Deniz Bayazit and Axel Marmet and Syrielle Montariol and Mary-Anne Hartley and Martin Jaggi and Antoine Bosselut}, title = {MediTron-70B: Scaling Medical Pretraining for Large Language Models}, month = November, year = 2023, url = {https://github.com/epfLLM/meditron} } ```

llm-aes/meva_full_rate_explain

--- dataset_info: features: - name: task_id dtype: string - name: worker_id dtype: string - name: human_label dtype: int64 - name: llm_label dtype: int64 - name: generator_1 dtype: string - name: generator_2 dtype: string - name: premise dtype: string splits: - name: train num_bytes: 391250 num_examples: 2000 download_size: 49350 dataset_size: 391250 configs: - config_name: default data_files: - split: train path: data/train-* ---

CyberHarem/rumi_bluearchive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of rumi/朱城ルミ/瑠美 (Blue Archive) This is the dataset of rumi/朱城ルミ/瑠美 (Blue Archive), containing 207 images and their tags. The core tags of this character are `brown_hair, animal_ears, long_hair, breasts, fox_ears, halo, hair_between_eyes, large_breasts, purple_eyes, crossed_bangs, red_halo, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 207 | 356.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumi_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 1200 | 207 | 300.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumi_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 520 | 598.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rumi_bluearchive/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/rumi_bluearchive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, black_skirt, blush, brown_pantyhose, looking_at_viewer, smile, solo, shirt, side_slit, simple_background, white_background, hand_on_own_hip, open_mouth, thigh_strap, black_pantyhose, gourd, rope_belt, short_sleeves, thighs | | 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, black_skirt, pantyhose, smile, solo, blush, looking_at_viewer, shirt, holding, sweat, steam | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blush, solo, covered_nipples, looking_at_viewer, simple_background, smile, sweat, white_background, red_ascot, black_skirt, wet_shirt, see-through_shirt, upper_body | | 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, blush, looking_at_viewer, sweat, bra_visible_through_clothes, see-through_shirt, solo, wet_shirt, simple_background, black_bra, black_skirt, hand_on_own_hip, open_mouth, smile, steaming_body, high-waist_skirt, pantyhose, short_sleeves | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, looking_at_viewer, solo, blush, simple_background, white_background, one_eye_closed, chinese_clothes, black_pantyhose, sweat, thigh_strap, grin, sitting, upper_body | | 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) | 1boy, 1girl, blush, solo_focus, smile, looking_at_viewer, penis, nipples, pov, breasts_squeezed_together, cum_on_breasts, sweat, censored, huge_breasts, open_mouth, paizuri_under_clothes, shirt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_skirt | blush | brown_pantyhose | looking_at_viewer | smile | solo | shirt | side_slit | simple_background | white_background | hand_on_own_hip | open_mouth | thigh_strap | black_pantyhose | gourd | rope_belt | short_sleeves | thighs | pantyhose | holding | sweat | steam | covered_nipples | red_ascot | wet_shirt | see-through_shirt | upper_body | bra_visible_through_clothes | black_bra | steaming_body | high-waist_skirt | one_eye_closed | chinese_clothes | grin | sitting | 1boy | solo_focus | penis | nipples | pov | breasts_squeezed_together | cum_on_breasts | censored | huge_breasts | paizuri_under_clothes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------|:------------------|:--------------------|:--------|:-------|:--------|:------------|:--------------------|:-------------------|:------------------|:-------------|:--------------|:------------------|:--------|:------------|:----------------|:---------|:------------|:----------|:--------|:--------|:------------------|:------------|:------------|:--------------------|:-------------|:------------------------------|:------------|:----------------|:-------------------|:-----------------|:------------------|:-------|:----------|:-------|:-------------|:--------|:----------|:------|:----------------------------|:-----------------|:-----------|:---------------|:------------------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | X | X | X | | | X | X | | | | | | | | | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 3 | 8 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | X | X | X | | | X | | X | X | | | | | X | | X | | X | | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | 4 | 8 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | X | | X | | X | | | X | X | | | X | X | | | | | | | X | | | | | | X | | | | | X | X | X | X | | | | | | | | | | | | 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 |

djfelipe/vocall

--- license: openrail ---

1aurent/RxRx1

--- license: cc-by-4.0 size_categories: - 100K<n<1M task_categories: - image-classification tags: - biology - drug - cells configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: image dtype: array3_d: dtype: uint8 shape: - 512 - 512 - 6 - name: site_id dtype: string - name: well_id dtype: string - name: cell_type dtype: string - name: experiment dtype: string - name: plate dtype: int32 - name: well dtype: string - name: site dtype: int32 - name: well_type dtype: class_label: names: '0': treatment '1': positive_control '2': negative_control - name: sirna dtype: string - name: sirna_id dtype: int32 - name: embeddings sequence: float32 length: 128 splits: - name: train num_bytes: 213139738276 num_examples: 81224 - name: test num_bytes: 116210798412 num_examples: 44286 dataset_size: 329350536688 --- [![DOI](https://zenodo.org/badge/DOI/10.48550/arXiv.2301.05768.svg)](https://doi.org/10.48550/arXiv.2301.05768) # RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods ![](https://cdn-uploads.huggingface.co/production/uploads/6364f1784f773b7e4cede70c/Nex4TagKJ2NFOZjOm26E-.png) **Homepage**: https://www.rxrx.ai/rxrx1 \ **Publication Date**: 2019-06 \ **License**: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) \ **Citation**: ```bibtex @misc{sypetkowski2023rxrx1, title = {RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods}, author = {Maciej Sypetkowski and Morteza Rezanejad and Saber Saberian and Oren Kraus and John Urbanik and James Taylor and Ben Mabey and Mason Victors and Jason Yosinski and Alborz Rezazadeh Sereshkeh and Imran Haque and Berton Earnshaw}, year = {2023}, eprint = {2301.05768}, archiveprefix = {arXiv}, primaryclass = {cs.CV} } ``` ## Description High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the technical execution of different experimental batches within such screens confound these observations and can lead to invalid biological conclusions. It is therefore necessary to account for these batch effects when analyzing outcomes. In this paper we describe RxRx1, a biological dataset designed specifically for the systematic study of batch effect correction methods. The dataset consists of 125,510 high-resolution fluorescence microscopy images of human cells under 1,138 genetic perturbations in 51 experimental batches across 4 cell types. Visual inspection of the images alone clearly demonstrates significant batch effects. We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task. Our goal in releasing RxRx1 is to encourage the development of effective experimental batch correction methods that generalize well to unseen experimental batches.

open-llm-leaderboard/details_FelixChao__Gemma-10.2B-Coder

--- pretty_name: Evaluation run of FelixChao/Gemma-10.2B-Coder dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [FelixChao/Gemma-10.2B-Coder](https://huggingface.co/FelixChao/Gemma-10.2B-Coder)\ \ 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_FelixChao__Gemma-10.2B-Coder\"\ ,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\ These are the [latest results from run 2024-03-21T14:06:19.977620](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Gemma-10.2B-Coder/blob/main/results_2024-03-21T14-06-19.977620.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\": 0.6193276450638524,\n\ \ \"acc_stderr\": 0.032595355839530035,\n \"acc_norm\": 0.6224484569319588,\n\ \ \"acc_norm_stderr\": 0.03325145393228843,\n \"mc1\": 0.34394124847001223,\n\ \ \"mc1_stderr\": 0.016629087514276775,\n \"mc2\": 0.5243704066589732,\n\ \ \"mc2_stderr\": 0.015065637058082886\n },\n \"harness|arc:challenge|25\"\ : {\n \"acc\": 0.5597269624573379,\n \"acc_stderr\": 0.014506769524804246,\n\ \ \"acc_norm\": 0.5870307167235495,\n \"acc_norm_stderr\": 0.014388344935398326\n\ \ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.617307309300936,\n\ \ \"acc_stderr\": 0.004850508945116089,\n \"acc_norm\": 0.8203545110535749,\n\ \ \"acc_norm_stderr\": 0.0038310732859630774\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\ : {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \ \ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \ \ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n\ \ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n\ \ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\ : {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952929,\n\ \ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952929\n\ \ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\ \ \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \ \ \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\ : {\n \"acc\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319878,\n\ \ \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319878\n\ \ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\ \ \"acc_stderr\": 0.03716177437566016,\n \"acc_norm\": 0.7291666666666666,\n\ \ \"acc_norm_stderr\": 0.03716177437566016\n },\n \"harness|hendrycksTest-college_chemistry|5\"\ : {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \ \ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\ \ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n\ \ \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\"\ : 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \ \ \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\"\ : 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\"\ : {\n \"acc\": 0.6069364161849711,\n \"acc_stderr\": 0.03724249595817729,\n\ \ \"acc_norm\": 0.6069364161849711,\n \"acc_norm_stderr\": 0.03724249595817729\n\ \ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n\ \ \"acc_stderr\": 0.04858083574266346,\n \"acc_norm\": 0.39215686274509803,\n\ \ \"acc_norm_stderr\": 0.04858083574266346\n },\n \"harness|hendrycksTest-computer_security|5\"\ : {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \ \ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \ \ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\ \ 0.6085106382978723,\n \"acc_stderr\": 0.03190701242326812,\n \"\ acc_norm\": 0.6085106382978723,\n \"acc_norm_stderr\": 0.03190701242326812\n\ \ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n\ \ \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n\ \ \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\ : {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916747,\n\ \ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916747\n\ \ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\ : 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055273,\n \"\ acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055273\n\ \ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\ \ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\ \ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\ : {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \ \ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \ \ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\ : 0.7387096774193549,\n \"acc_stderr\": 0.024993053397764812,\n \"\ acc_norm\": 0.7387096774193549,\n \"acc_norm_stderr\": 0.024993053397764812\n\ \ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\ : 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n \"\ acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n\ \ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \ \ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\ : 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\ : {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.0352439084451178,\n\ \ \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.0352439084451178\n\ \ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\ : 0.8232323232323232,\n \"acc_stderr\": 0.027178752639044915,\n \"\ acc_norm\": 0.8232323232323232,\n \"acc_norm_stderr\": 0.027178752639044915\n\ \ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\ \ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\ \ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\ \ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \ \ \"acc\": 0.5717948717948718,\n \"acc_stderr\": 0.025088301454694834,\n\ \ \"acc_norm\": 0.5717948717948718,\n \"acc_norm_stderr\": 0.025088301454694834\n\ \ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\ acc\": 0.32592592592592595,\n \"acc_stderr\": 0.028578348365473072,\n \ \ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.028578348365473072\n\ \ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \ \ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.03095663632856655,\n \ \ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.03095663632856655\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\"\ : 0.8018348623853211,\n \"acc_stderr\": 0.017090573804217905,\n \"\ acc_norm\": 0.8018348623853211,\n \"acc_norm_stderr\": 0.017090573804217905\n\ \ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\ : 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\ acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\ \ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\ : 0.7745098039215687,\n \"acc_stderr\": 0.029331162294251735,\n \"\ acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.029331162294251735\n\ \ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\ acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \ \ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\ \ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6457399103139013,\n\ \ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\ \ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\ : {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467765,\n\ \ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467765\n\ \ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\ \ 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547129,\n \"\ acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547129\n\ \ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\ \ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \ \ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\ : {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\ \ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\ \ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\ \ \"acc_stderr\": 0.04738975119274155,\n \"acc_norm\": 0.4732142857142857,\n\ \ \"acc_norm_stderr\": 0.04738975119274155\n },\n \"harness|hendrycksTest-management|5\"\ : {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\ \ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\ \ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\ \ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\ \ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\ : {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \ \ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \ \ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\ \ \"acc_stderr\": 0.013964393769899136,\n \"acc_norm\": 0.8122605363984674,\n\ \ \"acc_norm_stderr\": 0.013964393769899136\n },\n \"harness|hendrycksTest-moral_disputes|5\"\ : {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.02599247202930639,\n\ \ \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.02599247202930639\n\ \ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3743016759776536,\n\ \ \"acc_stderr\": 0.016185444179457175,\n \"acc_norm\": 0.3743016759776536,\n\ \ \"acc_norm_stderr\": 0.016185444179457175\n },\n \"harness|hendrycksTest-nutrition|5\"\ : {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145894,\n\ \ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145894\n\ \ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6527331189710611,\n\ \ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\ \ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness|hendrycksTest-prehistory|5\"\ : {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495026,\n\ \ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495026\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\": 0.44654498044328556,\n\ \ \"acc_stderr\": 0.012697046024399687,\n \"acc_norm\": 0.44654498044328556,\n\ \ \"acc_norm_stderr\": 0.012697046024399687\n },\n \"harness|hendrycksTest-professional_medicine|5\"\ : {\n \"acc\": 0.5257352941176471,\n \"acc_stderr\": 0.030332578094555026,\n\ \ \"acc_norm\": 0.5257352941176471,\n \"acc_norm_stderr\": 0.030332578094555026\n\ \ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\ acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \ \ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\ \ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\ \ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\ \ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\ : {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.02853556033712845,\n\ \ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.02853556033712845\n\ \ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8159203980099502,\n\ \ \"acc_stderr\": 0.027403859410786848,\n \"acc_norm\": 0.8159203980099502,\n\ \ \"acc_norm_stderr\": 0.027403859410786848\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\ : {\n \"acc\": 0.83,\n \"acc_stderr\": 0.037752516806863715,\n \ \ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.037752516806863715\n \ \ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\ \ \"acc_stderr\": 0.03878626771002361,\n \"acc_norm\": 0.5421686746987951,\n\ \ \"acc_norm_stderr\": 0.03878626771002361\n },\n \"harness|hendrycksTest-world_religions|5\"\ : {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368032,\n\ \ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368032\n\ \ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.34394124847001223,\n\ \ \"mc1_stderr\": 0.016629087514276775,\n \"mc2\": 0.5243704066589732,\n\ \ \"mc2_stderr\": 0.015065637058082886\n },\n \"harness|winogrande|5\"\ : {\n \"acc\": 0.7837411207576953,\n \"acc_stderr\": 0.011570614861409357\n\ \ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5011372251705838,\n \ \ \"acc_stderr\": 0.013772449096346838\n }\n}\n```" repo_url: https://huggingface.co/FelixChao/Gemma-10.2B-Coder leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard point_of_contact: clementine@hf.co configs: - config_name: harness_arc_challenge_25 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|arc:challenge|25_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|arc:challenge|25_2024-03-21T14-06-19.977620.parquet' - config_name: harness_gsm8k_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|gsm8k|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|gsm8k|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hellaswag_10 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hellaswag|10_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hellaswag|10_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-management|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-06-19.977620.parquet' - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_abstract_algebra_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_anatomy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_astronomy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_business_ethics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_clinical_knowledge_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_biology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_chemistry_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_computer_science_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_mathematics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_medicine_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_college_physics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_computer_security_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_conceptual_physics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_econometrics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_electrical_engineering_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_elementary_mathematics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_formal_logic_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_global_facts_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_biology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_chemistry_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_computer_science_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_european_history_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_geography_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_government_and_politics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_macroeconomics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_mathematics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_microeconomics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_physics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_psychology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_statistics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_us_history_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_high_school_world_history_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_human_aging_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_human_sexuality_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_international_law_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_jurisprudence_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_logical_fallacies_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_machine_learning_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_management_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-management|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_marketing_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_medical_genetics_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_miscellaneous_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_moral_disputes_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_moral_scenarios_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_nutrition_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_philosophy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_prehistory_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_accounting_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_law_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_medicine_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_professional_psychology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_public_relations_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_security_studies_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_sociology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_us_foreign_policy_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_virology_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_hendrycksTest_world_religions_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-06-19.977620.parquet' - config_name: harness_truthfulqa_mc_0 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|truthfulqa:mc|0_2024-03-21T14-06-19.977620.parquet' - config_name: harness_winogrande_5 data_files: - split: 2024_03_21T14_06_19.977620 path: - '**/details_harness|winogrande|5_2024-03-21T14-06-19.977620.parquet' - split: latest path: - '**/details_harness|winogrande|5_2024-03-21T14-06-19.977620.parquet' - config_name: results data_files: - split: 2024_03_21T14_06_19.977620 path: - results_2024-03-21T14-06-19.977620.parquet - split: latest path: - results_2024-03-21T14-06-19.977620.parquet --- # Dataset Card for Evaluation run of FelixChao/Gemma-10.2B-Coder  Dataset automatically created during the evaluation run of model [FelixChao/Gemma-10.2B-Coder](https://huggingface.co/FelixChao/Gemma-10.2B-Coder) 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_FelixChao__Gemma-10.2B-Coder", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-03-21T14:06:19.977620](https://huggingface.co/datasets/open-llm-leaderboard/details_FelixChao__Gemma-10.2B-Coder/blob/main/results_2024-03-21T14-06-19.977620.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": 0.6193276450638524, "acc_stderr": 0.032595355839530035, "acc_norm": 0.6224484569319588, "acc_norm_stderr": 0.03325145393228843, "mc1": 0.34394124847001223, "mc1_stderr": 0.016629087514276775, "mc2": 0.5243704066589732, "mc2_stderr": 0.015065637058082886 }, "harness|arc:challenge|25": { "acc": 0.5597269624573379, "acc_stderr": 0.014506769524804246, "acc_norm": 0.5870307167235495, "acc_norm_stderr": 0.014388344935398326 }, "harness|hellaswag|10": { "acc": 0.617307309300936, "acc_stderr": 0.004850508945116089, "acc_norm": 0.8203545110535749, "acc_norm_stderr": 0.0038310732859630774 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5333333333333333, "acc_stderr": 0.043097329010363554, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.043097329010363554 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7236842105263158, "acc_stderr": 0.03639057569952929, "acc_norm": 0.7236842105263158, "acc_norm_stderr": 0.03639057569952929 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6264150943396226, "acc_stderr": 0.029773082713319878, "acc_norm": 0.6264150943396226, "acc_norm_stderr": 0.029773082713319878 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7291666666666666, "acc_stderr": 0.03716177437566016, "acc_norm": 0.7291666666666666, "acc_norm_stderr": 0.03716177437566016 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.4, "acc_stderr": 0.04923659639173309, "acc_norm": 0.4, "acc_norm_stderr": 0.04923659639173309 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.45, "acc_stderr": 0.05, "acc_norm": 0.45, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6069364161849711, "acc_stderr": 0.03724249595817729, "acc_norm": 0.6069364161849711, "acc_norm_stderr": 0.03724249595817729 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266346, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266346 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.044084400227680794, "acc_norm": 0.74, "acc_norm_stderr": 0.044084400227680794 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6085106382978723, "acc_stderr": 0.03190701242326812, "acc_norm": 0.6085106382978723, "acc_norm_stderr": 0.03190701242326812 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6206896551724138, "acc_stderr": 0.04043461861916747, "acc_norm": 0.6206896551724138, "acc_norm_stderr": 0.04043461861916747 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4126984126984127, "acc_stderr": 0.025355741263055273, "acc_norm": 0.4126984126984127, "acc_norm_stderr": 0.025355741263055273 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.47619047619047616, "acc_stderr": 0.04467062628403273, "acc_norm": 0.47619047619047616, "acc_norm_stderr": 0.04467062628403273 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7387096774193549, "acc_stderr": 0.024993053397764812, "acc_norm": 0.7387096774193549, "acc_norm_stderr": 0.024993053397764812 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4630541871921182, "acc_stderr": 0.035083705204426656, "acc_norm": 0.4630541871921182, "acc_norm_stderr": 0.035083705204426656 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.64, "acc_stderr": 0.048241815132442176, "acc_norm": 0.64, "acc_norm_stderr": 0.048241815132442176 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7151515151515152, "acc_stderr": 0.0352439084451178, "acc_norm": 0.7151515151515152, "acc_norm_stderr": 0.0352439084451178 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8232323232323232, "acc_stderr": 0.027178752639044915, "acc_norm": 0.8232323232323232, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8652849740932642, "acc_stderr": 0.024639789097709443, "acc_norm": 0.8652849740932642, "acc_norm_stderr": 0.024639789097709443 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5717948717948718, "acc_stderr": 0.025088301454694834, "acc_norm": 0.5717948717948718, "acc_norm_stderr": 0.025088301454694834 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32592592592592595, "acc_stderr": 0.028578348365473072, "acc_norm": 0.32592592592592595, "acc_norm_stderr": 0.028578348365473072 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6512605042016807, "acc_stderr": 0.03095663632856655, "acc_norm": 0.6512605042016807, "acc_norm_stderr": 0.03095663632856655 }, "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": 0.8018348623853211, "acc_stderr": 0.017090573804217905, "acc_norm": 0.8018348623853211, "acc_norm_stderr": 0.017090573804217905 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7745098039215687, "acc_stderr": 0.029331162294251735, "acc_norm": 0.7745098039215687, "acc_norm_stderr": 0.029331162294251735 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7805907172995781, "acc_stderr": 0.026939106581553945, "acc_norm": 0.7805907172995781, "acc_norm_stderr": 0.026939106581553945 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6457399103139013, "acc_stderr": 0.032100621541349864, "acc_norm": 0.6457399103139013, "acc_norm_stderr": 0.032100621541349864 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7099236641221374, "acc_stderr": 0.03980066246467765, "acc_norm": 0.7099236641221374, "acc_norm_stderr": 0.03980066246467765 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8429752066115702, "acc_stderr": 0.03321244842547129, "acc_norm": 0.8429752066115702, "acc_norm_stderr": 0.03321244842547129 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.75, "acc_stderr": 0.04186091791394607, "acc_norm": 0.75, "acc_norm_stderr": 0.04186091791394607 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7668711656441718, "acc_stderr": 0.0332201579577674, "acc_norm": 0.7668711656441718, "acc_norm_stderr": 0.0332201579577674 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.04738975119274155, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.04738975119274155 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.76, "acc_stderr": 0.042923469599092816, "acc_norm": 0.76, "acc_norm_stderr": 0.042923469599092816 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8122605363984674, "acc_stderr": 0.013964393769899136, "acc_norm": 0.8122605363984674, "acc_norm_stderr": 0.013964393769899136 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.630057803468208, "acc_stderr": 0.02599247202930639, "acc_norm": 0.630057803468208, "acc_norm_stderr": 0.02599247202930639 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3743016759776536, "acc_stderr": 0.016185444179457175, "acc_norm": 0.3743016759776536, "acc_norm_stderr": 0.016185444179457175 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6895424836601307, "acc_stderr": 0.026493033225145894, "acc_norm": 0.6895424836601307, "acc_norm_stderr": 0.026493033225145894 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6527331189710611, "acc_stderr": 0.027040745502307336, "acc_norm": 0.6527331189710611, "acc_norm_stderr": 0.027040745502307336 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7098765432098766, "acc_stderr": 0.025251173936495026, "acc_norm": 0.7098765432098766, "acc_norm_stderr": 0.025251173936495026 }, "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": 0.44654498044328556, "acc_stderr": 0.012697046024399687, "acc_norm": 0.44654498044328556, "acc_norm_stderr": 0.012697046024399687 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.5257352941176471, "acc_stderr": 0.030332578094555026, "acc_norm": 0.5257352941176471, "acc_norm_stderr": 0.030332578094555026 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6764705882352942, "acc_stderr": 0.018926082916083383, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.018926082916083383 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302506, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302506 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.726530612244898, "acc_stderr": 0.02853556033712845, "acc_norm": 0.726530612244898, "acc_norm_stderr": 0.02853556033712845 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8159203980099502, "acc_stderr": 0.027403859410786848, "acc_norm": 0.8159203980099502, "acc_norm_stderr": 0.027403859410786848 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.83, "acc_stderr": 0.037752516806863715, "acc_norm": 0.83, "acc_norm_stderr": 0.037752516806863715 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.03878626771002361, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.03878626771002361 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8128654970760234, "acc_stderr": 0.029913127232368032, "acc_norm": 0.8128654970760234, "acc_norm_stderr": 0.029913127232368032 }, "harness|truthfulqa:mc|0": { "mc1": 0.34394124847001223, "mc1_stderr": 0.016629087514276775, "mc2": 0.5243704066589732, "mc2_stderr": 0.015065637058082886 }, "harness|winogrande|5": { "acc": 0.7837411207576953, "acc_stderr": 0.011570614861409357 }, "harness|gsm8k|5": { "acc": 0.5011372251705838, "acc_stderr": 0.013772449096346838 } } ``` ## 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-multi_news-default-e22c67-2252871792

--- type: predictions tags: - autotrain - evaluation datasets: - multi_news eval_info: task: summarization model: pszemraj/long-t5-tglobal-xl-16384-book-summary metrics: [] dataset_name: multi_news dataset_config: default dataset_split: test col_mapping: text: document target: summary --- # Dataset Card for AutoTrain Evaluator This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset: * Task: Summarization * Model: pszemraj/long-t5-tglobal-xl-16384-book-summary * Dataset: multi_news * Config: default * Split: test To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator). ## Contributions Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.

Falah/arabic_modern_prompts

--- dataset_info: features: - name: prompts dtype: string splits: - name: train num_bytes: 44164932 num_examples: 100000 download_size: 4499641 dataset_size: 44164932 --- # Dataset Card for "arabic_modern_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

leomiranda02/vozcaiomartins

--- license: openrail ---

joey234/mmlu-human_aging-neg-prepend-verbal

--- configs: - config_name: default data_files: - split: dev path: data/dev-* - split: test path: data/test-* dataset_info: features: - name: question dtype: string - name: choices sequence: string - name: answer dtype: class_label: names: '0': A '1': B '2': C '3': D - name: negate_openai_prompt struct: - name: content dtype: string - name: role dtype: string - name: neg_question dtype: string - name: fewshot_context dtype: string - name: ori_prompt dtype: string - name: neg_prompt dtype: string - name: fewshot_context_neg dtype: string - name: fewshot_context_ori dtype: string splits: - name: dev num_bytes: 6203 num_examples: 5 - name: test num_bytes: 1393366 num_examples: 223 download_size: 173053 dataset_size: 1399569 --- # Dataset Card for "mmlu-human_aging-neg-prepend-verbal" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ibranze/araproje_arc_tr_dynamic

--- dataset_info: features: - name: keys dtype: string - name: values sequence: string splits: - name: train num_bytes: 126841 num_examples: 250 download_size: 12407 dataset_size: 126841 configs: - config_name: default data_files: - split: train path: data/train-* ---

Biomedical-TeMU/SPACCC_Tokenizer

--- license: cc-by-4.0 --- # The Tokenizer for Clinical Cases Written in Spanish ## Introduction This repository contains the tokenization model trained using the SPACCC_TOKEN corpus (https://github.com/PlanTL-SANIDAD/SPACCC_TOKEN). The model was trained using the 90% of the corpus (900 clinical cases) and tested against the 10% (100 clinical cases). This model is a great resource to tokenize biomedical documents, specially clinical cases written in Spanish. This model was created using the Apache OpenNLP machine learning toolkit (https://opennlp.apache.org/), with the release number 1.8.4, released in December 2017. This repository contains the training set, testing set, Gold Standard. ## Prerequisites This software has been compiled with Java SE 1.8 and it should work with recent versions. You can download Java from the following website: https://www.java.com/en/download The executable file already includes the Apache OpenNLP dependencies inside, so the download of this toolkit is not necessary. However, you may download the latest version from this website: https://opennlp.apache.org/download.html The library file we have used to compile is "opennlp-tools-1.8.4.jar". The source code should be able to compile with the latest version of OpenNLP, "opennlp-tools-*RELEASE_NUMBER*.jar". In case there are compilation or execution errors, please let us know and we will make all the necessary updates. ## Directory structure <pre> exec/ An executable file that can be used to apply the tokenization to your documents. You can find the notes about its execution below in section "Usage". gold_standard/ The clinical cases used as gold standard to evaluate the model's performance. model/ The tokenizationint model, "es-tokenization-model-spaccc.bin", a binary file. src/ The source code to create the model (CreateModelTok.java) and evaluate it (EvaluateModelTok.java). The directory includes an example about how to use the model inside your code (Tokenization.java). File "abbreviations.dat" contains a list of abbreviations, essential to build the model. test_set/ The clinical cases used as test set to evaluate the model's performance. train_set/ The clinical cases used to build the model. We use a single file with all documents present in directory "train_set_docs" concatented. train_set_docs/ The clinical cases used to build the model. For each record the sentences are already splitted. </pre> ## Usage The executable file *Tokenizer.jar* is the program you need to tokenize the text in your document. For this program, two arguments are needed: (1) the text file to tokenize, and (2) the model file (*es-tokenization-model-spaccc.bin*). The program will display all tokens in the terminal, with one token per line. From the `exec` folder, type the following command in your terminal: <pre> $ java -jar Tokenizer.jar INPUT_FILE MODEL_FILE </pre> ## Examples Assuming you have the executable file, the input file and the model file in the same directory: <pre> $ java -jar Tokenizer.jar file.txt es-tokenizer-model-spaccc.bin </pre> ## Model creation To create this tokenization model, we used the following training parameters (class *TrainingParameters* in OpenNLP) to get the best performance: - Number of iterations: 1500. - Cutoff parameter: 4. - Trainer type parameter: *EventTrainer.EVENT_VALUE*. - Algorithm: Maximum Entropy (*ModelType.MAXENT.name()*). Meanwhile, we used the following parameters for the tokenizer builder (class *TokenizerFactory* in OpenNLP) to get the best performance: - Language code: *es* (for Spanish). - Abbreviation dictionary: file "abbreviations.dat" (included in the `src/` directory). - Use alphanumeric optimization: false - Alphanumeric pattern: null ## Model evaluation After tuning the model using different values for each parameter mentioned above, we got the best performance with the values mentioned above. | | Value | | ----------------------------------------: | :------ | | Number of tokens in the gold standard | 38247 | | Number of tokens generated | 38227 | | Number of words correctly tokenized | 38182 | | Number of words wrongly tokenized | 35 | | Number of tokens missed | 30 | | **Precision** | **99.88%** | | **Recall** | **99.83%** | | **F-Measure** | **99.85%**| Table 1: Evaluation statistics for the tokenization model. ## Contact Ander Intxaurrondo (ander.intxaurrondo@bsc.es) ## License <a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Copyright (c) 2018 Secretaría de Estado para el Avance Digital (SEAD)

mHossain/final_train_v2_450000

--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: input_text dtype: string - name: target_text dtype: string - name: prefix dtype: string splits: - name: train num_bytes: 9110645.1 num_examples: 27000 - name: test num_bytes: 1012293.9 num_examples: 3000 download_size: 4438870 dataset_size: 10122939.0 --- # Dataset Card for "final_train_v2_450000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

judgeou/nai3-artist-collection

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

manojpatil/training

--- language: - en size_categories: - 1K<n<10K task_categories: - summarization tags: - legal pretty_name: abc ---

CyberHarem/furutaka_azurlane

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of furutaka/古鷹/古鹰 (Azur Lane) This is the dataset of furutaka/古鷹/古鹰 (Azur Lane), containing 34 images and their tags. The core tags of this character are `long_hair, brown_hair, breasts, blue_eyes, large_breasts, animal_ears, bangs, between_breasts, hair_between_eyes`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 34 | 36.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 34 | 23.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 90 | 52.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 34 | 33.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 90 | 70.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furutaka_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/furutaka_azurlane', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 34 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, retrofit_(azur_lane), solo, detached_sleeves, looking_at_viewer, pleated_skirt, sailor_collar, blush, navel, midriff, thighhighs, crop_top, simple_background, black_skirt, miniskirt, open_mouth, gloves, white_background, wide_sleeves, smile, armpits | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | retrofit_(azur_lane) | solo | detached_sleeves | looking_at_viewer | pleated_skirt | sailor_collar | blush | navel | midriff | thighhighs | crop_top | simple_background | black_skirt | miniskirt | open_mouth | gloves | white_background | wide_sleeves | smile | armpits | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------------|:-------|:-------------------|:--------------------|:----------------|:----------------|:--------|:--------|:----------|:-------------|:-----------|:--------------------|:--------------|:------------|:-------------|:---------|:-------------------|:---------------|:--------|:----------| | 0 | 34 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

AdapterOcean/biology_dataset_standardized_cluster_3_alpaca

--- dataset_info: features: - name: input dtype: string - name: output dtype: string splits: - name: train num_bytes: 22853614 num_examples: 7463 download_size: 0 dataset_size: 22853614 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "biology_dataset_standardized_cluster_3_alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

CVasNLPExperiments/textvqa_valid_google_flan_t5_xxl_mode_OCR_VQA_Q_rices_ns_5000

--- dataset_info: features: - name: id dtype: int64 - name: question dtype: string - name: true_label sequence: string - name: prediction dtype: string splits: - name: fewshot_0 num_bytes: 934914 num_examples: 5000 download_size: 334889 dataset_size: 934914 configs: - config_name: default data_files: - split: fewshot_0 path: data/fewshot_0-* ---

benayas/snips_chatgpt_20pct_v2

--- dataset_info: features: - name: text dtype: string - name: category dtype: string - name: __index_level_0__ dtype: int64 splits: - name: train num_bytes: 1053502 num_examples: 13084 download_size: 423959 dataset_size: 1053502 configs: - config_name: default data_files: - split: train path: data/train-* ---

TheFinAI/en-acronym

--- dataset_info: features: - name: query dtype: string - name: answer dtype: string - name: text dtype: string splits: - name: test num_bytes: 142136 num_examples: 1527 download_size: 48264 dataset_size: 142136 configs: - config_name: default data_files: - split: test path: data/test-* ---

unknownX/Eddie_cartoon

--- license: other ---

ProfQu/MinecraftRecipes

--- license: mit --- # MinecraftRecipes This dataset contains all crafting table recipes from Minecraft, the recipes were taken straight from the Minecraft source and put here. The data was generated from [this repository](https://github.com/ProfessorQu/MCCraftingRecipes).

CyberHarem/nanao_yuriko_theidolmstermillionlive

--- license: mit task_categories: - text-to-image tags: - art - not-for-all-audiences size_categories: - n<1K --- # Dataset of nanao_yuriko/七尾百合子/나나오유리코 (THE iDOLM@STER: Million Live!) This is the dataset of nanao_yuriko/七尾百合子/나나오유리코 (THE iDOLM@STER: Million Live!), containing 500 images and their tags. The core tags of this character are `blue_hair, yellow_eyes, short_hair, breasts, braid, bangs, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 568.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 356.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1212 | 759.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 514.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1212 | 1.00 GiB | [Download](https://huggingface.co/datasets/CyberHarem/nanao_yuriko_theidolmstermillionlive/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/nanao_yuriko_theidolmstermillionlive', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, smile, looking_at_viewer, blush, open_mouth, book | | 1 | 26 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, solo, blush, looking_at_viewer, open_mouth, frilled_bikini, navel, hair_flower, smile, cleavage, outdoors, collarbone, green_bikini | | 2 | 18 | ![](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) | 1boy, 1girl, blush, hetero, open_mouth, solo_focus, nipples, pussy, sex, vaginal, penis, lying, navel, completely_nude, mosaic_censoring, spread_legs | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1boy, 1girl, blush, fellatio, hetero, nude, penis, solo_focus, looking_at_viewer, pov, ass, mosaic_censoring, cum_in_mouth | | 4 | 9 | ![](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, serafuku, solo, looking_at_viewer, white_shirt, blush, navel, pleated_skirt, red_neckerchief, short_sleeves, fingerless_gloves, white_background, white_skirt, black_gloves, cape, closed_mouth, midriff, simple_background, black_thighhighs, medium_hair, white_sailor_collar | | 5 | 7 | ![](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, blush, collarbone, solo, cleavage, upper_body, looking_at_viewer, simple_background, white_background, armpits, arms_up, bow_bra, navel, small_breasts, smile, underwear_only | | 6 | 8 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, blush, looking_at_viewer, open_mouth, solo, :d, bow, detached_sleeves, white_dress, bare_shoulders, collarbone, frilled_dress, hair_ribbon, puffy_short_sleeves, sleeveless_dress, yellow_ribbon, blue_sky, day, mini_hat, outdoors, sailor_dress, white_headwear, wrist_cuffs, argyle, choker, cloud, ribbon_braid, sparkle, standing, tilted_headwear, white_sailor_collar, belt_buckle, blue_thighhighs, feathers, holding, outstretched_arm, pleated_dress, shiny_hair, white_background, white_sleeves | | 7 | 6 | ![](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, detached_collar, looking_at_viewer, rabbit_ears, solo, wrist_cuffs, cleavage, fake_animal_ears, playboy_bunny, bare_shoulders, black_bowtie, blush, sitting, strapless_leotard, black_pantyhose, closed_mouth, covered_navel, cowboy_shot, holding_tray, rabbit_tail, smile, wine_glass | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | looking_at_viewer | blush | open_mouth | book | frilled_bikini | navel | hair_flower | cleavage | outdoors | collarbone | green_bikini | 1boy | hetero | solo_focus | nipples | pussy | sex | vaginal | penis | lying | completely_nude | mosaic_censoring | spread_legs | fellatio | nude | pov | ass | cum_in_mouth | serafuku | white_shirt | pleated_skirt | red_neckerchief | short_sleeves | fingerless_gloves | white_background | white_skirt | black_gloves | cape | closed_mouth | midriff | simple_background | black_thighhighs | medium_hair | white_sailor_collar | upper_body | armpits | arms_up | bow_bra | small_breasts | underwear_only | :d | bow | detached_sleeves | white_dress | bare_shoulders | frilled_dress | hair_ribbon | puffy_short_sleeves | sleeveless_dress | yellow_ribbon | blue_sky | day | mini_hat | sailor_dress | white_headwear | wrist_cuffs | argyle | choker | cloud | ribbon_braid | sparkle | standing | tilted_headwear | belt_buckle | blue_thighhighs | feathers | holding | outstretched_arm | pleated_dress | shiny_hair | white_sleeves | detached_collar | rabbit_ears | fake_animal_ears | playboy_bunny | black_bowtie | sitting | strapless_leotard | black_pantyhose | covered_navel | cowboy_shot | holding_tray | rabbit_tail | wine_glass | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:--------|:-------------|:-------|:-----------------|:--------|:--------------|:-----------|:-----------|:-------------|:---------------|:-------|:---------|:-------------|:----------|:--------|:------|:----------|:--------|:--------|:------------------|:-------------------|:--------------|:-----------|:-------|:------|:------|:---------------|:-----------|:--------------|:----------------|:------------------|:----------------|:--------------------|:-------------------|:--------------|:---------------|:-------|:---------------|:----------|:--------------------|:-------------------|:--------------|:----------------------|:-------------|:----------|:----------|:----------|:----------------|:-----------------|:-----|:------|:-------------------|:--------------|:-----------------|:----------------|:--------------|:----------------------|:-------------------|:----------------|:-----------|:------|:-----------|:---------------|:-----------------|:--------------|:---------|:---------|:--------|:---------------|:----------|:-----------|:------------------|:--------------|:------------------|:-----------|:----------|:-------------------|:----------------|:-------------|:----------------|:------------------|:--------------|:-------------------|:----------------|:---------------|:----------|:--------------------|:------------------|:----------------|:--------------|:---------------|:--------------|:-------------| | 0 | 25 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 26 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 18 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | | X | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | X | | | | | | | | | | X | X | X | | | | | X | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 8 | ![](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 | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 7 | 6 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | 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Lo/adapt-pre-trained-VL-models-to-text-data-LXMERT-finetune

--- language: - en license: - mit multilinguality: - monolingual --- The LXMERT text finetune data used to train visual features for the adaption of vision-and-language models to text-only tasks in the paper "How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?". The data has been created from the data made available by the [LXMERT repo](https://github.com/airsplay/lxmert).

RiniPL/Dementia_Dataset

--- license: ecl-2.0 task_categories: - image-classification language: - en tags: - code pretty_name: Dementia ---

rbeauchamp/augmented_images_perplexity

--- dataset_info: features: - name: prompt dtype: string - name: image_path dtype: string - name: image dtype: image splits: - name: train num_bytes: 1000689543.812 num_examples: 1052 download_size: 1001267552 dataset_size: 1000689543.812 --- # Dataset Card for "augmented_images_perplexity" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bezzam/DigiCam-Mirflickr-MultiMask-10K

--- license: mit dataset_info: features: - name: lensless dtype: image - name: lensed dtype: image - name: mask_label dtype: int64 splits: - name: train num_bytes: 4048333123.5 num_examples: 8500 - name: test num_bytes: 716310780.5 num_examples: 1500 download_size: 4765086529 dataset_size: 4764643904.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* ---

Denissilva88/NERI

--- license: openrail ---

serbog/esco_occupations_details_multilingual

--- configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: el struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: lt struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: code dtype: string - name: uk struct: - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: ga struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: sv struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: cs struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: bg struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: 'no' struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: en struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: lv struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: ar struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: es struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: et struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: fi struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: sk struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: da struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: nl struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: is struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: sl struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: hr struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: pl struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: it struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: de struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: url dtype: string - name: mt struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: hu struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: fr struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: pt struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string - name: ro struct: - name: alternativeLabel sequence: string - name: description dtype: string - name: preferredLabel dtype: string - name: preferredTerm dtype: string splits: - name: train num_bytes: 52470213 num_examples: 3629 download_size: 22696020 dataset_size: 52470213 --- # Dataset Card for "esco_occupations_details_multilingual" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

freshpearYoon/train_free_13

--- dataset_info: features: - name: input_features sequence: sequence: float32 - name: labels sequence: int64 splits: - name: train num_bytes: 9604588872 num_examples: 10000 download_size: 1312729438 dataset_size: 9604588872 configs: - config_name: default data_files: - split: train path: data/train-* ---

andersonbcdefg/micropile

--- dataset_info: features: - name: text dtype: string - name: __id dtype: int64 splits: - name: train num_bytes: 5544284 num_examples: 1000 download_size: 2933209 dataset_size: 5544284 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "micropile" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dmrau/cqadubstack-gaming-qrels

--- configs: - config_name: default data_files: - split: test path: data/test-* dataset_info: features: - name: query-id dtype: string - name: corpus-id dtype: string - name: score dtype: int64 splits: - name: test num_bytes: 60520 num_examples: 2263 download_size: 32524 dataset_size: 60520 --- # Dataset Card for "cqadubstack-gaming-qrels" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MouhsineGT/new_fr_200_excel

--- license: unknown ---