ftopal/huggingface-datasets-processed · Datasets at Hugging Face

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7c7002a5c4fb939587f214826e00504b1ec89355	# Dataset of otokura_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls) This is the dataset of otokura_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, grey_hair, bangs, black_eyes, grey_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 \| 500 \| 637.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 367.17 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1215 \| 783.54 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 574.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1215 \| 1.09 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_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/otokura_yuuki_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, blush, looking_at_viewer, open_mouth, sailor_collar, sailor_dress, simple_background, solo, white_background, white_dress, wrist_cuffs, blue_bow, frills, :d, sleeveless_dress, bowtie, hair_ornament, collarbone, hair_bow, skirt_hold \| \| 1 \| 13 \| ![](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, blazer, school_uniform, smile, school_bag, skirt, solo, looking_at_viewer, blush, neck_ribbon, open_mouth, blue_ribbon, shirt \| \| 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) \| blazer, blue_ribbon, blush, long_sleeves, neck_ribbon, school_uniform, white_background, white_shirt, 1girl, looking_at_viewer, simple_background, solo, upper_body, collared_shirt, :d, black_jacket, blue_jacket, closed_mouth, hand_up, open_jacket, open_mouth, vest \| \| 3 \| 10 \| ![](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, open_mouth, looking_at_viewer, midriff, solo, blush, navel, black_hair, shorts, :d, belt, suspenders, white_gloves, fingerless_gloves, mini_hat, simple_background, sleeveless, white_background \| \| 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, blush, floral_print, looking_at_viewer, solo, obi, hair_flower, wide_sleeves, print_kimono, long_sleeves, open_mouth, upper_body, white_kimono, yukata, :d, closed_mouth, fur_collar, head_tilt, holding, pink_kimono \| \| 5 \| 15 \| ![](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, frilled_bikini, navel, solo, blush, looking_at_viewer, open_mouth, bikini_skirt, collarbone, day, pink_bikini, outdoors, sun_hat, :d, blue_sky, halterneck, straw_hat, cloud, ocean, plaid, hair_between_eyes, bare_shoulders, cowboy_shot, hat_bow, standing, water \| \| 6 \| 17 \| ![](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, solo, blush, looking_at_viewer, navel, open_mouth, collarbone, :d, midriff, sweat, short_shorts, simple_background, small_breasts, sports_bra, white_background, open_jacket, shoes \| \| 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, blue_sky, cloud, denim_shorts, earrings, looking_at_viewer, necklace, open_mouth, short_shorts, short_sleeves, solo, tied_shirt, hairband, midriff, navel, orange_shirt, outdoors, :d, belt, blue_shorts, blush, breasts, collarbone, cutoffs, day, flower, bracelet, clothes_writing, cowboy_shot, salute, sunlight, thighs, watch \| \| 8 \| 5 \| ![](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, blush, striped_shirt, collarbone, looking_at_viewer, open_mouth, solo, hooded_jacket, hoodie, pink_jacket, shorts, simple_background, smile, white_background, key_necklace \| \| 9 \| 5 \| ![](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, pink_shirt, smile, solo, looking_at_viewer, outdoors, short_sleeves, sitting, open_mouth, blurry_background, collarbone, short_shorts, sweat, water_bottle \| \| 10 \| 12 \| ![](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) \| 1girl, looking_at_viewer, solo, small_breasts, blush, armpits, covered_navel, cowboy_shot, school_swimsuit, smile, arms_up, collarbone, standing, blue_one-piece_swimsuit, white_background \| \| 11 \| 11 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, blush, solo, looking_at_viewer, pink_bow, fur-trimmed_gloves, hair_bow, striped_bow, beret, black_headwear, brown_gloves, heart_earrings, sleeveless, brown_headwear, polka_dot, skirt, :d, breasts, jingle_bell, navel, open_mouth, plaid, rose, see-through, upper_body, white_background, bare_shoulders, closed_mouth, hair_between_eyes, pink_dress, sitting \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| looking_at_viewer \| open_mouth \| sailor_collar \| sailor_dress \| simple_background \| solo \| white_background \| white_dress \| wrist_cuffs \| blue_bow \| frills \| :d \| sleeveless_dress \| bowtie \| hair_ornament \| collarbone \| hair_bow \| skirt_hold \| blazer \| school_uniform \| smile \| school_bag \| skirt \| neck_ribbon \| blue_ribbon \| shirt \| long_sleeves \| white_shirt \| upper_body \| collared_shirt \| black_jacket \| blue_jacket \| closed_mouth \| hand_up \| open_jacket \| vest \| midriff \| navel \| black_hair \| shorts \| belt \| suspenders \| white_gloves \| fingerless_gloves \| mini_hat \| sleeveless \| floral_print \| obi \| hair_flower \| wide_sleeves \| print_kimono \| white_kimono \| yukata \| fur_collar \| head_tilt \| holding \| pink_kimono \| frilled_bikini \| bikini_skirt \| day \| pink_bikini \| outdoors \| sun_hat \| blue_sky \| halterneck \| straw_hat \| cloud \| ocean \| plaid \| hair_between_eyes \| bare_shoulders \| cowboy_shot \| hat_bow \| standing \| water \| sweat \| short_shorts \| small_breasts \| sports_bra \| shoes \| denim_shorts \| earrings \| necklace \| short_sleeves \| tied_shirt \| hairband \| orange_shirt \| blue_shorts \| breasts \| cutoffs \| flower \| bracelet \| clothes_writing \| salute \| sunlight \| thighs \| watch \| striped_shirt \| hooded_jacket \| hoodie \| pink_jacket \| key_necklace \| pink_shirt \| sitting \| blurry_background \| water_bottle \| armpits \| covered_navel \| school_swimsuit \| arms_up \| blue_one-piece_swimsuit \| pink_bow \| fur-trimmed_gloves \| striped_bow \| beret \| black_headwear \| brown_gloves \| heart_earrings \| brown_headwear \| polka_dot \| jingle_bell \| rose \| see-through \| pink_dress \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:--------\|:--------------------\|:-------------\|:----------------\|:---------------\|:--------------------\|:-------\|:-------------------\|:--------------\|:--------------\|:-----------\|:---------\|:-----\|:-------------------\|:---------\|:----------------\|:-------------\|:-----------\|:-------------\|:---------\|:-----------------\|:--------\|:-------------\|:--------\|:--------------\|:--------------\|:--------\|:---------------\|:--------------\|:-------------\|:-----------------\|:---------------\|:--------------\|:---------------\|:----------\|:--------------\|:-------\|:----------\|:--------\|:-------------\|:---------\|:-------\|:-------------\|:---------------\|:--------------------\|:-----------\|:-------------\|:---------------\|:------\|:--------------\|:---------------\|:---------------\|:---------------\|:---------\|:-------------\|:------------\|:----------\|:--------------\|:-----------------\|:---------------\|:------\|:--------------\|:-----------\|:----------\|:-----------\|:-------------\|:------------\|:--------\|:--------\|:--------\|:--------------------\|:-----------------\|:--------------\|:----------\|:-----------\|:--------\|:--------\|:---------------\|:----------------\|:-------------\|:--------\|:---------------\|:-----------\|:-----------\|:----------------\|:-------------\|:-----------\|:---------------\|:--------------\|:----------\|:----------\|:---------\|:-----------\|:------------------\|:---------\|:-----------\|:---------\|:--------\|:----------------\|:----------------\|:---------\|:--------------\|:---------------\|:-------------\|:----------\|:--------------------\|:---------------\|:----------\|:----------------\|:------------------\|:----------\|:--------------------------\|:-----------\|:---------------------\|:--------------\|:--------\|:-----------------\|:---------------\|:-----------------\|:-----------------\|:------------\|:--------------\|:-------\|:--------------\|:-------------\| \| 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 \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 13 \| ![](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 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 10 \| ![](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 \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 15 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 5 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 5 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 11 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-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 \|	CyberHarem/otokura_yuuki_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-13T21:50:42+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:11:58+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of otokura\_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls) ========================================================================= This is the dataset of otokura\_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'short\_hair, grey\_hair, bangs, black\_eyes, grey\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
3706a3a7af88cff4c68bee8baa78ed62c67daf9f	# Dataset of oikawa_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls) This is the dataset of oikawa_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, brown_hair, breasts, brown_eyes, large_breasts, huge_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 \| 494.58 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 334.93 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1161 \| 670.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 460.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1161 \| 859.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_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/oikawa_shizuku_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, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, headset, neck_bell, open_mouth, pink_thighhighs, solo, blush, cleavage, cow_tail, pink_gloves, skirt, smile, navel \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, headset, neck_bell, nipples, open_mouth, pink_thighhighs, solo, skirt, cow_tail, lactation, smile \| \| 2 \| 15 \| ![](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, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, neck_bell, solo, headset, cow_tail, blush, open_mouth, smile, cleavage, navel, looking_at_viewer, simple_background, skirt, white_background \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, neck_bell, headset, open_mouth, solo, lactation, smile, nipples \| \| 4 \| 5 \| ![](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) \| 1boy, 1girl, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, cow_tail, cowbell, elbow_gloves, hetero, neck_bell, paizuri, penis, solo_focus, breasts_squeezed_together, headset, mosaic_censoring, nipples, open_mouth, ejaculation, :d, cum_on_breasts, pink_gloves, tongue \| \| 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, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, elbow_gloves, hetero, multiple_penises, neck_bell, nipples, solo_focus, cowbell, double_handjob, gloved_handjob, lactation, open_mouth, bar_censor, cum_on_breasts, facial, gangbang, headset, paizuri, pink_gloves, 2boys, 3boys, 4boys, 5boys, bukkake, cum_on_hair, cum_on_tongue, ejaculation, fellatio, gigantic_breasts, mmf_threesome, pointless_censoring, veins \| \| 6 \| 11 \| ![](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) \| looking_at_viewer, 1girl, blush, cleavage, collarbone, navel, open_mouth, solo, bangs, simple_background, white_background, bare_shoulders, :d, side-tie_bikini_bottom, striped_bikini, white_bikini \| \| 7 \| 7 \| ![](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, looking_at_viewer, simple_background, solo, white_background, blush, open_mouth, :d \| \| 8 \| 13 \| ![](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) \| 1boy, 1girl, hetero, solo_focus, blush, nipples, paizuri, cum_on_breasts, penis, smile, open_mouth, breasts_squeezed_together, censored, ejaculation, gigantic_breasts \| \| 9 \| 9 \| ![](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, bare_shoulders, elbow_gloves, open_mouth, solo, black_gloves, blush, looking_at_viewer, bangs, hairband, smile, belt, bodysuit, simple_background, zipper \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| collar \| cow_ears \| cow_girl \| cow_horns \| cow_print \| cowbell \| elbow_gloves \| headset \| neck_bell \| open_mouth \| pink_thighhighs \| solo \| blush \| cleavage \| cow_tail \| pink_gloves \| skirt \| smile \| navel \| nipples \| lactation \| looking_at_viewer \| simple_background \| white_background \| 1boy \| hetero \| paizuri \| penis \| solo_focus \| breasts_squeezed_together \| mosaic_censoring \| ejaculation \| :d \| cum_on_breasts \| tongue \| multiple_penises \| double_handjob \| gloved_handjob \| bar_censor \| facial \| gangbang \| 2boys \| 3boys \| 4boys \| 5boys \| bukkake \| cum_on_hair \| cum_on_tongue \| fellatio \| gigantic_breasts \| mmf_threesome \| pointless_censoring \| veins \| collarbone \| bangs \| bare_shoulders \| side-tie_bikini_bottom \| striped_bikini \| white_bikini \| censored \| black_gloves \| hairband \| belt \| bodysuit \| zipper \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------\|:-----------\|:-----------\|:------------\|:------------\|:----------\|:---------------\|:----------\|:------------\|:-------------\|:------------------\|:-------\|:--------\|:-----------\|:-----------\|:--------------\|:--------\|:--------\|:--------\|:----------\|:------------\|:--------------------\|:--------------------\|:-------------------\|:-------\|:---------\|:----------\|:--------\|:-------------\|:----------------------------\|:-------------------\|:--------------\|:-----\|:-----------------\|:---------\|:-------------------\|:-----------------\|:-----------------\|:-------------\|:---------\|:-----------\|:--------\|:--------\|:--------\|:--------\|:----------\|:--------------\|:----------------\|:-----------\|:-------------------\|:----------------\|:----------------------\|:--------\|:-------------\|:--------\|:-----------------\|:-------------------------\|:-----------------\|:---------------\|:-----------\|:---------------\|:-----------\|:-------\|:-----------\|:---------\| \| 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 \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 15 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| \| X \| X \| X \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| X \| \| \| X \| \| \| \| X \| X \| \| \| \| \| X \| X \| \| X \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 11 \| ![](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 \| \| \| \| \| \| \| \| 7 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 13 \| ![](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 \| \| \| \| \| \| \| 9 \| 9 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| \| \| \| \| X \| \| \| X \| \| X \| X \| \| \| \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| X \| X \| X \| X \| X \|	CyberHarem/oikawa_shizuku_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-13T22:27:46+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T16:10:50+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of oikawa\_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls) ========================================================================= This is the dataset of oikawa\_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'short\_hair, brown\_hair, breasts, brown\_eyes, large\_breasts, huge\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
7520dffa87bd6f3d41f5b5ed1f74f63743c8eda3	# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble ## Dataset Description - Homepage: - Repository: https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-13B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble) 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_oh-yeontaek__llama-2-13B-LoRA-assemble", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T12:38:31.031518](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble/blob/main/results_2023-10-28T12-38-31.031518.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": 0.018246644295302015, "em_stderr": 0.0013706682452812897, "f1": 0.12087667785234917, "f1_stderr": 0.002262552570535497, "acc": 0.4228981679335413, "acc_stderr": 0.009810986357152753 }, "harness\|drop\|3": { "em": 0.018246644295302015, "em_stderr": 0.0013706682452812897, "f1": 0.12087667785234917, "f1_stderr": 0.002262552570535497 }, "harness\|gsm8k\|5": { "acc": 0.0841546626231994, "acc_stderr": 0.0076470240466032045 }, "harness\|winogrande\|5": { "acc": 0.7616416732438832, "acc_stderr": 0.011974948667702302 } } ``` ### 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]	open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble	[ "region:us" ]	2023-09-13T22:30:23+00:00	{"pretty_name": "Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble", "dataset_summary": "Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-13B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T12:38:31.031518](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble/blob/main/results_2023-10-28T12-38-31.031518.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\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812897,\n \"f1\": 0.12087667785234917,\n \"f1_stderr\": 0.002262552570535497,\n \"acc\": 0.4228981679335413,\n \"acc_stderr\": 0.009810986357152753\n },\n \"harness\|drop\|3\": {\n \"em\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812897,\n \"f1\": 0.12087667785234917,\n \"f1_stderr\": 0.002262552570535497\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0841546626231994,\n \"acc_stderr\": 0.0076470240466032045\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702302\n }\n}\n```", "repo_url": "https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|arc:challenge\|25_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T12_38_31.031518", "path": ["/details_harness\|drop\|3_2023-10-28T12-38-31.031518.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-28T12-38-31.031518.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T12_38_31.031518", "path": ["/details_harness\|gsm8k\|5_2023-10-28T12-38-31.031518.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-28T12-38-31.031518.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hellaswag\|10_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-international_law\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-management\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-marketing\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-sociology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-virology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-elementary_mathematics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-formal_logic\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_computer_science\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_european_history\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_geography\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_government_and_politics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_macroeconomics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_mathematics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-human_aging\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-international_law\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-machine_learning\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-management\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-marketing\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-nutrition\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-philosophy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-prehistory\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_law\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-public_relations\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-security_studies\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-sociology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-virology\|5_2023-09-13T23-30-08.066135.parquet", "/details_harness\|hendrycksTest-world_religions\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_abstract_algebra_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-anatomy\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-astronomy\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-business_ethics\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_biology\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_computer_science_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_mathematics_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_medicine_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_medicine\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_physics_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_physics\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_computer_security_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-computer_security\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_conceptual_physics_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-conceptual_physics\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_econometrics_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-econometrics\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_electrical_engineering_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-electrical_engineering\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": 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{"split": "latest", "path": ["/details_harness\|hendrycksTest-global_facts\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_biology_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_biology\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_chemistry_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": ["/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T23-30-08.066135.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-high_school_chemistry\|5_2023-09-13T23-30-08.066135.parquet"]}]}, {"config_name": "harness_hendrycksTest_high_school_computer_science_5", "data_files": [{"split": "2023_09_13T23_30_08.066135", "path": 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clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-28T12:38:31.031518(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T12:38:31.031518(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T12:38:31.031518(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 25, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T12:38:31.031518(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
c6d757d4e47e2bfd6f0f195a4ab1287134818204	An Ongoing SVG Collection of Many Multiples of Brand Logos Object looks like this: Shape: ``` ├── brandName "" ├── brandWebsite "" ├── brandPresence[{ │ └── platform │ └── url │ └── username}] ├── brandLogo[{ │ └── fileName │ └── svgPath │ └── svgData │ ├── meta │ ├── width │ ├── height │ ├── viewbox │ └── fill │ └── svgRaw}] └── brandColors[{ └── meta ├── primary ├── secondary ├── tertiary ├── quaternary ├── priority └── setting ├── colorName ├── colorHex ├── colorRGB ├── colorCMYK └── colorPantone}] ```	mattrichmo/brand-logos	[ "size_categories:1K<n<10K", "region:us" ]	2023-09-13T22:36:51+00:00	{"size_categories": ["1K<n<10K"]}	2023-09-14T19:21:42+00:00	[]	[]	TAGS #size_categories-1K<n<10K #region-us	An Ongoing SVG Collection of Many Multiples of Brand Logos Object looks like this: Shape:	[]	[ "TAGS\n#size_categories-1K<n<10K #region-us \n" ]	[ 18 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #region-us \n" ]
dd2311b1ca951d687d181437372f501333f4481a	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	jsonfin17/financial_conversation_summary	[ "region:us" ]	2023-09-13T23:05:49+00:00	{}	2023-09-13T23:07:35+00:00	[]	[]	TAGS #region-us	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
9aaf53bb4c22bc9d29d990d85da9a035aceb98ef	# Dataset of mifune_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls) This is the dataset of mifune_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls), containing 487 images and their tags. The core tags of this character are `brown_hair, long_hair, breasts, brown_eyes, ponytail, bangs, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:---------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 487 \| 522.91 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 487 \| 342.76 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1117 \| 684.74 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 487 \| 476.56 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1117 \| 899.28 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_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/mifune_miyu_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, blush, cleavage, looking_at_viewer, smile, solo, bikini, navel, collarbone, jewelry \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, necklace, smile, solo, blush, simple_background, white_background, bare_shoulders, collarbone, bracelet, sleeveless_dress \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, blush, simple_background, sleeveless_dress, solo, white_background, white_dress, cleavage, collarbone, sidelocks, sun_hat, white_choker, white_headwear, white_panties, closed_mouth, dress_lift, looking_at_viewer, side-tie_panties, ass_visible_through_thighs, cowboy_shot, hair_ribbon, lifted_by_self, sundress \| \| 3 \| 16 \| ![](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, blush, cleavage, looking_at_viewer, navel, black_bikini, white_shirt, collarbone, dress_shirt, open_shirt, cowboy_shot, side-tie_bikini_bottom, smile, long_sleeves, open_mouth, bikini_under_clothes, day, front-tie_bikini_top, straw_hat, sun_hat, medium_breasts, outdoors, sidelocks, simple_background, sky, standing \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, hair_flower, solo, bare_shoulders, blush, open_mouth, smile, blue_dress, bracelet, choker, looking_at_viewer, blue_flower, cleavage, collarbone, detached_sleeves, medium_breasts, petals \| \| 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_skirt, cowboy_shot, looking_at_viewer, office_lady, pencil_skirt, sidelocks, solo, black_jacket, blush, collarbone, long_sleeves, miniskirt, simple_background, standing, white_background, white_shirt, cleavage, dress_shirt, medium_breasts, red-framed_eyewear, semi-rimless_eyewear, skirt_suit, swept_bangs, bespectacled, collared_shirt, open_mouth, parted_lips, smile \| \| 6 \| 11 \| ![](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) \| looking_at_viewer, obi, 1girl, blush, smile, solo, floral_print, hair_flower, sidelocks, yukata, cleavage, wide_sleeves \| \| 7 \| 20 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| rabbit_ears, 1girl, detached_collar, playboy_bunny, fake_animal_ears, bowtie, solo, blush, wrist_cuffs, cleavage, looking_at_viewer, simple_background, strapless_leotard, white_background, bare_shoulders, medium_breasts, black_leotard, black_pantyhose, fishnet_pantyhose, rabbit_tail, sidelocks, smile \| \| 8 \| 16 \| ![](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) \| blush, tiger_print, 1girl, fingerless_gloves, solo, fake_animal_ears, tiger_ears, bare_shoulders, cleavage, looking_at_viewer, midriff, tiger_tail, navel, fur_trim, headset, belt, skirt, open_mouth, tube_top, black_choker, collarbone, crop_top, fishnet_pantyhose, side_ponytail, simple_background \| \| 9 \| 5 \| ![](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, gym_uniform, kneehighs, looking_at_viewer, open_mouth, short_sleeves, solo, white_shirt, white_socks, red_buruma, simple_background, white_background, buruma_pull, collarbone, lifted_by_self, medium_breasts, sidelocks, sitting, thighs, uwabaki, white_footwear, full_body, gym_shirt, navel, partially_visible_vulva, shirt_lift, spread_legs, wedgie, white_bra \| \| 10 \| 5 \| ![](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) \| 1boy, 1girl, blush, hetero, nipples, navel, open_mouth, solo_focus, breast_grab, collarbone, grabbing, pussy, sex, sweat, vaginal, completely_nude, looking_at_viewer, mosaic_censoring, on_back, penis, pubic_hair \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, blush, long_sleeves, pleated_skirt, black_pantyhose, solo, black_serafuku, black_shirt, black_skirt, collarbone, looking_at_viewer, sitting, white_neckerchief, bag, black_sailor_collar, blue_serafuku, blue_shirt, blue_skirt, smile \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| cleavage \| looking_at_viewer \| smile \| solo \| bikini \| navel \| collarbone \| jewelry \| necklace \| simple_background \| white_background \| bare_shoulders \| bracelet \| sleeveless_dress \| white_dress \| sidelocks \| sun_hat \| white_choker \| white_headwear \| white_panties \| closed_mouth \| dress_lift \| side-tie_panties \| ass_visible_through_thighs \| cowboy_shot \| hair_ribbon \| lifted_by_self \| sundress \| black_bikini \| white_shirt \| dress_shirt \| open_shirt \| side-tie_bikini_bottom \| long_sleeves \| open_mouth \| bikini_under_clothes \| day \| front-tie_bikini_top \| straw_hat \| medium_breasts \| outdoors \| sky \| standing \| hair_flower \| blue_dress \| choker \| blue_flower \| detached_sleeves \| petals \| black_skirt \| office_lady \| pencil_skirt \| black_jacket \| miniskirt \| red-framed_eyewear \| semi-rimless_eyewear \| skirt_suit \| swept_bangs \| bespectacled \| collared_shirt \| parted_lips \| obi \| floral_print \| yukata \| wide_sleeves \| rabbit_ears \| detached_collar \| playboy_bunny \| fake_animal_ears \| bowtie \| wrist_cuffs \| strapless_leotard \| black_leotard \| black_pantyhose \| fishnet_pantyhose \| rabbit_tail \| tiger_print \| fingerless_gloves \| tiger_ears \| midriff \| tiger_tail \| fur_trim \| headset \| belt \| skirt \| tube_top \| black_choker \| crop_top \| side_ponytail \| gym_uniform \| kneehighs \| short_sleeves \| white_socks \| red_buruma \| buruma_pull \| sitting \| thighs \| uwabaki \| white_footwear \| full_body \| gym_shirt \| partially_visible_vulva \| shirt_lift \| spread_legs \| wedgie \| white_bra \| 1boy \| hetero \| nipples \| solo_focus \| breast_grab \| grabbing \| pussy \| sex \| sweat \| vaginal \| completely_nude \| mosaic_censoring \| on_back \| penis \| pubic_hair \| pleated_skirt \| black_serafuku \| black_shirt \| white_neckerchief \| bag \| black_sailor_collar \| blue_serafuku \| blue_shirt \| blue_skirt \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:--------\|:-----------\|:--------------------\|:--------\|:-------\|:---------\|:--------\|:-------------\|:----------\|:-----------\|:--------------------\|:-------------------\|:-----------------\|:-----------\|:-------------------\|:--------------\|:------------\|:----------\|:---------------\|:-----------------\|:----------------\|:---------------\|:-------------\|:-------------------\|:-----------------------------\|:--------------\|:--------------\|:-----------------\|:-----------\|:---------------\|:--------------\|:--------------\|:-------------\|:-------------------------\|:---------------\|:-------------\|:-----------------------\|:------\|:-----------------------\|:------------\|:-----------------\|:-----------\|:------\|:-----------\|:--------------\|:-------------\|:---------\|:--------------\|:-------------------\|:---------\|:--------------\|:--------------\|:---------------\|:---------------\|:------------\|:---------------------\|:-----------------------\|:-------------\|:--------------\|:---------------\|:-----------------\|:--------------\|:------\|:---------------\|:---------\|:---------------\|:--------------\|:------------------\|:----------------\|:-------------------\|:---------\|:--------------\|:--------------------\|:----------------\|:------------------\|:--------------------\|:--------------\|:--------------\|:--------------------\|:-------------\|:----------\|:-------------\|:-----------\|:----------\|:-------\|:--------\|:-----------\|:---------------\|:-----------\|:----------------\|:--------------\|:------------\|:----------------\|:--------------\|:-------------\|:--------------\|:----------\|:---------\|:----------\|:-----------------\|:------------\|:------------\|:--------------------------\|:-------------\|:--------------\|:---------\|:------------\|:-------\|:---------\|:----------\|:-------------\|:--------------\|:-----------\|:--------\|:------\|:--------\|:----------\|:------------------\|:-------------------\|:----------\|:--------\|:-------------\|:----------------\|:-----------------\|:--------------\|:--------------------\|:------\|:----------------------\|:----------------\|:-------------\|:-------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 6 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| X \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| X \| \| X \| \| \| X \| \| \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 16 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| X \| \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| \| \| X \| X \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 20 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| X \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 16 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| X \| X \| X \| \| X \| \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| \| X \| \| X \| \| X \| X \| \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 5 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/mifune_miyu_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-13T23:14:53+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T16:21:22+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of mifune\_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls) =============================================================== This is the dataset of mifune\_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls), containing 487 images and their tags. The core tags of this character are 'brown\_hair, long\_hair, breasts, brown\_eyes, ponytail, bangs, large\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
f26b5261b15ffd608903645e93a0e203577c86ef	# Dataset Card for "deleted-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	HydraLM/filter-delete-1	[ "region:us" ]	2023-09-13T23:15:21+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1403707526, "num_examples": 230443}], "download_size": 1340424028, "dataset_size": 1403707526}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-13T23:20:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for "deleted-2" More Information needed	[ "# Dataset Card for \"deleted-2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"deleted-2\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"deleted-2\"\n\nMore Information needed" ]
6da6baa4078f3d2da4efa3cbb74b561d13d09921	# Dataset Card for "truth-tagged-oasst-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/truth-tagged-oasst-alpaca	[ "region:us" ]	2023-09-13T23:19:53+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "s_idx", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "validation", "num_bytes": 232102, "num_examples": 197}], "download_size": 61886, "dataset_size": 232102}}	2023-09-13T23:52:39+00:00	[]	[]	TAGS #region-us	# Dataset Card for "truth-tagged-oasst-alpaca" More Information needed	[ "# Dataset Card for \"truth-tagged-oasst-alpaca\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"truth-tagged-oasst-alpaca\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"truth-tagged-oasst-alpaca\"\n\nMore Information needed" ]
3ba59498674cd2f1ed0fcac42c7efdff4a392745	# Dataset of hino_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls) This is the dataset of hino_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls), containing 424 images and their tags. The core tags of this character are `long_hair, brown_hair, ponytail, green_eyes, breasts, high_ponytail, bow, hair_bow, medium_breasts, aqua_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 \| 424 \| 350.37 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 424 \| 271.02 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 916 \| 530.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 424 \| 336.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 916 \| 642.50 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_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/hino_akane_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 \| 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, looking_at_viewer, open_mouth, solo, :d, navel, side-tie_bikini_bottom, striped_bikini, blush, cleavage, day, front-tie_top, large_breasts \| \| 1 \| 29 \| ![](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, cleavage, open_mouth, smile, solo, looking_at_viewer, navel, midriff, skirt, blush, star_(symbol), wristband, orange_hair \| \| 2 \| 8 \| ![](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, cleavage, navel, open_mouth, smile, solo, striped, blue_shorts, bracelet, midriff, large_breasts, armpits \| \| 3 \| 49 \| ![](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, plaid_skirt, smile, solo, school_uniform, pleated_skirt, open_mouth, blush, looking_at_viewer, scrunchie, bracelet \| \| 4 \| 7 \| ![](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, midriff, navel, skirt, solo, cleavage, highleg_panties, jacket, looking_at_viewer, smile, race_queen, :3, blush, jewelry, umbrella \| \| 5 \| 6 \| ![](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) \| elbow_gloves, wedding_dress, 1girl, bare_shoulders, hair_flower, open_mouth, smile, solo, blush, cleavage, veil, sunflower, white_gloves \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| open_mouth \| solo \| :d \| navel \| side-tie_bikini_bottom \| striped_bikini \| blush \| cleavage \| day \| front-tie_top \| large_breasts \| smile \| midriff \| skirt \| star_(symbol) \| wristband \| orange_hair \| striped \| blue_shorts \| bracelet \| armpits \| plaid_skirt \| school_uniform \| pleated_skirt \| scrunchie \| highleg_panties \| jacket \| race_queen \| :3 \| jewelry \| umbrella \| elbow_gloves \| wedding_dress \| bare_shoulders \| hair_flower \| veil \| sunflower \| white_gloves \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------------\|:-------\|:-----\|:--------\|:-------------------------\|:-----------------\|:--------\|:-----------\|:------\|:----------------\|:----------------\|:--------\|:----------\|:--------\|:----------------\|:------------\|:--------------\|:----------\|:--------------\|:-----------\|:----------\|:--------------\|:-----------------\|:----------------\|:------------\|:------------------\|:---------\|:-------------\|:-----\|:----------\|:-----------\|:---------------\|:----------------\|:-----------------\|:--------------\|:-------\|:------------\|:---------------\| \| 0 \| 5 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 29 \| ![](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 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| X \| \| X \| \| \| \| X \| \| \| X \| X \| X \| \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 49 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](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 \|	CyberHarem/hino_akane_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T00:01:10+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T16:25:43+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of hino\_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls) =================================================================== This is the dataset of hino\_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls), containing 424 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, ponytail, green\_eyes, breasts, high\_ponytail, bow, hair\_bow, medium\_breasts, aqua\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
1c77675649aca64435d44b664b648c16eefef79f	# Dataset of ichihara_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls) This is the dataset of ichihara_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are `long_hair, brown_hair, brown_eyes, bangs, blunt_bangs, bow`, 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 \| 489 \| 430.19 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 489 \| 301.39 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1004 \| 591.87 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 489 \| 399.17 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1004 \| 750.73 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_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/ichihara_nina_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 \| 8 \| ![](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, :d, blush, kigurumi, open_mouth, solo, rabbit_costume, sitting \| \| 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, animal_costume, bell, kigurumi, sheep_ears, solo, open_mouth, horns, striped_pantyhose, :d \| \| 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, animal_costume, blush, kigurumi, open_mouth, pantyhose, polka_dot_legwear, solo, :d, tail, animal_ears, animal_hat, looking_at_viewer, paw_gloves \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| :d, blush, open_mouth, white_background, white_shirt, 1girl, arm_up, black_shorts, looking_at_viewer, short_sleeves, simple_background, solo, striped, hair_bow, short_shorts, animal_hood, black_bow, blue_bow, collared_shirt, very_long_hair \| \| 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, hood_up, rabbit_hood, solo, blush, hooded_jacket, long_sleeves, white_background, looking_at_viewer, pink_skirt, simple_background, :d, open_mouth, full_body, pom_pom_(clothes), rabbit_ears, shoes, blue_shirt, brown_jacket, fake_animal_ears, open_jacket, pink_footwear, striped_pantyhose, striped_thighhighs \| \| 5 \| 19 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, solo, blush, looking_at_viewer, open_mouth, two_side_up, demon_tail, :d, demon_wings, frilled_dress, detached_sleeves, striped, fangs, bat_hair_ornament, demon_horns, long_sleeves, simple_background, asymmetrical_legwear, black_gloves, white_background, fake_horns, single_thighhigh, frilled_hairband, full_body, orange_hair, puffy_sleeves, single_sock \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| :d \| blush \| kigurumi \| open_mouth \| solo \| rabbit_costume \| sitting \| animal_costume \| bell \| sheep_ears \| horns \| striped_pantyhose \| pantyhose \| polka_dot_legwear \| tail \| animal_ears \| animal_hat \| looking_at_viewer \| paw_gloves \| white_background \| white_shirt \| arm_up \| black_shorts \| short_sleeves \| simple_background \| striped \| hair_bow \| short_shorts \| animal_hood \| black_bow \| blue_bow \| collared_shirt \| very_long_hair \| hood_up \| rabbit_hood \| hooded_jacket \| long_sleeves \| pink_skirt \| full_body \| pom_pom_(clothes) \| rabbit_ears \| shoes \| blue_shirt \| brown_jacket \| fake_animal_ears \| open_jacket \| pink_footwear \| striped_thighhighs \| two_side_up \| demon_tail \| demon_wings \| frilled_dress \| detached_sleeves \| fangs \| bat_hair_ornament \| demon_horns \| asymmetrical_legwear \| black_gloves \| fake_horns \| single_thighhigh \| frilled_hairband \| orange_hair \| puffy_sleeves \| single_sock \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----\|:--------\|:-----------\|:-------------\|:-------\|:-----------------\|:----------\|:-----------------\|:-------\|:-------------\|:--------\|:--------------------\|:------------\|:--------------------\|:-------\|:--------------\|:-------------\|:--------------------\|:-------------\|:-------------------\|:--------------\|:---------\|:---------------\|:----------------\|:--------------------\|:----------\|:-----------\|:---------------\|:--------------\|:------------\|:-----------\|:-----------------\|:-----------------\|:----------\|:--------------\|:----------------\|:---------------\|:-------------\|:------------\|:--------------------\|:--------------\|:--------\|:-------------\|:---------------\|:-------------------\|:--------------\|:----------------\|:---------------------\|:--------------\|:-------------\|:--------------\|:----------------\|:-------------------\|:--------\|:--------------------\|:--------------\|:-----------------------\|:---------------\|:-------------\|:-------------------\|:-------------------\|:--------------\|:----------------\|:--------------\| \| 0 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 19 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/ichihara_nina_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T00:36:52+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T13:31:26+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of ichihara\_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls) ================================================================= This is the dataset of ichihara\_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, brown\_eyes, bangs, blunt\_bangs, bow', 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
5863214ab1aa7be2e0294e653a7a4731ec65c4ad	# Dataset of takamori_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls) This is the dataset of takamori_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `brown_hair, brown_eyes, long_hair, single_hair_bun, hair_ornament, hair_bun, sidelocks`, 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 \| 572.12 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 381.35 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1126 \| 785.33 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 525.26 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1126 \| 1016.42 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_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/takamori_aiko_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 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| blush, looking_at_viewer, 1girl, bowtie, school_uniform, white_shirt, cardigan, pleated_skirt, ponytail, solo, bangs, miniskirt, open_mouth, plaid_skirt, :d, closed_mouth, microphone, sweater, wristband \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, hair_flower, smile, blush, dress, solo, looking_at_viewer, ponytail, open_mouth \| \| 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, looking_at_viewer, solo, white_background, navel, simple_background, white_bikini, small_breasts, micro_bikini, open_mouth, smile \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, frilled_bikini, looking_at_viewer, solo, :d, collarbone, navel, open_mouth, outdoors, barefoot, beach, hair_flower, ocean, ponytail, sitting, water, wet, bangs, cloud, day, sky, small_breasts, sparkle \| \| 4 \| 5 \| ![](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, looking_at_viewer, naked_towel, solo, water, collarbone, onsen, sitting, bangs, cleavage, open_mouth, partially_submerged, small_breasts, steam, wet, :d, bare_shoulders, bathing, medium_breasts \| \| 5 \| 10 \| ![](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, navel, blush, nipples, small_breasts, solo, female_pubic_hair, looking_at_viewer, smile, open_mouth, nude, sweat, pussy, on_back, panties \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1girl, solo, looking_at_viewer, torn_pantyhose, epaulettes, naval_uniform, ponytail, peaked_cap, blush, military_hat, skirt, black_pantyhose, breasts, navel \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| blush \| looking_at_viewer \| 1girl \| bowtie \| school_uniform \| white_shirt \| cardigan \| pleated_skirt \| ponytail \| solo \| bangs \| miniskirt \| open_mouth \| plaid_skirt \| :d \| closed_mouth \| microphone \| sweater \| wristband \| hair_flower \| smile \| dress \| white_background \| navel \| simple_background \| white_bikini \| small_breasts \| micro_bikini \| frilled_bikini \| collarbone \| outdoors \| barefoot \| beach \| ocean \| sitting \| water \| wet \| cloud \| day \| sky \| sparkle \| naked_towel \| onsen \| cleavage \| partially_submerged \| steam \| bare_shoulders \| bathing \| medium_breasts \| nipples \| female_pubic_hair \| nude \| sweat \| pussy \| on_back \| panties \| torn_pantyhose \| epaulettes \| naval_uniform \| peaked_cap \| military_hat \| skirt \| black_pantyhose \| breasts \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:--------\|:---------\|:-----------------\|:--------------\|:-----------\|:----------------\|:-----------\|:-------\|:--------\|:------------\|:-------------\|:--------------\|:-----\|:---------------\|:-------------\|:----------\|:------------\|:--------------\|:--------\|:--------\|:-------------------\|:--------\|:--------------------\|:---------------\|:----------------\|:---------------\|:-----------------\|:-------------\|:-----------\|:-----------\|:--------\|:--------\|:----------\|:--------\|:------\|:--------\|:------\|:------\|:----------\|:--------------\|:--------\|:-----------\|:----------------------\|:--------\|:-----------------\|:----------\|:-----------------\|:----------\|:--------------------\|:-------\|:--------\|:--------\|:----------\|:----------\|:-----------------\|:-------------\|:----------------\|:-------------\|:---------------\|:--------\|:------------------\|:----------\| \| 0 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| \| \| \| \| X \| X \| X \| \| X \| \| X \| \| \| \| \| X \| \| \| \| X \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| 10 \| ![](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 \| \| \| \| \| \| \| \| \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/takamori_aiko_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T01:40:51+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T14:31:36+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of takamori\_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls) ================================================================= This is the dataset of takamori\_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'brown\_hair, brown\_eyes, long\_hair, single\_hair\_bun, hair\_ornament, hair\_bun, sidelocks', 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
cbcd46dd1ddcdf9903936119be71951c91915800	# Summary `hindi-headline-article-generation` is an open source dataset of instruct-style records generated from the [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset. This was created as part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) License. Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 # Dataset Overview `hindi-headline-article-generation` is a corpus of records generated by conversion of [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset into Instruct-Style format. This Dataset can be used for the following task: - Given a headline, generate an article. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. # Dataset ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('ganeshjcs/hindi-headline-article-generation') ``` ## Purpose of Collection This was created as a part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus): Converted this dataset into Instruct-style prompts and completions. ## Data Fields - `inputs` : Prompt or input to the language model. - `targets` : Completion or output of the language model. - `template_id` : Id of the template used in `inputs` and `targets`. - `template_lang`: ISO code of the language used in the `inputs` and `targets` where hin refers to Hindi. ## Templates For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. \| template_id \| inputs \| targets \| \|-------------\|--------\|---------\| \| 0 \| ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` \| ```यह एक लेख है: {{Article}}``` \| \| 1 \| ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {Title}}``` \| ```लेख: {{Article}}``` \| \| 2 \| ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {{Title}}``` \| ```{{Article}}``` \| \| 3 \| ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {{Title}}``` \| ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` \| \| 4 \| ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` \| ```{{Article}}``` \| \| 5 \| ```इसके लिए एक लेख लिखें: {{Title}}``` \| ```लेख: {{Article}}``` \| \| 6 \| ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` \| ```यह एक लेख है: {{Article}}``` \| \| 7 \| ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` \| ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` \| \| 8 \| ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` \| ```लेख: {{Article}}``` \| \| 9 \| ```इसके लिए एक लेख लिखें: {{Title}}``` \| ```{{Article}}``` \| \| 10 \| ```इसके लिए एक लेख लिखें: {{Title}}``` \| ```यह एक लेख है: {{Article}}``` \| \| 11 \| ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` \| ```लेख: {{Article}}``` \| \| 12 \| ```इसके लिए एक लेख लिखें: {{Title}}``` \| ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` \| \| 13 \| ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` \| ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` \| \| 14 \| ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {{Title}}``` \| ```यह एक लेख है: {{Article}}``` \| \| 15 \| ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` \| ```{{Article}}``` \| ## Personal or Sensitive Data This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language Hindi # Known Limitations - The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. - Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. # Contributors [Ganesh Jagadeesan](https://github.com/ElefHead)	ganeshjcs/hindi-headline-article-generation	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus", "language:hi", "license:cc-by-sa-4.0", "generation", "region:us" ]	2023-09-14T01:53:23+00:00	{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["hi"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Hindi Article Generation", "tags": ["generation"]}	2024-01-28T19:19:13+00:00	[]	[ "hi" ]	TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us	Summary ======= 'hindi-headline-article-generation' is an open source dataset of instruct-style records generated from the Hindi Text Short and Large Summarization dataset. This was created as part of Aya Open Science Initiative from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY-SA 4.0 License. Supported Tasks: * Training LLMs * Synthetic Data Generation * Data Augmentation Languages: Hindi Version: 1.0 Dataset Overview ================ 'hindi-headline-article-generation' is a corpus of records generated by conversion of Hindi Text Short and Large Summarization dataset into Instruct-Style format. This Dataset can be used for the following task: * Given a headline, generate an article. Intended Uses ============= While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. Dataset ======= Load with Datasets ------------------ To load this dataset with Datasets, you'll just need to install Datasets as 'pip install datasets --upgrade' and then use the following code: Purpose of Collection --------------------- This was created as a part of Aya Open Science Initiative from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. Sources ------- * Hindi Text Short and Large Summarization Corpus: Converted this dataset into Instruct-style prompts and completions. Data Fields ----------- * 'inputs' : Prompt or input to the language model. * 'targets' : Completion or output of the language model. * 'template\_id' : Id of the template used in 'inputs' and 'targets'. * 'template\_lang': ISO code of the language used in the 'inputs' and 'targets' where hin refers to Hindi. Templates --------- For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. template\_id: 0, inputs: , targets: template\_id: 1, inputs: , targets: template\_id: 2, inputs: , targets: template\_id: 3, inputs: , targets: template\_id: 4, inputs: , targets: template\_id: 5, inputs: , targets: template\_id: 6, inputs: , targets: template\_id: 7, inputs: , targets: template\_id: 8, inputs: , targets: template\_id: 9, inputs: , targets: template\_id: 10, inputs: , targets: template\_id: 11, inputs: , targets: template\_id: 12, inputs: , targets: template\_id: 13, inputs: , targets: template\_id: 14, inputs: , targets: template\_id: 15, inputs: , targets: Personal or Sensitive Data -------------------------- This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. Language -------- Hindi Known Limitations ================= * The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. * Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. Contributors ============ Ganesh Jagadeesan	[]	[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]	[ 126 ]	[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]
98d8e08f3eb1daf53d7cdd7bf505ea52b72695b5	# Dataset of satou_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls) This is the dataset of satou_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls), containing 429 images and their tags. The core tags of this character are `green_eyes, ahoge, long_hair, breasts, twintails, bangs, hair_ornament, blonde_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 \| 429 \| 566.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 429 \| 334.52 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1052 \| 735.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 429 \| 500.13 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1052 \| 1.00 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_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/satou_shin_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 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blush, hair_bow, looking_at_viewer, smile, solo, choker, frills, ribbon, open_mouth, cleavage, dress, medium_breasts, one_eye_closed, skirt, hairclip, heart_ahoge, heart_hair_ornament, navel, pink_bow, ;d, simple_background, white_background \| \| 1 \| 13 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, looking_at_viewer, solo, hair_ribbon, cleavage, medium_breasts, smile, bare_shoulders, pink_dress, simple_background, white_background, hair_bow, upper_body, frills, heart_ahoge, wrist_cuffs \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, one_eye_closed, smile, solo, blush, tongue_out, wrist_cuffs, ;q, heart_hands, bare_shoulders, dress, necktie, polka_dot, skirt, wings \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, blush, looking_at_viewer, red_dress, solo, bow, cleavage, hair_ribbon, sleeveless_dress, smile, wrist_cuffs, heart_hair_ornament, medium_breasts, nail_polish, side_ponytail, valentine, box, holding_gift \| \| 4 \| 5 \| ![](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, ;d, blush, cleavage, looking_at_viewer, navel, one_eye_closed, open_mouth, smile, solo, collarbone, heart, large_breasts, bare_shoulders, pink_bikini, simple_background, cowboy_shot, frilled_bikini, holding, medium_breasts, thigh_gap, thighs, water_gun, 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, beach, blue_sky, blush, cleavage, cloud, day, looking_at_viewer, medium_breasts, navel, ocean, outdoors, smile, solo, collarbone, heart_ahoge, jewelry, side-tie_bikini_bottom, yellow_bikini, :q, ;q, barefoot, beer, front-tie_top, holding, one_eye_closed, wariza \| \| 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) \| looking_at_viewer, school_uniform, short_sleeves, 1girl, blush, bowtie, cardigan_around_waist, collared_shirt, double_bun, hair_bow, one_eye_closed, pleated_skirt, smile, solo, white_shirt, blue_bow, nail_polish, open_mouth, pink_nails, ring, simple_background, ;d, bracelet, brown_hair, cellphone, dress_shirt, heart, holding_phone, kogal, plaid_skirt, sweater_around_waist, white_background \| \| 7 \| 7 \| ![](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) \| blush, hetero, large_breasts, navel, nipples, penis, solo_focus, spread_legs, 1boy, 1girl, nude, sex, vaginal, looking_at_viewer, sweat, mosaic_censoring, open_mouth, pov, smile, swept_bangs, bar_censor, cum_in_pussy, hair_bobbles, heart_ahoge, missionary, on_back, pillow, pubic_hair, pussy_juice, thighs \| \| 8 \| 9 \| ![](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, blush, hair_flower, solo, looking_at_viewer, smile, floral_print, umbrella, upper_body, white_kimono \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| hair_bow \| looking_at_viewer \| smile \| solo \| choker \| frills \| ribbon \| open_mouth \| cleavage \| dress \| medium_breasts \| one_eye_closed \| skirt \| hairclip \| heart_ahoge \| heart_hair_ornament \| navel \| pink_bow \| ;d \| simple_background \| white_background \| hair_ribbon \| bare_shoulders \| pink_dress \| upper_body \| wrist_cuffs \| tongue_out \| ;q \| heart_hands \| necktie \| polka_dot \| wings \| red_dress \| bow \| sleeveless_dress \| nail_polish \| side_ponytail \| valentine \| box \| holding_gift \| collarbone \| heart \| large_breasts \| pink_bikini \| cowboy_shot \| frilled_bikini \| holding \| thigh_gap \| thighs \| water_gun \| beach \| blue_sky \| cloud \| day \| ocean \| outdoors \| jewelry \| side-tie_bikini_bottom \| yellow_bikini \| :q \| barefoot \| beer \| front-tie_top \| wariza \| school_uniform \| short_sleeves \| bowtie \| cardigan_around_waist \| collared_shirt \| double_bun \| pleated_skirt \| white_shirt \| blue_bow \| pink_nails \| ring \| bracelet \| brown_hair \| cellphone \| dress_shirt \| holding_phone \| kogal \| plaid_skirt \| sweater_around_waist \| hetero \| nipples \| penis \| solo_focus \| spread_legs \| 1boy \| nude \| sex \| vaginal \| sweat \| mosaic_censoring \| pov \| swept_bangs \| bar_censor \| cum_in_pussy \| hair_bobbles \| missionary \| on_back \| pillow \| pubic_hair \| pussy_juice \| hair_flower \| floral_print \| umbrella \| white_kimono \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-----------\|:--------------------\|:--------\|:-------\|:---------\|:---------\|:---------\|:-------------\|:-----------\|:--------\|:-----------------\|:-----------------\|:--------\|:-----------\|:--------------\|:----------------------\|:--------\|:-----------\|:-----\|:--------------------\|:-------------------\|:--------------\|:-----------------\|:-------------\|:-------------\|:--------------\|:-------------\|:-----\|:--------------\|:----------\|:------------\|:--------\|:------------\|:------\|:-------------------\|:--------------\|:----------------\|:------------\|:------\|:---------------\|:-------------\|:--------\|:----------------\|:--------------\|:--------------\|:-----------------\|:----------\|:------------\|:---------\|:------------\|:--------\|:-----------\|:--------\|:------\|:--------\|:-----------\|:----------\|:-------------------------\|:----------------\|:-----\|:-----------\|:-------\|:----------------\|:---------\|:-----------------\|:----------------\|:---------\|:------------------------\|:-----------------\|:-------------\|:----------------\|:--------------\|:-----------\|:-------------\|:-------\|:-----------\|:-------------\|:------------\|:--------------\|:----------------\|:--------\|:--------------\|:-----------------------\|:---------\|:----------\|:--------\|:-------------\|:--------------\|:-------\|:-------\|:------\|:----------\|:--------\|:-------------------\|:------\|:--------------\|:-------------\|:---------------\|:---------------\|:-------------\|:----------\|:---------\|:-------------\|:--------------\|:--------------\|:---------------\|:-----------\|:---------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 13 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| \| X \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| X \| X \| X \| \| \| \| \| X \| \| X \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 7 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| 8 \| 9 \| ![](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 \|	CyberHarem/satou_shin_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T02:06:20+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T15:24:16+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of satou\_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls) ============================================================= This is the dataset of satou\_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls), containing 429 images and their tags. The core tags of this character are 'green\_eyes, ahoge, long\_hair, breasts, twintails, bangs, hair\_ornament, blonde\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
37990f7731a2d02b85e7024d37b62706b83f05cc	# Dataset Card for "shiba-inu-fenda-dreambooth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	zxx-silence/shiba-inu-fenda-dreambooth	[ "region:us" ]	2023-09-14T02:08:27+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2920788.0, "num_examples": 10}], "download_size": 2921904, "dataset_size": 2920788.0}}	2023-11-02T03:06:27+00:00	[]	[]	TAGS #region-us	# Dataset Card for "shiba-inu-fenda-dreambooth" More Information needed	[ "# Dataset Card for \"shiba-inu-fenda-dreambooth\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"shiba-inu-fenda-dreambooth\"\n\nMore Information needed" ]	[ 6, 21 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"shiba-inu-fenda-dreambooth\"\n\nMore Information needed" ]
2e0f5a7db4ea29537a136be102452826f096d647	# Summary `hindi-article-summarization` is an open source dataset of instruct-style records generated from the [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset. This was created as part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) License. Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 # Dataset Overview `hindi-article-summarization` is a corpus of records generated by conversion of [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset into Instruct-Style format. This Dataset can be used for the following task: - Given an article, generate its summary. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. # Dataset ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('ganeshjcs/hindi-article-summarization') ``` ## Purpose of Collection This was created as a part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - [Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus): Converted this dataset into Instruct-style prompts and completions. ## Data Fields - `inputs` : Prompt or input to the language model. - `targets` : Completion or output of the language model. - `template_id` : Id of the template used in `inputs` and `targets`. - `template_lang`: ISO code of the language used in the `inputs` and `targets` where hin refers to Hindi. ## Templates For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. \| template_id \| inputs \| targets \| \|-------------\|--------\|---------\| \| 0 \| ```इस पाठ का सारांश बनाओ: {{Article}}``` \| ```संक्षिप्त सारांश: {{Summary}}``` \| \| 1 \| ```इस पाठ का सारांश बनाओ: {Article}}``` \| ``` {{Summary}}``` \| \| 2 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```यह एक सारांश है: {{Summary}}``` \| \| 3 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` \| \| 4 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```यहाँ एक सारांश है: {{Summary}}``` \| \| 5 \| ```एक सारांश बनाओ: {{Article}}``` \| ```सारांश: {{Summary}}``` \| \| 6 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```{{Summary}}``` \| \| 7 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```सारांश: {{Summary}}``` \| \| 8 \| ```एक सारांश बनाओ: {{Article}}``` \| ```संक्षिप्त सारांश: {{Summary}}``` \| \| 9 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```यह एक सारांश है: {{Summary}}``` \| \| 10 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```संक्षिप्त सारांश: {{Summary}}``` \| \| 11 \| ```इस पाठ का सारांश बनाओ: {{Article}}``` \| ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` \| \| 12 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```यहाँ एक सारांश है: {{Summary}}``` \| \| 13 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```संक्षिप्त पाठ: {{Summary}}``` \| \| 14 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```संक्षिप्त पाठ: {{Summary}}``` \| \| 15 \| ```एक सारांश बनाओ: {{Article}}``` \| ```यहाँ एक सारांश है: {{Summary}}``` \| \| 16 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```यह एक सारांश है: {{Summary}}``` \| \| 17 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```यहाँ एक सारांश है: {{Summary}}``` \| \| 18 \| ```इस पाठ का सारांश बनाओ: {{Article}}``` \| ```यहाँ एक सारांश है: {{Summary}}``` \| \| 19 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` \| \| 20 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```सारांश: {{Summary}}``` \| \| 21 \| ```एक सारांश बनाओ: {{Article}}``` \| ```यह एक सारांश है: {{Summary}}``` \| \| 22 \| ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` \| ```संक्षिप्त पाठ: {{Summary}}``` \| \| 23 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```संक्षिप्त सारांश: {{Summary}}``` \| \| 24 \| ```इस पाठ का सारांश बनाओ: {{Article}}``` \| ```यह एक सारांश है: {{Summary}}``` \| \| 25 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` \| \| 26 \| ```एक सारांश बनाओ: {{Article}}``` \| ```{{Summary}}``` \| \| 27 \| ```इस पाठ का सारांश बनाओ: {{Article}}``` \| ```संक्षिप्त पाठ: {{Summary}}``` \| \| 28 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```{{Summary}}``` \| \| 29 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```संक्षिप्त सारांश: {{Summary}}``` \| \| 30 \| ```एक सारांश बनाओ: {{Article}}``` \| ```संक्षिप्त पाठ: {{Summary}}``` \| \| 31 \| ```इस के लिए एक सारांश बनाएं: {{Article}}``` \| ```सारांश: {{Summary}}``` \| \| 32 \| ```एक सारांश बनाओ: {{Article}}``` \| ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` \| \| 33 \| ```इस पाठ का सारांश बनाओ: {{Article}}``` \| ```सारांश: {{Summary}}``` \| \| 34 \| ```इस पाठ का सारांश बनाएं: {{Article}}``` \| ```{{Summary}}``` \| ## Personal or Sensitive Data This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language Hindi # Known Limitations - The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. - Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. # Contributors [Ganesh Jagadeesan](https://github.com/ElefHead)	ganeshjcs/hindi-article-summarization	[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus", "language:hi", "license:cc-by-sa-4.0", "generation", "region:us" ]	2023-09-14T02:15:29+00:00	{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["hi"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Hindi Article Summarization", "tags": ["generation"]}	2024-01-29T21:29:39+00:00	[]	[ "hi" ]	TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us	Summary ======= 'hindi-article-summarization' is an open source dataset of instruct-style records generated from the Hindi Text Short and Large Summarization dataset. This was created as part of Aya Open Science Initiative from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY-SA 4.0 License. Supported Tasks: * Training LLMs * Synthetic Data Generation * Data Augmentation Languages: Hindi Version: 1.0 Dataset Overview ================ 'hindi-article-summarization' is a corpus of records generated by conversion of Hindi Text Short and Large Summarization dataset into Instruct-Style format. This Dataset can be used for the following task: * Given an article, generate its summary. Intended Uses ============= While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. Dataset ======= Load with Datasets ------------------ To load this dataset with Datasets, you'll just need to install Datasets as 'pip install datasets --upgrade' and then use the following code: Purpose of Collection --------------------- This was created as a part of Aya Open Science Initiative from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. Sources ------- * Hindi Text Short and Large Summarization Corpus: Converted this dataset into Instruct-style prompts and completions. Data Fields ----------- * 'inputs' : Prompt or input to the language model. * 'targets' : Completion or output of the language model. * 'template\_id' : Id of the template used in 'inputs' and 'targets'. * 'template\_lang': ISO code of the language used in the 'inputs' and 'targets' where hin refers to Hindi. Templates --------- For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. template\_id: 0, inputs: , targets: template\_id: 1, inputs: , targets: template\_id: 2, inputs: , targets: template\_id: 3, inputs: , targets: template\_id: 4, inputs: , targets: template\_id: 5, inputs: , targets: template\_id: 6, inputs: , targets: template\_id: 7, inputs: , targets: template\_id: 8, inputs: , targets: template\_id: 9, inputs: , targets: template\_id: 10, inputs: , targets: template\_id: 11, inputs: , targets: template\_id: 12, inputs: , targets: template\_id: 13, inputs: , targets: template\_id: 14, inputs: , targets: template\_id: 15, inputs: , targets: template\_id: 16, inputs: , targets: template\_id: 17, inputs: , targets: template\_id: 18, inputs: , targets: template\_id: 19, inputs: , targets: template\_id: 20, inputs: , targets: template\_id: 21, inputs: , targets: template\_id: 22, inputs: , targets: template\_id: 23, inputs: , targets: template\_id: 24, inputs: , targets: template\_id: 25, inputs: , targets: template\_id: 26, inputs: , targets: template\_id: 27, inputs: , targets: template\_id: 28, inputs: , targets: template\_id: 29, inputs: , targets: template\_id: 30, inputs: , targets: template\_id: 31, inputs: , targets: template\_id: 32, inputs: , targets: template\_id: 33, inputs: , targets: template\_id: 34, inputs: , targets: Personal or Sensitive Data -------------------------- This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. Language -------- Hindi Known Limitations ================= * The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. * Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. Contributors ============ Ganesh Jagadeesan	[]	[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]	[ 126 ]	[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]
ead98be61d862d271b09ab423a39b73d3d7d04f4	# AutoTrain Dataset for project: big ## Dataset Description This dataset has been automatically processed by AutoTrain for project big. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_Unnamed: 0.1": 4865, "feat_Unnamed: 0": 754, "target": "\"Names\": \"Aidan\", \"Physical Appearance\": \"Tall and lanky build\", \"Location\": \"A lively street filled with buskers\", \"Romantic Relationships\": \"Happily committed to Taylor\", \"Sexuality\": \"Heterosexual\", \"Personality Traits\": \"Meticulous\", \"Secrets\": \"Holds the key to a valuable treasure\", \"Physical Appearance\": \"Tall and lanky build\", \"Skills & Talents\": \"Talent for creating memorable experiences\", \"Hobbies or Interests\": \"Playing board games\", \"Motivations\": \"Search for personal identity\", \"Flaws\": \"Excessive pride leading to underestimating others\", \"Personality Traits\": \"Innovative\", \"Familial Relationships\": \"Eleanor (grandmother)\", \"Personal Beliefs and Values\": \"Commitment to promoting equality and inclusion for all\", \"Tone & Mood\": \"Playful teasing among friends\", \"Political Views\": \"Total apathy toward politics\", \"Hobbies or Interests\": \"Playing board games\", \"Fear or Desire for Change\": \"Desire for stability and routine\", \"Motivations\": \"Quest for personal identity\", \"Personality Traits\": \"Introverted\"", "feat_Names": "Aidan", "feat_Personality Traits": "Introverted", "feat_Motivations": "Quest for personal identity", "feat_Action": null, "feat_Feelings": null, "feat_Tone & Mood": "Playful teasing among friends", "feat_Friendships and Alliances": null, "feat_Pets": null, "feat_Romantic Relationships": "Happily committed to Taylor", "feat_Familial Relationships": "Eleanor (grandmother)", "feat_Location": "A lively street filled with buskers", "feat_Action Taken": null, "feat_Personal Beliefs and Values": "Commitment to promoting equality and inclusion for all", "feat_Socioeconomic Status": null, "feat_Hobbies or Interests": "Playing board games", "feat_Physical Appearance": "Tall and lanky build", "feat_Skills & Talents": "Talent for creating memorable experiences", "feat_Fear or Desire for Change": "Desire for stability and routine", "feat_Family": null, "feat_Background and History": null, "feat_Goals and Aspirations": null, "feat_Thoughts and Inner Monologues": null, "feat_External Pressures or Influences": null, "feat_Flaws": "Excessive pride leading to underestimating others", "feat_hairstyle": null, "feat_Thoughts": null, "feat_Status": null, "feat_Past Experiences Influencing Actions": null, "feat_Phrase": null, "feat_, \"Weight": null, "feat_Rivalries or Conflicts": null, "feat_Marital Status": null, "feat_Weather": null, "feat_Reasons for Feelings": null, "feat_Cultural Practices": null, "feat_Moral or Ethical Dilemmas": null, "feat_Political Views": "Total apathy toward politics", "feat_Mental or Emotional States": null, "feat_Age": null, "feat_Medical Conditions": null, "feat_Contentment": null, "feat_Personal Interests": null, "feat_Clothing": null, "feat_Secrets": "Holds the key to a valuable treasure", "feat_Background": null, "feat_Cultural Practices or Traditions": null, "feat_Personal Feelings": null, "feat_Parenting": null, "feat_Goals": null, "feat_Reaction": null, "feat_Fears": null, "feat_Physical Characteristics": null, "feat_Gender": null, "feat_Mental Health": null, "feat_Knowledge & Education": null, "feat_Fears & Anxieties": null, "feat_Political Affiliation": null, "feat_Romantic Status": null, "feat_Friendships": null, "feat_Gratitude Expressed": null, "feat_Morals": null, "feat_Personal Possessions": null, "feat_Wishes or Aspirations": null, "feat_Academic and Career Goals": null, "feat_Financial Status": null, "feat_Reasons for Action": null, "feat_Knowledge or Skills": null, "feat_Nationality": null, "feat_Action Tendencies": null, "feat_Feelings Toward School": null, "feat_Occupation": null, "feat_tone_attitude": null, "feat_emotional_state": null, "feat_pastimes_hobbies": null, "feat_Quotes": null, "feat_tone": null, "feat_question": null, "feat_Content_Tone": null, "feat_Past Medical History": null, "feat_Clothing or Fashion": null, "feat_Clothing & Accessories": null, "feat_Personal Challenges": null, "feat_Eye Color": null, "feat_Physical Sensations": null, "feat_Siblings": null, "feat_Speech & Dialogue": null, "feat_Relationships": null, "feat_Regrets": null, "feat_Time Period": null, "feat_Factual Information": null, "feat_Moral Dilemmas": null, "feat_Personal Struggles": null, "feat_Mental and Emotional Health": null, "feat_Phobias": null, "feat_FearOrWorry": null, "feat_Personal Motivations": null, "feat_Religious Beliefs and Practices": null, "feat_Thoughts and Opinions": null, "feat_Romantic Tensions": null, "feat_Current Location": null, "feat_Fears & Worries": null, "feat_Career": null, "feat_External Pressures": null, "feat_Sexual Orientation": null, "feat_Role or Occupation": null, "feat_Friendships_and_Relationships": null, "feat_Opinions": null, "feat_Appearance": null, "feat_Likes": null, "feat_Resolutions": null, "feat_Tone & Attitude": null, "feat_Instructions or Directions": null, "feat_Position or Job Title": null, "feat_assumptions_made": null, "feat_Animal": null, "feat_Manner of Speaking": null, "feat_Self-Perception": null, "feat_Subjective Opinions": null, "feat_Statements of Belief": null, "feat_Responsibilities": null, "feat_Personal Sacrifices": null, "feat_Academic and Professional Background": null, "feat_Cultural Background": null, "feat_past_experiences_impacting_actions": null, "feat_ExternalFacts": null, "feat_Statements": null, "feat_No\", \"Names": null, "feat_Self-Reflection": null, "feat_Fact": null, "feat_Empathy Statements": null, "feat_Hobbies or Pastimes": null, "feat_Moral and Ethical Considerations": null, "feat_Personal Tastes or Preferences": null, "feat_Subject": null, "feat_Lifestyle Choices": null, "feat_Sensory Experiences": null, "feat_Personal History": null, "feat_Physical Health": null, "feat_Temptations": null, "feat_Home Life": null, "feat_What They're Avoiding": null, "feat_Hobbies": null, "feat_Speech Characteristics": null, "feat_What They Do": null, "feat_Personal Relationships": null, "feat_What They Fear Most": null, "feat_Psychological Disorders": null, "feat_Ethnicity": null, "feat_Romantic Ideals": null, "feat_Ailments & Health Conditions": null, "feat_Education": null, "feat_Sentence Structure": null, "feat_Likes and Dislikes": null, "feat_Profession": null, "feat_Advice": null, "feat_Habits or Tendencies": null, "feat_Bad Habits or Flaws": null, "feat_Personal Motto or Philosophy": null, "feat_Quirks": null, "feat_Prejudices or Biases": null, "feat_Grief or Emotional Pain": null, "feat_past_tense_verbs": null, "feat_sentiments_expressed": null, "feat_Lifestyle": null, "feat_Stereotypes & Bias": null, "feat_Cultural Norms": null, "feat_Childhood Memories": null, "feat_Cause of Death": null, "feat_fear_or_phobia": null, "feat_thoughts_and_feelings": null, "feat_Stance on Controversial Issues": null, "feat_Job": null, "text": "`` i never said i did n't want you two to get to know each other-i said i did n't want you using him for a fling . ''", "feat_context": null, "feat_hair_color": null, "feat_fashion_style": null, "feat_Requests or Directions": null, "feat_Religious Views": null, "feat_Conflicts": null, "feat_Guilt": null, "feat_Person": null, "feat_Positive Experiences": null, "feat_Personal Accomplishments": null, "feat_Backstory": null, "feat_Circumstances of Death": null, "feat_Children": null, "feat_Virtues": null, "feat_Sentiment Towards the Topic": null, "feat_Time": null, "feat_External Factors Impacting the User": null, "feat_Hair Color": null, "feat_Financial": null, "feat_Societal Norms or Pressures": null, "feat_Hopes and Dreams": null, "feat_Insecurities": null, "feat_Emotions Expressed": null, "feat_Biological Sex": null, "feat_Body": null, "feat_Lifestyle and Social Status": null, "feat_Affiliations": null, "feat_Negative Traits": null, "feat_Positive Traits": null, "feat_Physical Traits": null, "feat_Reaction to Conflict": null, "feat_Racial or Ethnic Identity": null, "feat_Emotions": null, "feat_Behavior": null, "feat_External Conflicts": null, "feat_familial_roles": null, "feat_Apology Languages": null, "feat_Worries or Anxieties": null, "feat_Direct Address": null, "feat_Enneagram Type": null, "feat_Employment": null, "feat_Rumors or Gossip": null, "feat_Romantic Tension or Conflict": null, "feat_Personal Philosophies and Weltanschauungen": null, "feat_Things That Make Them Happy": null, "feat_Love Interests": null, "feat_Negative Emotions": null, "feat_Physical Symptoms": null, "feat_skills": null, "feat_Question or Prompt": null, "feat_Setting": null, "feat_External Challenges or Obstacles": null, "feat_Obligations and Responsibilities": null, "feat_Attributes": null, "feat_Decision-Making Process": null, "feat_Thoughts and Prayers": null, "feat_Feelings of the Speaker": null, "feat_familial_status": null, "feat_voice": null, "feat_Events": null, "feat_Familial Duties and Conflicts": null, "feat_Origin": null, "feat_Sexuality": "Heterosexual", "feat_Fears & Phobias": null, "feat_past_experience": null, "feat_Transportation": null, "feat_Job Titles": null, "feat_Personal Philosophies": null, "feat_Contexts and Backgrounds": null, "feat_Senses": null, "feat_Sentiment Towards Technology": null, "feat_Content Preferences": null, "feat_Mannerisms": null, "feat_Gratitude Journals": null, "feat_Psychological Traits": null, "feat_Advice Taken": null, "feat_Answer": null, "feat_Sentiment": null, "feat_Personal Names": null, "feat_Time Phrases": null, "feat_Nickname": null, "feat_Role": null, "feat_Request": null, "feat_Response": null, "feat_Physical_Pain_or_Illness": null, "feat_Body_Parts": null, "feat_Skills_&_Abilities": null, "feat_Major Life Events": null, "feat_Opinions About Controversial Topics": null, "feat_home_town": null, "feat_Afraid of": null, "feat_Food Preferences": null, "feat_Relationships and Family": null, "feat_Body Language": null, "feat_No\", \"Mental or Physical Disabilities": null, "feat_Other Feelings": null, "feat_Question or Hypothesis": null, "feat_PastTenseVerbs": null, "feat_Dreams": null, "feat_Dietary Restrictions or Choices": null, "feat_Plans and Intentions": null, "feat_Nicknames or Aliases": null, "feat_Symbols or Motifs": null, "feat_Passions or Interests": null, "feat_Description": null, "feat_Moral Values": null, "feat_Question or query": null, "feat_external_appearances": null, "feat_Themes": null, "feat_Chapter": null, "feat_General Sentiment": null, "feat_Traits": null, "feat_Tone&Style": null, "feat_Revenge or Retribution": null, "feat_Strengths": null, "feat_What they want": null, "feat_Priorities": null, "feat_Compliments Received": null, "feat_Daily Routine": null, "feat_Timeframe": null, "feat_tactics_or_strategies": null, "feat_Societal Norms or Expectations": null, "feat_Outfit": null, "feat_sentiment_score": 0.1596837944664031 }, { "feat_Unnamed: 0.1": 3631, "feat_Unnamed: 0": 338, "target": "\"Names\": \"Becky\", \"Familial Relationships\": \"John (nephew)\", \"External Pressures or Influences\": \"Influence of a persuasive mentor in professional decisions\", \"Personality Traits\": \"Friendly\", \"Hobbies or Interests\": \"Playing the piano\", \"Location\": \"A small cottage in the countryside\", \"Feelings\": \"Confusion\", \"Socioeconomic Status\": \"Impoverished children in a developing nation\"", "feat_Names": "Becky", "feat_Personality Traits": "Friendly", "feat_Motivations": null, "feat_Action": null, "feat_Feelings": "Confusion", "feat_Tone & Mood": null, "feat_Friendships and Alliances": null, "feat_Pets": null, "feat_Romantic Relationships": null, "feat_Familial Relationships": "John (nephew)", "feat_Location": "A small cottage in the countryside", "feat_Action Taken": null, "feat_Personal Beliefs and Values": null, "feat_Socioeconomic Status": "Impoverished children in a developing nation", "feat_Hobbies or Interests": "Playing the piano", "feat_Physical Appearance": null, "feat_Skills & Talents": null, "feat_Fear or Desire for Change": null, "feat_Family": null, "feat_Background and History": null, "feat_Goals and Aspirations": null, "feat_Thoughts and Inner Monologues": null, "feat_External Pressures or Influences": "Influence of a persuasive mentor in professional decisions", "feat_Flaws": null, "feat_hairstyle": null, "feat_Thoughts": null, "feat_Status": null, "feat_Past Experiences Influencing Actions": null, "feat_Phrase": null, "feat_, \"Weight": null, "feat_Rivalries or Conflicts": null, "feat_Marital Status": null, "feat_Weather": null, "feat_Reasons for Feelings": null, "feat_Cultural Practices": null, "feat_Moral or Ethical Dilemmas": null, "feat_Political Views": null, "feat_Mental or Emotional States": null, "feat_Age": null, "feat_Medical Conditions": null, "feat_Contentment": null, "feat_Personal Interests": null, "feat_Clothing": null, "feat_Secrets": null, "feat_Background": null, "feat_Cultural Practices or Traditions": null, "feat_Personal Feelings": null, "feat_Parenting": null, "feat_Goals": null, "feat_Reaction": null, "feat_Fears": null, "feat_Physical Characteristics": null, "feat_Gender": null, "feat_Mental Health": null, "feat_Knowledge & Education": null, "feat_Fears & Anxieties": null, "feat_Political Affiliation": null, "feat_Romantic Status": null, "feat_Friendships": null, "feat_Gratitude Expressed": null, "feat_Morals": null, "feat_Personal Possessions": null, "feat_Wishes or Aspirations": null, "feat_Academic and Career Goals": null, "feat_Financial Status": null, "feat_Reasons for Action": null, "feat_Knowledge or Skills": null, "feat_Nationality": null, "feat_Action Tendencies": null, "feat_Feelings Toward School": null, "feat_Occupation": null, "feat_tone_attitude": null, "feat_emotional_state": null, "feat_pastimes_hobbies": null, "feat_Quotes": null, "feat_tone": null, "feat_question": null, "feat_Content_Tone": null, "feat_Past Medical History": null, "feat_Clothing or Fashion": null, "feat_Clothing & Accessories": null, "feat_Personal Challenges": null, "feat_Eye Color": null, "feat_Physical Sensations": null, "feat_Siblings": null, "feat_Speech & Dialogue": null, "feat_Relationships": null, "feat_Regrets": null, "feat_Time Period": null, "feat_Factual Information": null, "feat_Moral Dilemmas": null, "feat_Personal Struggles": null, "feat_Mental and Emotional Health": null, "feat_Phobias": null, "feat_FearOrWorry": null, "feat_Personal Motivations": null, "feat_Religious Beliefs and Practices": null, "feat_Thoughts and Opinions": null, "feat_Romantic Tensions": null, "feat_Current Location": null, "feat_Fears & Worries": null, "feat_Career": null, "feat_External Pressures": null, "feat_Sexual Orientation": null, "feat_Role or Occupation": null, "feat_Friendships_and_Relationships": null, "feat_Opinions": null, "feat_Appearance": null, "feat_Likes": null, "feat_Resolutions": null, "feat_Tone & Attitude": null, "feat_Instructions or Directions": null, "feat_Position or Job Title": null, "feat_assumptions_made": null, "feat_Animal": null, "feat_Manner of Speaking": null, "feat_Self-Perception": null, "feat_Subjective Opinions": null, "feat_Statements of Belief": null, "feat_Responsibilities": null, "feat_Personal Sacrifices": null, "feat_Academic and Professional Background": null, "feat_Cultural Background": null, "feat_past_experiences_impacting_actions": null, "feat_ExternalFacts": null, "feat_Statements": null, "feat_No\", \"Names": null, "feat_Self-Reflection": null, "feat_Fact": null, "feat_Empathy Statements": null, "feat_Hobbies or Pastimes": null, "feat_Moral and Ethical Considerations": null, "feat_Personal Tastes or Preferences": null, "feat_Subject": null, "feat_Lifestyle Choices": null, "feat_Sensory Experiences": null, "feat_Personal History": null, "feat_Physical Health": null, "feat_Temptations": null, "feat_Home Life": null, "feat_What They're Avoiding": null, "feat_Hobbies": null, "feat_Speech Characteristics": null, "feat_What They Do": null, "feat_Personal Relationships": null, "feat_What They Fear Most": null, "feat_Psychological Disorders": null, "feat_Ethnicity": null, "feat_Romantic Ideals": null, "feat_Ailments & Health Conditions": null, "feat_Education": null, "feat_Sentence Structure": null, "feat_Likes and Dislikes": null, "feat_Profession": null, "feat_Advice": null, "feat_Habits or Tendencies": null, "feat_Bad Habits or Flaws": null, "feat_Personal Motto or Philosophy": null, "feat_Quirks": null, "feat_Prejudices or Biases": null, "feat_Grief or Emotional Pain": null, "feat_past_tense_verbs": null, "feat_sentiments_expressed": null, "feat_Lifestyle": null, "feat_Stereotypes & Bias": null, "feat_Cultural Norms": null, "feat_Childhood Memories": null, "feat_Cause of Death": null, "feat_fear_or_phobia": null, "feat_thoughts_and_feelings": null, "feat_Stance on Controversial Issues": null, "feat_Job": null, "text": "she spoke to john 's brothers , percy and georgie , before making her way around the room .", "feat_context": null, "feat_hair_color": null, "feat_fashion_style": null, "feat_Requests or Directions": null, "feat_Religious Views": null, "feat_Conflicts": null, "feat_Guilt": null, "feat_Person": null, "feat_Positive Experiences": null, "feat_Personal Accomplishments": null, "feat_Backstory": null, "feat_Circumstances of Death": null, "feat_Children": null, "feat_Virtues": null, "feat_Sentiment Towards the Topic": null, "feat_Time": null, "feat_External Factors Impacting the User": null, "feat_Hair Color": null, "feat_Financial": null, "feat_Societal Norms or Pressures": null, "feat_Hopes and Dreams": null, "feat_Insecurities": null, "feat_Emotions Expressed": null, "feat_Biological Sex": null, "feat_Body": null, "feat_Lifestyle and Social Status": null, "feat_Affiliations": null, "feat_Negative Traits": null, "feat_Positive Traits": null, "feat_Physical Traits": null, "feat_Reaction to Conflict": null, "feat_Racial or Ethnic Identity": null, "feat_Emotions": null, "feat_Behavior": null, "feat_External Conflicts": null, "feat_familial_roles": null, "feat_Apology Languages": null, "feat_Worries or Anxieties": null, "feat_Direct Address": null, "feat_Enneagram Type": null, "feat_Employment": null, "feat_Rumors or Gossip": null, "feat_Romantic Tension or Conflict": null, "feat_Personal Philosophies and Weltanschauungen": null, "feat_Things That Make Them Happy": null, "feat_Love Interests": null, "feat_Negative Emotions": null, "feat_Physical Symptoms": null, "feat_skills": null, "feat_Question or Prompt": null, "feat_Setting": null, "feat_External Challenges or Obstacles": null, "feat_Obligations and Responsibilities": null, "feat_Attributes": null, "feat_Decision-Making Process": null, "feat_Thoughts and Prayers": null, "feat_Feelings of the Speaker": null, "feat_familial_status": null, "feat_voice": null, "feat_Events": null, "feat_Familial Duties and Conflicts": null, "feat_Origin": null, "feat_Sexuality": null, "feat_Fears & Phobias": null, "feat_past_experience": null, "feat_Transportation": null, "feat_Job Titles": null, "feat_Personal Philosophies": null, "feat_Contexts and Backgrounds": null, "feat_Senses": null, "feat_Sentiment Towards Technology": null, "feat_Content Preferences": null, "feat_Mannerisms": null, "feat_Gratitude Journals": null, "feat_Psychological Traits": null, "feat_Advice Taken": null, "feat_Answer": null, "feat_Sentiment": null, "feat_Personal Names": null, "feat_Time Phrases": null, "feat_Nickname": null, "feat_Role": null, "feat_Request": null, "feat_Response": null, "feat_Physical_Pain_or_Illness": null, "feat_Body_Parts": null, "feat_Skills_&_Abilities": null, "feat_Major Life Events": null, "feat_Opinions About Controversial Topics": null, "feat_home_town": null, "feat_Afraid of": null, "feat_Food Preferences": null, "feat_Relationships and Family": null, "feat_Body Language": null, "feat_No\", \"Mental or Physical Disabilities": null, "feat_Other Feelings": null, "feat_Question or Hypothesis": null, "feat_PastTenseVerbs": null, "feat_Dreams": null, "feat_Dietary Restrictions or Choices": null, "feat_Plans and Intentions": null, "feat_Nicknames or Aliases": null, "feat_Symbols or Motifs": null, "feat_Passions or Interests": null, "feat_Description": null, "feat_Moral Values": null, "feat_Question or query": null, "feat_external_appearances": null, "feat_Themes": null, "feat_Chapter": null, "feat_General Sentiment": null, "feat_Traits": null, "feat_Tone&Style": null, "feat_Revenge or Retribution": null, "feat_Strengths": null, "feat_What they want": null, "feat_Priorities": null, "feat_Compliments Received": null, "feat_Daily Routine": null, "feat_Timeframe": null, "feat_tactics_or_strategies": null, "feat_Societal Norms or Expectations": null, "feat_Outfit": null, "feat_sentiment_score": 0.0642857142857142 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_Unnamed: 0.1": "Value(dtype='int64', id=None)", "feat_Unnamed: 0": "Value(dtype='int64', id=None)", "target": "Value(dtype='string', id=None)", "feat_Names": "Value(dtype='string', id=None)", "feat_Personality Traits": "Value(dtype='string', id=None)", "feat_Motivations": "Value(dtype='string', id=None)", "feat_Action": "Value(dtype='string', id=None)", "feat_Feelings": "Value(dtype='string', id=None)", "feat_Tone & Mood": "Value(dtype='string', id=None)", "feat_Friendships and Alliances": "Value(dtype='string', id=None)", "feat_Pets": "Value(dtype='string', id=None)", "feat_Romantic Relationships": "Value(dtype='string', id=None)", "feat_Familial Relationships": "Value(dtype='string', id=None)", "feat_Location": "Value(dtype='string', id=None)", "feat_Action Taken": "Value(dtype='string', id=None)", "feat_Personal Beliefs and Values": "Value(dtype='string', id=None)", "feat_Socioeconomic Status": "Value(dtype='string', id=None)", "feat_Hobbies or Interests": "Value(dtype='string', id=None)", "feat_Physical Appearance": "Value(dtype='string', id=None)", "feat_Skills & Talents": "Value(dtype='string', id=None)", "feat_Fear or Desire for Change": "Value(dtype='string', id=None)", "feat_Family": "Value(dtype='string', id=None)", "feat_Background and History": "Value(dtype='string', id=None)", "feat_Goals and Aspirations": "Value(dtype='string', id=None)", "feat_Thoughts and Inner Monologues": "Value(dtype='string', id=None)", "feat_External Pressures or Influences": "Value(dtype='string', id=None)", "feat_Flaws": "Value(dtype='string', id=None)", "feat_hairstyle": "Value(dtype='string', id=None)", "feat_Thoughts": "Value(dtype='string', id=None)", "feat_Status": "Value(dtype='string', id=None)", "feat_Past Experiences Influencing Actions": "Value(dtype='string', id=None)", "feat_Phrase": "Value(dtype='string', id=None)", "feat_, \"Weight": "Value(dtype='string', id=None)", "feat_Rivalries or Conflicts": "Value(dtype='string', id=None)", "feat_Marital Status": "Value(dtype='string', id=None)", "feat_Weather": "Value(dtype='string', id=None)", "feat_Reasons for 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"feat_fear_or_phobia": "Value(dtype='string', id=None)", "feat_thoughts_and_feelings": "Value(dtype='string', id=None)", "feat_Stance on Controversial Issues": "Value(dtype='string', id=None)", "feat_Job": "Value(dtype='string', id=None)", "text": "Value(dtype='string', id=None)", "feat_context": "Value(dtype='string', id=None)", "feat_hair_color": "Value(dtype='string', id=None)", "feat_fashion_style": "Value(dtype='string', id=None)", "feat_Requests or Directions": "Value(dtype='string', id=None)", "feat_Religious Views": "Value(dtype='string', id=None)", "feat_Conflicts": "Value(dtype='string', id=None)", "feat_Guilt": "Value(dtype='string', id=None)", "feat_Person": "Value(dtype='string', id=None)", "feat_Positive Experiences": "Value(dtype='string', id=None)", "feat_Personal Accomplishments": "Value(dtype='string', id=None)", "feat_Backstory": "Value(dtype='string', id=None)", "feat_Circumstances of Death": "Value(dtype='string', id=None)", "feat_Children": "Value(dtype='string', id=None)", "feat_Virtues": "Value(dtype='string', id=None)", "feat_Sentiment Towards the Topic": "Value(dtype='string', id=None)", "feat_Time": "Value(dtype='string', id=None)", "feat_External Factors Impacting the User": "Value(dtype='string', id=None)", "feat_Hair Color": "Value(dtype='string', id=None)", "feat_Financial": "Value(dtype='string', id=None)", "feat_Societal Norms or Pressures": "Value(dtype='string', id=None)", "feat_Hopes and Dreams": "Value(dtype='string', id=None)", "feat_Insecurities": "Value(dtype='string', id=None)", "feat_Emotions Expressed": "Value(dtype='string', id=None)", "feat_Biological Sex": "Value(dtype='string', id=None)", "feat_Body": "Value(dtype='string', id=None)", "feat_Lifestyle and Social Status": "Value(dtype='string', id=None)", "feat_Affiliations": "Value(dtype='string', id=None)", "feat_Negative Traits": "Value(dtype='string', id=None)", "feat_Positive Traits": "Value(dtype='string', id=None)", "feat_Physical Traits": "Value(dtype='string', id=None)", "feat_Reaction to Conflict": "Value(dtype='string', id=None)", "feat_Racial or Ethnic Identity": "Value(dtype='string', id=None)", "feat_Emotions": "Value(dtype='string', id=None)", "feat_Behavior": "Value(dtype='string', id=None)", "feat_External Conflicts": "Value(dtype='string', id=None)", "feat_familial_roles": "Value(dtype='string', id=None)", "feat_Apology Languages": "Value(dtype='string', id=None)", "feat_Worries or Anxieties": "Value(dtype='string', id=None)", "feat_Direct Address": "Value(dtype='string', id=None)", "feat_Enneagram Type": "Value(dtype='string', id=None)", "feat_Employment": "Value(dtype='string', id=None)", "feat_Rumors or Gossip": "Value(dtype='string', id=None)", "feat_Romantic Tension or Conflict": "Value(dtype='string', id=None)", "feat_Personal Philosophies and Weltanschauungen": "Value(dtype='string', id=None)", "feat_Things That Make Them Happy": "Value(dtype='string', id=None)", "feat_Love Interests": "Value(dtype='string', id=None)", "feat_Negative Emotions": "Value(dtype='string', id=None)", "feat_Physical Symptoms": "Value(dtype='string', id=None)", "feat_skills": "Value(dtype='string', id=None)", "feat_Question or Prompt": "Value(dtype='string', id=None)", "feat_Setting": "Value(dtype='string', id=None)", "feat_External Challenges or Obstacles": "Value(dtype='string', id=None)", "feat_Obligations and Responsibilities": "Value(dtype='string', id=None)", "feat_Attributes": "Value(dtype='string', id=None)", "feat_Decision-Making Process": "Value(dtype='string', id=None)", "feat_Thoughts and Prayers": "Value(dtype='string', id=None)", "feat_Feelings of the Speaker": "Value(dtype='string', id=None)", "feat_familial_status": "Value(dtype='string', id=None)", "feat_voice": "Value(dtype='string', id=None)", "feat_Events": "Value(dtype='string', id=None)", "feat_Familial Duties and Conflicts": "Value(dtype='string', id=None)", "feat_Origin": "Value(dtype='string', id=None)", "feat_Sexuality": "Value(dtype='string', id=None)", "feat_Fears & Phobias": "Value(dtype='string', id=None)", "feat_past_experience": "Value(dtype='string', id=None)", "feat_Transportation": "Value(dtype='string', id=None)", "feat_Job Titles": "Value(dtype='string', id=None)", "feat_Personal Philosophies": "Value(dtype='string', id=None)", "feat_Contexts and Backgrounds": "Value(dtype='string', id=None)", "feat_Senses": "Value(dtype='string', id=None)", "feat_Sentiment Towards Technology": "Value(dtype='string', id=None)", "feat_Content Preferences": "Value(dtype='string', id=None)", "feat_Mannerisms": "Value(dtype='string', id=None)", "feat_Gratitude Journals": "Value(dtype='string', id=None)", "feat_Psychological Traits": "Value(dtype='string', id=None)", "feat_Advice Taken": "Value(dtype='string', id=None)", "feat_Answer": "Value(dtype='string', id=None)", "feat_Sentiment": "Value(dtype='string', id=None)", "feat_Personal Names": "Value(dtype='string', id=None)", "feat_Time Phrases": "Value(dtype='string', id=None)", "feat_Nickname": "Value(dtype='string', id=None)", "feat_Role": "Value(dtype='string', id=None)", "feat_Request": "Value(dtype='string', id=None)", "feat_Response": "Value(dtype='string', id=None)", "feat_Physical_Pain_or_Illness": "Value(dtype='string', id=None)", "feat_Body_Parts": "Value(dtype='string', id=None)", "feat_Skills_&_Abilities": "Value(dtype='string', id=None)", "feat_Major Life Events": "Value(dtype='string', id=None)", "feat_Opinions About Controversial Topics": "Value(dtype='string', id=None)", "feat_home_town": "Value(dtype='string', id=None)", "feat_Afraid of": "Value(dtype='string', id=None)", "feat_Food Preferences": "Value(dtype='string', id=None)", "feat_Relationships and Family": "Value(dtype='string', id=None)", "feat_Body Language": "Value(dtype='string', id=None)", "feat_No\", \"Mental or Physical Disabilities": "Value(dtype='string', id=None)", "feat_Other Feelings": "Value(dtype='string', id=None)", "feat_Question or Hypothesis": "Value(dtype='string', id=None)", "feat_PastTenseVerbs": "Value(dtype='string', id=None)", "feat_Dreams": "Value(dtype='string', id=None)", "feat_Dietary Restrictions or Choices": "Value(dtype='string', id=None)", "feat_Plans and Intentions": "Value(dtype='string', id=None)", "feat_Nicknames or Aliases": "Value(dtype='string', id=None)", "feat_Symbols or Motifs": "Value(dtype='string', id=None)", "feat_Passions or Interests": "Value(dtype='string', id=None)", "feat_Description": "Value(dtype='string', id=None)", "feat_Moral Values": "Value(dtype='string', id=None)", "feat_Question or query": "Value(dtype='string', id=None)", "feat_external_appearances": "Value(dtype='string', id=None)", "feat_Themes": "Value(dtype='string', id=None)", "feat_Chapter": "Value(dtype='string', id=None)", "feat_General Sentiment": "Value(dtype='string', id=None)", "feat_Traits": "Value(dtype='string', id=None)", "feat_Tone&Style": "Value(dtype='string', id=None)", "feat_Revenge or Retribution": "Value(dtype='string', id=None)", "feat_Strengths": "Value(dtype='string', id=None)", "feat_What they want": "Value(dtype='string', id=None)", "feat_Priorities": "Value(dtype='string', id=None)", "feat_Compliments Received": "Value(dtype='string', id=None)", "feat_Daily Routine": "Value(dtype='string', id=None)", "feat_Timeframe": "Value(dtype='string', id=None)", "feat_tactics_or_strategies": "Value(dtype='string', id=None)", "feat_Societal Norms or Expectations": "Value(dtype='string', id=None)", "feat_Outfit": "Value(dtype='string', id=None)", "feat_sentiment_score": "Value(dtype='float64', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 3958 \| \| valid \| 990 \|	Sambosis/autotrain-data-big	[ "task_categories:summarization", "language:en", "region:us" ]	2023-09-14T02:43:39+00:00	{"language": ["en"], "task_categories": ["summarization"]}	2023-09-14T02:48:01+00:00	[]	[ "en" ]	TAGS #task_categories-summarization #language-English #region-us	AutoTrain Dataset for project: big ================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project big. ### Languages The BCP-47 code for the dataset's language is en. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:	[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]	[ "TAGS\n#task_categories-summarization #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]	[ 20, 26, 17, 23, 27 ]	[ "passage: TAGS\n#task_categories-summarization #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]
0dc8394d1b32b9f3864aa5642a12fcc3c171795a	# Dataset Card for "dataset-multiple-myeloma-study-dictionary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	nzindoc/dataset-multiple-myeloma-study-dictionary	[ "region:us" ]	2023-09-14T02:58:55+00:00	{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1285724160, "num_examples": 1975680}], "download_size": 30419669, "dataset_size": 1285724160}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T03:00:10+00:00	[]	[]	TAGS #region-us	# Dataset Card for "dataset-multiple-myeloma-study-dictionary" More Information needed	[ "# Dataset Card for \"dataset-multiple-myeloma-study-dictionary\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-multiple-myeloma-study-dictionary\"\n\nMore Information needed" ]	[ 6, 26 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"dataset-multiple-myeloma-study-dictionary\"\n\nMore Information needed" ]
9fe6e146a49427b1e035f89643065fc358a273c0	# Dataset of tada_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls) This is the dataset of tada_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, brown_hair, green_eyes, headphones, 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 \| 419.54 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 298.94 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1009 \| 552.75 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 387.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1009 \| 697.36 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_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/tada_riina_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 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, serafuku, solo, blush, looking_at_viewer, skirt, sitting, smile \| \| 1 \| 8 \| ![](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, necklace, plaid_skirt, solo, blush, clothes_writing, looking_at_viewer, smile, belt, open_mouth, black_thighhighs \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, belt, clothes_writing, necklace, plaid_skirt, solo, tank_top, open_mouth \| \| 3 \| 5 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, open_mouth, solo, looking_at_viewer, :d, dress, hair_ornament, fingerless_gloves \| \| 4 \| 12 \| ![](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, smile, solo, fingerless_gloves, skirt, microphone, open_mouth, belt, detached_sleeves, thighhighs, choker, looking_at_viewer, chain, garter_straps, cuffs \| \| 5 \| 11 \| ![](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) \| cat_ear_headphones, cat_ears, midriff, navel, looking_at_viewer, open_mouth, smile, 2girls, necklace, blush, 1girl, belt, black_skirt, boots, miniskirt, solo_focus, thighhighs \| \| 6 \| 6 \| ![](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, bangs, hair_between_eyes, looking_at_viewer, shirt, solo, white_background, blush, shiny_hair, simple_background, :d, miniskirt, open_mouth, pleated_skirt, sleeveless, black_skirt, collarbone, jacket, long_sleeves, open_clothes \| \| 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, cleavage, medium_breasts, microphone_stand, smile, solo, thighhighs, choker, looking_at_viewer, skirt, belt, jewelry, open_mouth, boots, gloves, midriff, navel \| \| 8 \| 13 \| ![](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, cleavage, looking_at_viewer, medium_breasts, smile, solo, navel, open_mouth, blue_bikini, blush, jewelry \| \| 9 \| 5 \| ![](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, pleated_skirt, serafuku, short_sleeves, solo, white_shirt, bangs, blue_sailor_collar, blue_skirt, hair_between_eyes, kneehighs, looking_at_viewer, yellow_neckerchief, black_socks, brown_footwear, full_body, miniskirt, grin, headphones_around_neck, loafers, navel, one_eye_closed, open_mouth, simple_background \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| serafuku \| solo \| blush \| looking_at_viewer \| skirt \| sitting \| smile \| necklace \| plaid_skirt \| clothes_writing \| belt \| open_mouth \| black_thighhighs \| tank_top \| :d \| dress \| hair_ornament \| fingerless_gloves \| microphone \| detached_sleeves \| thighhighs \| choker \| chain \| garter_straps \| cuffs \| cat_ear_headphones \| cat_ears \| midriff \| navel \| 2girls \| black_skirt \| boots \| miniskirt \| solo_focus \| bangs \| hair_between_eyes \| shirt \| white_background \| shiny_hair \| simple_background \| pleated_skirt \| sleeveless \| collarbone \| jacket \| long_sleeves \| open_clothes \| cleavage \| medium_breasts \| microphone_stand \| jewelry \| gloves \| blue_bikini \| short_sleeves \| white_shirt \| blue_sailor_collar \| blue_skirt \| kneehighs \| yellow_neckerchief \| black_socks \| brown_footwear \| full_body \| grin \| headphones_around_neck \| loafers \| one_eye_closed \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:-------\|:--------\|:--------------------\|:--------\|:----------\|:--------\|:-----------\|:--------------\|:------------------\|:-------\|:-------------\|:-------------------\|:-----------\|:-----\|:--------\|:----------------\|:--------------------\|:-------------\|:-------------------\|:-------------\|:---------\|:--------\|:----------------\|:--------\|:---------------------\|:-----------\|:----------\|:--------\|:---------\|:--------------\|:--------\|:------------\|:-------------\|:--------\|:--------------------\|:--------\|:-------------------\|:-------------\|:--------------------\|:----------------\|:-------------\|:-------------\|:---------\|:---------------\|:---------------\|:-----------\|:-----------------\|:-------------------\|:----------\|:---------\|:--------------\|:----------------\|:--------------\|:---------------------\|:-------------\|:------------\|:---------------------\|:--------------\|:-----------------\|:------------\|:-------\|:-------------------------\|:----------\|:-----------------\| \| 0 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 8 \| ![](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 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 6 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 13 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| X \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/tada_riina_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T03:03:05+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T15:18:08+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of tada\_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls) =============================================================== This is the dataset of tada\_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'short\_hair, brown\_hair, green\_eyes, headphones, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
c802b66008e6cb0495afe70202b662c1db7128da	# Dataset of hisakawa_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls) This is the dataset of hisakawa_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `long_hair, bangs, grey_hair, braid, blue_eyes, breasts, braided_bangs, earrings, very_long_hair, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-------------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 500 \| 860.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 453.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1294 \| 995.48 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 743.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1294 \| 1.47 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_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/hisakawa_hayate_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) \| blush, collared_shirt, fingerless_gloves, looking_at_viewer, open_jacket, plaid_necktie, white_gloves, white_jacket, white_shirt, 1girl, jewelry, open_mouth, pleated_skirt, solo, white_skirt, :d, white_background, bare_shoulders, off_shoulder, short_sleeves, sleeveless_shirt, hands_up, star_(symbol), starry_background \| \| 1 \| 23 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| looking_at_viewer, white_shirt, blush, jewelry, collared_shirt, white_background, 1girl, dress_shirt, juliet_sleeves, black_jacket, simple_background, solo, black_skirt, open_mouth, collarbone, :d \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| collarbone, jacket, looking_at_viewer, necklace, off_shoulder, open_clothes, :d, bare_shoulders, blush, green_skirt, open_mouth, 1girl, aqua_skirt, heart_hands, long_sleeves, solo, white_shirt, simple_background, white_background \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, collarbone, jewelry, simple_background, solo, white_background, cleavage, looking_at_viewer, navel, large_breasts, micro_bikini, sweat, gold_bikini, skindentation, upper_body, white_bikini, yellow_bikini \| \| 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, cleavage, collarbone, looking_at_viewer, solo, open_mouth, outdoors, :d, day, frilled_bikini, navel, ocean, water, white_bikini, bikini_skirt, blue_sky, blush, bare_shoulders, cloud, bracelet, wet \| \| 5 \| 6 \| ![](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) \| blue_sky, collarbone, day, looking_at_viewer, navel, ocean, outdoors, smile, 1girl, solo, string_bikini, beach, blush, cleavage, cloud, cowboy_shot, halterneck, open_mouth, armpits, horizon, side-tie_bikini_bottom, standing \| \| 6 \| 17 \| ![](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, looking_at_viewer, hair_flower, hair_ribbon, open_mouth, solo, bare_shoulders, blush, jewelry, choker, collarbone, white_dress, simple_background, white_background, :d, off-shoulder_dress, short_sleeves, green_ribbon, hand_up, upper_body, white_flower \| \| 7 \| 17 \| ![](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, looking_at_viewer, solo, necktie, wrist_cuffs, white_sailor_collar, blush, blue_dress, puffy_short_sleeves, open_mouth, hair_ornament, sailor_dress, white_background, :d, grey_eyes, pleated_dress, pleated_skirt, shirt \| \| 8 \| 9 \| ![](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) \| 1boy, blush, hetero, open_mouth, solo_focus, completely_nude, jewelry, nipples, penis, 1girl, sweat, collarbone, large_breasts, looking_at_viewer, mosaic_censoring, cum, navel, pov, sex \| \| 9 \| 10 \| ![](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) \| black_dress, blush, looking_at_viewer, enmaided, maid_apron, white_apron, 1girl, maid_headdress, solo, frilled_apron, open_mouth, simple_background, puffy_short_sleeves, white_background, :d, jewelry, long_sleeves, neck_ribbon \| \| 10 \| 7 \| ![](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) \| 1girl, blue_headwear, bow, detached_sleeves, long_sleeves, :d, bare_shoulders, beret, blush, jewelry, looking_at_viewer, open_mouth, solo, white_background, belt_buckle, blue_shirt, collared_shirt, pink_flower, simple_background, sweater, blue_sleeves, pleated_skirt, sleeveless_shirt, sleeves_past_wrists, hand_up, red_flower, tilted_headwear, yellow_skirt \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, blush, collarbone, cow_ears, cow_horns, cow_print, fake_animal_ears, fake_horns, looking_at_viewer, navel, neck_bell, solo, cleavage, cowbell, hairband, heart, jewelry, large_breasts, open_mouth, print_bikini, smile, white_background, 2021, ;d, holding, milk_bottle, one_eye_closed, print_legwear, side-tie_bikini_bottom, simple_background, two-tone_background, white_bikini, white_thighhighs, year_of_the_ox \| \| 12 \| 11 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| obi, blush, floral_print, long_sleeves, print_kimono, wide_sleeves, 1girl, blue_kimono, hair_flower, holding, jewelry, solo, looking_at_viewer, :d, open_mouth, blue_flower, upper_body, white_background \| \| 13 \| 11 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| looking_at_viewer, playboy_bunny, 1girl, bowtie, cleavage, detached_collar, fake_animal_ears, rabbit_ears, solo, strapless_leotard, blush, wrist_cuffs, bare_shoulders, collarbone, black_leotard, black_pantyhose, simple_background, smile, white_background, hairband, open_mouth, covered_navel, high_heels, large_breasts, wing_collar \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| blush \| collared_shirt \| fingerless_gloves \| looking_at_viewer \| open_jacket \| plaid_necktie \| white_gloves \| white_jacket \| white_shirt \| 1girl \| jewelry \| open_mouth \| pleated_skirt \| solo \| white_skirt \| :d \| white_background \| bare_shoulders \| off_shoulder \| short_sleeves \| sleeveless_shirt \| hands_up \| star_(symbol) \| starry_background \| dress_shirt \| juliet_sleeves \| black_jacket \| simple_background \| black_skirt \| collarbone \| jacket \| necklace \| open_clothes \| green_skirt \| aqua_skirt \| heart_hands \| long_sleeves \| cleavage \| navel \| large_breasts \| micro_bikini \| sweat \| gold_bikini \| skindentation \| upper_body \| white_bikini \| yellow_bikini \| outdoors \| day \| frilled_bikini \| ocean \| water \| bikini_skirt \| blue_sky \| cloud \| bracelet \| wet \| smile \| string_bikini \| beach \| cowboy_shot \| halterneck \| armpits \| horizon \| side-tie_bikini_bottom \| standing \| hair_flower \| hair_ribbon \| choker \| white_dress \| off-shoulder_dress \| green_ribbon \| hand_up \| white_flower \| necktie \| wrist_cuffs \| white_sailor_collar \| blue_dress \| puffy_short_sleeves \| hair_ornament \| sailor_dress \| grey_eyes \| pleated_dress \| shirt \| 1boy \| hetero \| solo_focus \| completely_nude \| nipples \| penis \| mosaic_censoring \| cum \| pov \| sex \| black_dress \| enmaided \| maid_apron \| white_apron \| maid_headdress \| frilled_apron \| neck_ribbon \| blue_headwear \| bow \| detached_sleeves \| beret \| belt_buckle \| blue_shirt \| pink_flower \| sweater \| blue_sleeves \| sleeves_past_wrists \| red_flower \| tilted_headwear \| yellow_skirt \| cow_ears \| cow_horns \| cow_print \| fake_animal_ears \| fake_horns \| neck_bell \| cowbell \| hairband \| heart \| print_bikini \| 2021 \| ;d \| holding \| milk_bottle \| one_eye_closed \| print_legwear \| two-tone_background \| white_thighhighs \| year_of_the_ox \| obi \| floral_print \| print_kimono \| wide_sleeves \| blue_kimono \| blue_flower \| playboy_bunny \| bowtie \| detached_collar \| rabbit_ears \| strapless_leotard \| black_leotard \| black_pantyhose \| covered_navel \| high_heels \| wing_collar \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:-----------------\|:--------------------\|:--------------------\|:--------------\|:----------------\|:---------------\|:---------------\|:--------------\|:--------\|:----------\|:-------------\|:----------------\|:-------\|:--------------\|:-----\|:-------------------\|:-----------------\|:---------------\|:----------------\|:-------------------\|:-----------\|:----------------\|:--------------------\|:--------------\|:-----------------\|:---------------\|:--------------------\|:--------------\|:-------------\|:---------\|:-----------\|:---------------\|:--------------\|:-------------\|:--------------\|:---------------\|:-----------\|:--------\|:----------------\|:---------------\|:--------\|:--------------\|:----------------\|:-------------\|:---------------\|:----------------\|:-----------\|:------\|:-----------------\|:--------\|:--------\|:---------------\|:-----------\|:--------\|:-----------\|:------\|:--------\|:----------------\|:--------\|:--------------\|:-------------\|:----------\|:----------\|:-------------------------\|:-----------\|:--------------\|:--------------\|:---------\|:--------------\|:---------------------\|:---------------\|:----------\|:---------------\|:----------\|:--------------\|:----------------------\|:-------------\|:----------------------\|:----------------\|:---------------\|:------------\|:----------------\|:--------\|:-------\|:---------\|:-------------\|:------------------\|:----------\|:--------\|:-------------------\|:------\|:------\|:------\|:--------------\|:-----------\|:-------------\|:--------------\|:-----------------\|:----------------\|:--------------\|:----------------\|:------\|:-------------------\|:--------\|:--------------\|:-------------\|:--------------\|:----------\|:---------------\|:----------------------\|:-------------\|:------------------\|:---------------\|:-----------\|:------------\|:------------\|:-------------------\|:-------------\|:------------\|:----------\|:-----------\|:--------\|:---------------\|:-------\|:-----\|:----------\|:--------------\|:-----------------\|:----------------\|:----------------------\|:-------------------\|:-----------------\|:------\|:---------------\|:---------------\|:---------------\|:--------------\|:--------------\|:----------------\|:---------\|:------------------\|:--------------\|:--------------------\|:----------------\|:------------------\|:----------------\|:-------------\|:--------------\| \| 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 \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 23 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| X \| \| \| \| \| X \| X \| \| X \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| X \| \| \| \| \| \| X \| X \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| X \| \| \| \| \| \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| X \| X \| \| X \| \| \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 9 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 10 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 7 \| ![](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 \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 5 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 12 \| 11 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 13 \| 11 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-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 \|	CyberHarem/hisakawa_hayate_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T03:34:11+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T15:22:45+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of hisakawa\_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of hisakawa\_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'long\_hair, bangs, grey\_hair, braid, blue\_eyes, breasts, braided\_bangs, earrings, very\_long\_hair, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
81df905a36243c3bd2e24bde182bad0454905dcb	# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck ## Dataset Description - Homepage: - Repository: https://huggingface.co/NoIdeaLand/test-2048-1500ck - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [NoIdeaLand/test-2048-1500ck](https://huggingface.co/NoIdeaLand/test-2048-1500ck) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_NoIdeaLand__test-2048-1500ck", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-14T04:39:40.489809](https://huggingface.co/datasets/open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck/blob/main/results_2023-09-14T04-39-40.489809.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": 0.26196111221791213, "acc_stderr": 0.03173586961427775, "acc_norm": 0.2653334325357461, "acc_norm_stderr": 0.03173833592722594, "mc1": 0.23990208078335373, "mc1_stderr": 0.014948812679062137, "mc2": 0.4095943166947606, "mc2_stderr": 0.014642509125225842 }, "harness\|arc:challenge\|25": { "acc": 0.33532423208191126, "acc_stderr": 0.013796182947785564, "acc_norm": 0.36689419795221845, "acc_norm_stderr": 0.014084133118104294 }, "harness\|hellaswag\|10": { "acc": 0.45817566221868156, "acc_stderr": 0.004972293764978723, "acc_norm": 0.6255725951005776, "acc_norm_stderr": 0.004829856058603573 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.24444444444444444, "acc_stderr": 0.037125378336148665, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.037125378336148665 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.19078947368421054, "acc_stderr": 0.031975658210325, "acc_norm": 0.19078947368421054, "acc_norm_stderr": 0.031975658210325 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118366, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118366 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2986111111111111, "acc_stderr": 0.03827052357950756, "acc_norm": 0.2986111111111111, "acc_norm_stderr": 0.03827052357950756 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.24, "acc_stderr": 0.04292346959909281, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909281 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.2023121387283237, "acc_stderr": 0.03063114553919882, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.03063114553919882 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.3191489361702128, "acc_stderr": 0.030472973363380042, "acc_norm": 0.3191489361702128, "acc_norm_stderr": 0.030472973363380042 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.2896551724137931, "acc_stderr": 0.03780019230438015, "acc_norm": 0.2896551724137931, "acc_norm_stderr": 0.03780019230438015 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.2328042328042328, "acc_stderr": 0.021765961672154523, "acc_norm": 0.2328042328042328, "acc_norm_stderr": 0.021765961672154523 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.23015873015873015, "acc_stderr": 0.03764950879790606, "acc_norm": 0.23015873015873015, "acc_norm_stderr": 0.03764950879790606 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036625, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036625 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.22258064516129034, "acc_stderr": 0.023664216671642518, "acc_norm": 0.22258064516129034, "acc_norm_stderr": 0.023664216671642518 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.23645320197044334, "acc_stderr": 0.029896114291733552, "acc_norm": 0.23645320197044334, "acc_norm_stderr": 0.029896114291733552 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.3151515151515151, "acc_stderr": 0.0362773057502241, "acc_norm": 0.3151515151515151, "acc_norm_stderr": 0.0362773057502241 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.19696969696969696, "acc_stderr": 0.02833560973246335, "acc_norm": 0.19696969696969696, "acc_norm_stderr": 0.02833560973246335 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.30569948186528495, "acc_stderr": 0.03324837939758159, "acc_norm": 0.30569948186528495, "acc_norm_stderr": 0.03324837939758159 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.2692307692307692, "acc_stderr": 0.022489389793654824, "acc_norm": 0.2692307692307692, "acc_norm_stderr": 0.022489389793654824 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.25555555555555554, "acc_stderr": 0.026593939101844086, "acc_norm": 0.25555555555555554, "acc_norm_stderr": 0.026593939101844086 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.22268907563025211, "acc_stderr": 0.02702543349888239, "acc_norm": 0.22268907563025211, "acc_norm_stderr": 0.02702543349888239 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.2119205298013245, "acc_stderr": 0.03336767086567977, "acc_norm": 0.2119205298013245, "acc_norm_stderr": 0.03336767086567977 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.22018348623853212, "acc_stderr": 0.01776597865232757, "acc_norm": 0.22018348623853212, "acc_norm_stderr": 0.01776597865232757 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.2361111111111111, "acc_stderr": 0.028963702570791033, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.028963702570791033 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.28431372549019607, "acc_stderr": 0.03166009679399812, "acc_norm": 0.28431372549019607, "acc_norm_stderr": 0.03166009679399812 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.2869198312236287, "acc_stderr": 0.02944377302259469, "acc_norm": 0.2869198312236287, "acc_norm_stderr": 0.02944377302259469 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.39461883408071746, "acc_stderr": 0.03280400504755291, "acc_norm": 0.39461883408071746, "acc_norm_stderr": 0.03280400504755291 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.24793388429752067, "acc_stderr": 0.03941897526516302, "acc_norm": 0.24793388429752067, "acc_norm_stderr": 0.03941897526516302 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.043300437496507437, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.043300437496507437 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.25153374233128833, "acc_stderr": 0.03408997886857529, "acc_norm": 0.25153374233128833, "acc_norm_stderr": 0.03408997886857529 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.29464285714285715, "acc_stderr": 0.04327040932578728, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.04327040932578728 }, "harness\|hendrycksTest-management\|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.3076923076923077, "acc_stderr": 0.030236389942173095, "acc_norm": 0.3076923076923077, "acc_norm_stderr": 0.030236389942173095 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.2835249042145594, "acc_stderr": 0.016117318166832265, "acc_norm": 0.2835249042145594, "acc_norm_stderr": 0.016117318166832265 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.2745664739884393, "acc_stderr": 0.02402774515526501, "acc_norm": 0.2745664739884393, "acc_norm_stderr": 0.02402774515526501 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.27450980392156865, "acc_stderr": 0.025553169991826507, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.025553169991826507 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2347266881028939, "acc_stderr": 0.024071805887677045, "acc_norm": 0.2347266881028939, "acc_norm_stderr": 0.024071805887677045 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2553191489361702, "acc_stderr": 0.02601199293090202, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.02601199293090202 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.2627118644067797, "acc_stderr": 0.01124054551499566, "acc_norm": 0.2627118644067797, "acc_norm_stderr": 0.01124054551499566 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.21691176470588236, "acc_stderr": 0.025035845227711254, "acc_norm": 0.21691176470588236, "acc_norm_stderr": 0.025035845227711254 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.27450980392156865, "acc_stderr": 0.0180540274588152, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.0180540274588152 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.2818181818181818, "acc_stderr": 0.04309118709946458, "acc_norm": 0.2818181818181818, "acc_norm_stderr": 0.04309118709946458 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.1836734693877551, "acc_stderr": 0.024789071332007653, "acc_norm": 0.1836734693877551, "acc_norm_stderr": 0.024789071332007653 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.29518072289156627, "acc_stderr": 0.035509201856896294, "acc_norm": 0.29518072289156627, "acc_norm_stderr": 0.035509201856896294 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.3157894736842105, "acc_stderr": 0.03565079670708311, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.03565079670708311 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.23990208078335373, "mc1_stderr": 0.014948812679062137, "mc2": 0.4095943166947606, "mc2_stderr": 0.014642509125225842 } } ``` ### 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]	open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck	[ "region:us" ]	2023-09-14T03:39:54+00:00	{"pretty_name": "Evaluation run of NoIdeaLand/test-2048-1500ck", "dataset_summary": "Dataset automatically created during the evaluation run of model [NoIdeaLand/test-2048-1500ck](https://huggingface.co/NoIdeaLand/test-2048-1500ck) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-14T04:39:40.489809](https://huggingface.co/datasets/open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck/blob/main/results_2023-09-14T04-39-40.489809.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\": 0.26196111221791213,\n \"acc_stderr\": 0.03173586961427775,\n \"acc_norm\": 0.2653334325357461,\n \"acc_norm_stderr\": 0.03173833592722594,\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062137,\n \"mc2\": 0.4095943166947606,\n \"mc2_stderr\": 0.014642509125225842\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.33532423208191126,\n \"acc_stderr\": 0.013796182947785564,\n \"acc_norm\": 0.36689419795221845,\n \"acc_norm_stderr\": 0.014084133118104294\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.45817566221868156,\n \"acc_stderr\": 0.004972293764978723,\n \"acc_norm\": 0.6255725951005776,\n \"acc_norm_stderr\": 0.004829856058603573\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.24444444444444444,\n \"acc_stderr\": 0.037125378336148665,\n \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.037125378336148665\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.19078947368421054,\n \"acc_stderr\": 0.031975658210325,\n \"acc_norm\": 0.19078947368421054,\n \"acc_norm_stderr\": 0.031975658210325\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118366,\n \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118366\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2986111111111111,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.2986111111111111,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.2023121387283237,\n \"acc_stderr\": 0.03063114553919882,\n \"acc_norm\": 0.2023121387283237,\n \"acc_norm_stderr\": 0.03063114553919882\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness\|hendrycksTest-computer_security\|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-conceptual_physics\|5\": {\n \"acc\": 0.3191489361702128,\n \"acc_stderr\": 0.030472973363380042,\n \"acc_norm\": 0.3191489361702128,\n \"acc_norm_stderr\": 0.030472973363380042\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438015,\n \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438015\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.2328042328042328,\n \"acc_stderr\": 0.021765961672154523,\n \"acc_norm\": 0.2328042328042328,\n \"acc_norm_stderr\": 0.021765961672154523\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.23015873015873015,\n \"acc_stderr\": 0.03764950879790606,\n \"acc_norm\": 0.23015873015873015,\n \"acc_norm_stderr\": 0.03764950879790606\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.22258064516129034,\n \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.22258064516129034,\n \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.23645320197044334,\n \"acc_stderr\": 0.029896114291733552,\n \"acc_norm\": 0.23645320197044334,\n \"acc_norm_stderr\": 0.029896114291733552\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.3151515151515151,\n \"acc_stderr\": 0.0362773057502241,\n \"acc_norm\": 0.3151515151515151,\n \"acc_norm_stderr\": 0.0362773057502241\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.19696969696969696,\n \"acc_stderr\": 0.02833560973246335,\n \"acc_norm\": 0.19696969696969696,\n \"acc_norm_stderr\": 0.02833560973246335\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.30569948186528495,\n \"acc_stderr\": 0.03324837939758159,\n \"acc_norm\": 0.30569948186528495,\n \"acc_norm_stderr\": 0.03324837939758159\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.2692307692307692,\n \"acc_stderr\": 0.022489389793654824,\n \"acc_norm\": 0.2692307692307692,\n \"acc_norm_stderr\": 0.022489389793654824\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.25555555555555554,\n 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[{"split": "2023_09_14T04_39_40.489809", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T04-39-40.489809.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T04-39-40.489809.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_14T04_39_40.489809", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T04-39-40.489809.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T04-39-40.489809.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_14T04_39_40.489809", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T04-39-40.489809.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T04-39-40.489809.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T04_39_40.489809", "path": ["results_2023-09-14T04-39-40.489809.parquet"]}, {"split": "latest", "path": ["results_2023-09-14T04-39-40.489809.parquet"]}]}]}	2023-09-14T03:40:54+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard. The dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-09-14T04:39:40.489809(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T04:39:40.489809(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T04:39:40.489809(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 21, 31, 169, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-14T04:39:40.489809(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
48714734801b2ef26c7baa1293577f43c748d4bc	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	hanho/test2	[ "license:openrail", "region:us" ]	2023-09-14T03:44:12+00:00	{"license": "openrail", "dataset_info": {"features": [{"name": "pokemon", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43, "num_examples": 2}], "download_size": 1215, "dataset_size": 43}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T03:51:35+00:00	[]	[]	TAGS #license-openrail #region-us	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#license-openrail #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 12, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#license-openrail #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
7b1544cda95be6c5da6d97c41f4f76b09e4b3930	# Dataset Card for "mmlu_binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/mmlu_binary	[ "region:us" ]	2023-09-14T03:47:27+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int32"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "validation", "num_bytes": 653717, "num_examples": 1218}, {"name": "test", "num_bytes": 5979564, "num_examples": 11526}], "download_size": 3456524, "dataset_size": 6633281}}	2023-09-19T04:12:16+00:00	[]	[]	TAGS #region-us	# Dataset Card for "mmlu_binary" More Information needed	[ "# Dataset Card for \"mmlu_binary\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu_binary\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"mmlu_binary\"\n\nMore Information needed" ]
f4dd3d7cf529f409aaf1d1cc3b170e6ac59e6615	# Dataset Card for "mmlu_chat_binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	atmallen/mmlu_chat_binary	[ "region:us" ]	2023-09-14T03:55:59+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-"}, {"split": "test", "path": "data/test-"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int32"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "validation", "num_bytes": 877546, "num_examples": 1218}, {"name": "test", "num_bytes": 8026608, "num_examples": 11526}], "download_size": 3732071, "dataset_size": 8904154}}	2023-09-19T04:12:20+00:00	[]	[]	TAGS #region-us	# Dataset Card for "mmlu_chat_binary" More Information needed	[ "# Dataset Card for \"mmlu_chat_binary\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu_chat_binary\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"mmlu_chat_binary\"\n\nMore Information needed" ]
5637eb112199d89afe3d4d088bc7537908e6c1d2	# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/Faradaylab/ARIA-70B-V2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Faradaylab/ARIA-70B-V2](https://huggingface.co/Faradaylab/ARIA-70B-V2) 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_Faradaylab__ARIA-70B-V2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T19:48:19.078343](https://huggingface.co/datasets/open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2/blob/main/results_2023-10-25T19-48-19.078343.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": 0.12174916107382551, "em_stderr": 0.0033487438315364985, "f1": 0.18035549496644265, "f1_stderr": 0.0034191831504093964, "acc": 0.552493667621485, "acc_stderr": 0.011672124185183144 }, "harness\|drop\|3": { "em": 0.12174916107382551, "em_stderr": 0.0033487438315364985, "f1": 0.18035549496644265, "f1_stderr": 0.0034191831504093964 }, "harness\|gsm8k\|5": { "acc": 0.2880970432145565, "acc_stderr": 0.012474469737197917 }, "harness\|winogrande\|5": { "acc": 0.8168902920284136, "acc_stderr": 0.01086977863316837 } } ``` ### 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]	open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2	[ "region:us" ]	2023-09-14T04:14:20+00:00	{"pretty_name": "Evaluation run of Faradaylab/ARIA-70B-V2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Faradaylab/ARIA-70B-V2](https://huggingface.co/Faradaylab/ARIA-70B-V2) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T19:48:19.078343](https://huggingface.co/datasets/open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2/blob/main/results_2023-10-25T19-48-19.078343.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\": 0.12174916107382551,\n \"em_stderr\": 0.0033487438315364985,\n \"f1\": 0.18035549496644265,\n \"f1_stderr\": 0.0034191831504093964,\n \"acc\": 0.552493667621485,\n \"acc_stderr\": 0.011672124185183144\n },\n \"harness\|drop\|3\": {\n \"em\": 0.12174916107382551,\n \"em_stderr\": 0.0033487438315364985,\n \"f1\": 0.18035549496644265,\n \"f1_stderr\": 0.0034191831504093964\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.2880970432145565,\n \"acc_stderr\": 0.012474469737197917\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.01086977863316837\n }\n}\n```", "repo_url": "https://huggingface.co/Faradaylab/ARIA-70B-V2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T05_14_04.383698", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T05-14-04.383698.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T05-14-04.383698.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T19_48_19.078343", "path": ["/details_harness\|drop\|3_2023-10-25T19-48-19.078343.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-25T19-48-19.078343.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T19_48_19.078343", "path": ["/details_harness\|gsm8k\|5_2023-10-25T19-48-19.078343.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-25T19-48-19.078343.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T05_14_04.383698", "path": ["/details_harness\|hellaswag\|10_2023-09-14T05-14-04.383698.parquet"]}, {"split": "latest", "path": 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### Dataset Summary Dataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-25T19:48:19.078343(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T19:48:19.078343(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T19:48:19.078343(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 19, 31, 167, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T19:48:19.078343(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
6d7ff385cc25fa66aa76870d3eec3ff23686b9a4	# Bangumi Image Base of Watashi No Yuri Wa Oshigoto Desu! This is the image base of bangumi Watashi no Yuri wa Oshigoto Desu!, we detected 31 characters, 3255 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 221 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 10 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 15 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 12 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 12 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 10 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 23 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 14 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 26 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 22 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 416 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 142 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 31 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 5 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| N/A \| N/A \| N/A \| \| 14 \| 420 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 63 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 23 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 970 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 87 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 364 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 60 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 21 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 36 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| 23 \| 11 \| [Download](23/dataset.zip) \| ![preview 1](23/preview_1.png) \| ![preview 2](23/preview_2.png) \| ![preview 3](23/preview_3.png) \| ![preview 4](23/preview_4.png) \| ![preview 5](23/preview_5.png) \| ![preview 6](23/preview_6.png) \| ![preview 7](23/preview_7.png) \| ![preview 8](23/preview_8.png) \| \| 24 \| 12 \| [Download](24/dataset.zip) \| ![preview 1](24/preview_1.png) \| ![preview 2](24/preview_2.png) \| ![preview 3](24/preview_3.png) \| ![preview 4](24/preview_4.png) \| ![preview 5](24/preview_5.png) \| ![preview 6](24/preview_6.png) \| ![preview 7](24/preview_7.png) \| ![preview 8](24/preview_8.png) \| \| 25 \| 13 \| [Download](25/dataset.zip) \| ![preview 1](25/preview_1.png) \| ![preview 2](25/preview_2.png) \| ![preview 3](25/preview_3.png) \| ![preview 4](25/preview_4.png) \| ![preview 5](25/preview_5.png) \| ![preview 6](25/preview_6.png) \| ![preview 7](25/preview_7.png) \| ![preview 8](25/preview_8.png) \| \| 26 \| 10 \| [Download](26/dataset.zip) \| ![preview 1](26/preview_1.png) \| ![preview 2](26/preview_2.png) \| ![preview 3](26/preview_3.png) \| ![preview 4](26/preview_4.png) \| ![preview 5](26/preview_5.png) \| ![preview 6](26/preview_6.png) \| ![preview 7](26/preview_7.png) \| ![preview 8](26/preview_8.png) \| \| 27 \| 24 \| [Download](27/dataset.zip) \| ![preview 1](27/preview_1.png) \| ![preview 2](27/preview_2.png) \| ![preview 3](27/preview_3.png) \| ![preview 4](27/preview_4.png) \| ![preview 5](27/preview_5.png) \| ![preview 6](27/preview_6.png) \| ![preview 7](27/preview_7.png) \| ![preview 8](27/preview_8.png) \| \| 28 \| 29 \| [Download](28/dataset.zip) \| ![preview 1](28/preview_1.png) \| ![preview 2](28/preview_2.png) \| ![preview 3](28/preview_3.png) \| ![preview 4](28/preview_4.png) \| ![preview 5](28/preview_5.png) \| ![preview 6](28/preview_6.png) \| ![preview 7](28/preview_7.png) \| ![preview 8](28/preview_8.png) \| \| 29 \| 13 \| [Download](29/dataset.zip) \| ![preview 1](29/preview_1.png) \| ![preview 2](29/preview_2.png) \| ![preview 3](29/preview_3.png) \| ![preview 4](29/preview_4.png) \| ![preview 5](29/preview_5.png) \| ![preview 6](29/preview_6.png) \| ![preview 7](29/preview_7.png) \| ![preview 8](29/preview_8.png) \| \| noise \| 140 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|	BangumiBase/watashinoyuriwaoshigotodesu	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-09-14T04:16:54+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-09-29T06:26:06+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Watashi No Yuri Wa Oshigoto Desu! ======================================================= This is the image base of bangumi Watashi no Yuri wa Oshigoto Desu!, we detected 31 characters, 3255 images in total. The full dataset is here. Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
af4ab2bf94b96e527aefc82fcacfd4d2817878dd	# Dataset Card for "fonts_ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	yuanmei424/fonts_ds	[ "region:us" ]	2023-09-14T04:18:19+00:00	{"dataset_info": {"features": [{"name": "edit_prompt", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 83621453418.25, "num_examples": 19837823}], "download_size": 0, "dataset_size": 83621453418.25}}	2023-09-27T21:50:32+00:00	[]	[]	TAGS #region-us	# Dataset Card for "fonts_ds" More Information needed	[ "# Dataset Card for \"fonts_ds\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"fonts_ds\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"fonts_ds\"\n\nMore Information needed" ]
56ad86b8331b5b138948994eaa2d5c2001612af2	# Dataset of aiba_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls) This is the dataset of aiba_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls), containing 379 images and their tags. The core tags of this character are `short_hair, brown_eyes, breasts, blonde_hair, bangs, medium_breasts, brown_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 \| 379 \| 427.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 379 \| 266.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 856 \| 537.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 379 \| 385.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 856 \| 725.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_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/aiba_yumi_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 \| 23 \| ![](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, blush, open_mouth, dress, looking_at_viewer, cleavage, necklace, :d, flower \| \| 1 \| 7 \| ![](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, collarbone, hair_flower, necklace, smile, solo, wrist_cuffs, bare_shoulders, blush, looking_at_viewer, red_flower, strapless_dress, earrings, petals, white_background, cleavage, standing, white_dress, bow, butterfly_hair_ornament, closed_mouth \| \| 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, bracelet, earrings, hair_flower, looking_at_viewer, open_mouth, smile, blush, collarbone, floral_print, navel, necklace, solo, belt, cleavage, one_eye_closed, water, bikini_skirt, day, frills, midriff, nail_polish, outdoors, petals, sandals, sky \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, midriff, navel, open_mouth, solo, hair_flower, rabbit_ears, rabbit_tail, :d, bare_shoulders, bow, earrings, microphone, skirt, star_(symbol), wrist_cuffs \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| blush, cleavage, collarbone, frilled_bikini, looking_at_viewer, 1girl, :d, navel, open_mouth, outdoors, blue_sky, day, floral_print, ocean, side-tie_bikini_bottom, solo, cloud, large_breasts, pink_bikini, water \| \| 5 \| 11 \| ![](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, playboy_bunny, rabbit_ears, solo, detached_collar, rabbit_tail, wrist_cuffs, black_leotard, looking_at_viewer, fake_animal_ears, strapless_leotard, black_pantyhose, cleavage, simple_background, ass, bare_shoulders, black_bowtie, blush, cowboy_shot, white_background \| \| 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, nurse_cap, short_sleeves, solo, :d, angel_wings, blush, feathered_wings, looking_at_viewer, open_mouth, white_wings, heart, white_dress, holding_syringe, star_(symbol), white_gloves, wrist_cuffs \| \| 7 \| 12 \| ![](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) \| 1boy, 1girl, blush, hetero, penis, nipples, solo_focus, vaginal, open_mouth, bar_censor, spread_legs, sweat, girl_on_top, navel, sex_from_behind, smile, straddling, breast_grab, cum_in_pussy, grabbing_from_behind, large_breasts, nude, clothed_sex, collarbone, jewelry, open_shirt, skirt \| \| 8 \| 5 \| ![](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, blush, brown_skirt, collared_shirt, plaid_skirt, pleated_skirt, solo, white_shirt, long_sleeves, looking_at_viewer, school_uniform, black_bowtie, dress_shirt, open_mouth, smile, ;d, black_socks, blurry, bra_visible_through_clothes, closed_mouth, day, hose, indoors, kneehighs, one_eye_closed, outdoors, see-through, standing, water, wet_shirt \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| blush \| open_mouth \| dress \| looking_at_viewer \| cleavage \| necklace \| :d \| flower \| collarbone \| hair_flower \| smile \| wrist_cuffs \| bare_shoulders \| red_flower \| strapless_dress \| earrings \| petals \| white_background \| standing \| white_dress \| bow \| butterfly_hair_ornament \| closed_mouth \| bracelet \| floral_print \| navel \| belt \| one_eye_closed \| water \| bikini_skirt \| day \| frills \| midriff \| nail_polish \| outdoors \| sandals \| sky \| rabbit_ears \| rabbit_tail \| microphone \| skirt \| star_(symbol) \| frilled_bikini \| blue_sky \| ocean \| side-tie_bikini_bottom \| cloud \| large_breasts \| pink_bikini \| playboy_bunny \| detached_collar \| black_leotard \| fake_animal_ears \| strapless_leotard \| black_pantyhose \| simple_background \| ass \| black_bowtie \| cowboy_shot \| nurse_cap \| short_sleeves \| angel_wings \| feathered_wings \| white_wings \| heart \| holding_syringe \| white_gloves \| 1boy \| hetero \| penis \| nipples \| solo_focus \| vaginal \| bar_censor \| spread_legs \| sweat \| girl_on_top \| sex_from_behind \| straddling \| breast_grab \| cum_in_pussy \| grabbing_from_behind \| nude \| clothed_sex \| jewelry \| open_shirt \| brown_skirt \| collared_shirt \| plaid_skirt \| pleated_skirt \| white_shirt \| long_sleeves \| school_uniform \| dress_shirt \| ;d \| black_socks \| blurry \| bra_visible_through_clothes \| hose \| indoors \| kneehighs \| see-through \| wet_shirt \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------\|:-------------\|:--------\|:--------------------\|:-----------\|:-----------\|:-----\|:---------\|:-------------\|:--------------\|:--------\|:--------------\|:-----------------\|:-------------\|:------------------\|:-----------\|:---------\|:-------------------\|:-----------\|:--------------\|:------\|:--------------------------\|:---------------\|:-----------\|:---------------\|:--------\|:-------\|:-----------------\|:--------\|:---------------\|:------\|:---------\|:----------\|:--------------\|:-----------\|:----------\|:------\|:--------------\|:--------------\|:-------------\|:--------\|:----------------\|:-----------------\|:-----------\|:--------\|:-------------------------\|:--------\|:----------------\|:--------------\|:----------------\|:------------------\|:----------------\|:-------------------\|:--------------------\|:------------------\|:--------------------\|:------\|:---------------\|:--------------\|:------------\|:----------------\|:--------------\|:------------------\|:--------------\|:--------\|:------------------\|:---------------\|:-------\|:---------\|:--------\|:----------\|:-------------\|:----------\|:-------------\|:--------------\|:--------\|:--------------\|:------------------\|:-------------\|:--------------\|:---------------\|:-----------------------\|:-------\|:--------------\|:----------\|:-------------\|:--------------\|:-----------------\|:--------------\|:----------------\|:--------------\|:---------------\|:-----------------\|:--------------\|:-----\|:--------------\|:---------\|:------------------------------\|:-------\|:----------\|:------------\|:--------------\|:------------\| \| 0 \| 23 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| X \| \| \| \| \| X \| \| \| X \| \| X \| X \| \| \| X \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 11 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| \| \| X \| X \| \| \| \| \| \| \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 12 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| X \| X \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 5 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| X \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/aiba_yumi_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T04:25:18+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:33:51+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of aiba\_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls) ============================================================= This is the dataset of aiba\_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls), containing 379 images and their tags. The core tags of this character are 'short\_hair, brown\_eyes, breasts, blonde\_hair, bangs, medium\_breasts, brown\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
bedb4cba9abd6b3eb84d3d3d1e906c439ee49f97	# Dataset of senkawa_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls) This is the dataset of senkawa_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls), containing 291 images and their tags. The core tags of this character are `brown_hair, braid, long_hair, single_braid, hair_over_shoulder, breasts, scrunchie, brown_eyes, hair_scrunchie, hair_ornament, 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 \| 291 \| 278.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 291 \| 182.79 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 641 \| 363.25 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 291 \| 252.68 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 641 \| 479.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_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/senkawa_chihiro_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 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, long_sleeves, solo, black_skirt, blush, collared_shirt, green_jacket, looking_at_viewer, pencil_skirt, red_scrunchie, white_shirt, yellow_necktie, black_pantyhose, office_lady, simple_background, white_background, :d, miniskirt, open_mouth, closed_mouth, dress_shirt, holding, name_tag \| \| 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, necktie, smile, solo, blush, open_mouth, looking_at_viewer \| \| 2 \| 14 \| ![](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, navel, solo, smile, green_bikini, looking_at_viewer, open_mouth, large_breasts, cleavage, frilled_bikini, side-tie_bikini_bottom \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, cleavage, medium_breasts, solo, looking_at_viewer, green_bra, navel, smile, black_thighhighs, green_panties, open_shirt, sitting \| \| 4 \| 5 \| ![](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, black_pantyhose, black_skirt, bralines, office_lady, pencil_skirt, solo, white_shirt, ass, bra_visible_through_clothes, from_behind, long_sleeves, closed_eyes, high-waist_skirt, indoors, pantylines, see-through, facing_away, handbag, lanyard, sleeping \| \| 5 \| 8 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| 1girl, detached_collar, playboy_bunny, rabbit_ears, medium_breasts, solo, wrist_cuffs, blush, bowtie, cleavage, fishnet_pantyhose, looking_at_viewer, black_pantyhose, rabbit_tail, strapless_leotard, bare_shoulders, black_leotard, fake_animal_ears, open_mouth, white_background, simple_background, smile \| \| 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) \| bare_shoulders, collarbone, green_dress, strapless_dress, 1girl, bare_arms, blush, looking_at_viewer, pearl_necklace, solo, bow, cleavage, hands_up, medium_breasts, open_mouth, orange_eyes, red_scrunchie, white_background, :d, bead_necklace, closed_mouth, cowboy_shot, gradient_background, hair_between_eyes, large_breasts, own_hands_together, purple_rose, sash, sparkle, standing, upper_body \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| long_sleeves \| solo \| black_skirt \| blush \| collared_shirt \| green_jacket \| looking_at_viewer \| pencil_skirt \| red_scrunchie \| white_shirt \| yellow_necktie \| black_pantyhose \| office_lady \| simple_background \| white_background \| :d \| miniskirt \| open_mouth \| closed_mouth \| dress_shirt \| holding \| name_tag \| necktie \| smile \| navel \| green_bikini \| large_breasts \| cleavage \| frilled_bikini \| side-tie_bikini_bottom \| medium_breasts \| green_bra \| black_thighhighs \| green_panties \| open_shirt \| sitting \| bralines \| ass \| bra_visible_through_clothes \| from_behind \| closed_eyes \| high-waist_skirt \| indoors \| pantylines \| see-through \| facing_away \| handbag \| lanyard \| sleeping \| detached_collar \| playboy_bunny \| rabbit_ears \| wrist_cuffs \| bowtie \| fishnet_pantyhose \| rabbit_tail \| strapless_leotard \| bare_shoulders \| black_leotard \| fake_animal_ears \| collarbone \| green_dress \| strapless_dress \| bare_arms \| pearl_necklace \| bow \| hands_up \| orange_eyes \| bead_necklace \| cowboy_shot \| gradient_background \| hair_between_eyes \| own_hands_together \| purple_rose \| sash \| sparkle \| standing \| upper_body \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-------\|:--------------\|:--------\|:-----------------\|:---------------\|:--------------------\|:---------------\|:----------------\|:--------------\|:-----------------\|:------------------\|:--------------\|:--------------------\|:-------------------\|:-----\|:------------\|:-------------\|:---------------\|:--------------\|:----------\|:-----------\|:----------\|:--------\|:--------\|:---------------\|:----------------\|:-----------\|:-----------------\|:-------------------------\|:-----------------\|:------------\|:-------------------\|:----------------\|:-------------\|:----------\|:-----------\|:------\|:------------------------------\|:--------------\|:--------------\|:-------------------\|:----------\|:-------------\|:--------------\|:--------------\|:----------\|:----------\|:-----------\|:------------------\|:----------------\|:--------------\|:--------------\|:---------\|:--------------------\|:--------------\|:--------------------\|:-----------------\|:----------------\|:-------------------\|:-------------\|:--------------\|:------------------\|:------------\|:-----------------\|:------\|:-----------\|:--------------\|:----------------\|:--------------\|:----------------------\|:--------------------\|:---------------------\|:--------------\|:-------\|:----------\|:-----------\|:-------------\| \| 0 \| 10 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 14 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| 8 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| X \| \| X \| \| \| X \| \| X \| \| \| \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/senkawa_chihiro_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T04:53:53+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:37:55+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of senkawa\_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls) =========================================================================== This is the dataset of senkawa\_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls), containing 291 images and their tags. The core tags of this character are 'brown\_hair, braid, long\_hair, single\_braid, hair\_over\_shoulder, breasts, scrunchie, brown\_eyes, hair\_scrunchie, hair\_ornament, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
40bf935773035a84787afebbfae4834dc52fd9ae	# Dataset Card for "art_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Falah/art_prompts	[ "region:us" ]	2023-09-14T05:31:13+00:00	{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 205606, "num_examples": 1000}], "download_size": 32002, "dataset_size": 205606}}	2023-09-14T05:31:14+00:00	[]	[]	TAGS #region-us	# Dataset Card for "art_prompts" More Information needed	[ "# Dataset Card for \"art_prompts\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"art_prompts\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"art_prompts\"\n\nMore Information needed" ]
ba5d3daf526ea0847f3a737744aeb7e9107b5912	# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B ## Dataset Description - Homepage: - Repository: https://huggingface.co/AIDC-ai-business/Marcoroni-70B - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B](https://huggingface.co/AIDC-ai-business/Marcoroni-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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_AIDC-ai-business__Marcoroni-70B", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-19T02:16:50.789886](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B/blob/main/results_2023-09-19T02-16-50.789886.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": 0.23992448312110085, "acc_stderr": 0.031078389352549952, "acc_norm": 0.24054395860756556, "acc_norm_stderr": 0.03108725267744147, "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness\|arc:challenge\|25": { "acc": 0.24744027303754265, "acc_stderr": 0.012610352663292673, "acc_norm": 0.2790102389078498, "acc_norm_stderr": 0.013106784883601346 }, "harness\|hellaswag\|10": { "acc": 0.2621987651862179, "acc_stderr": 0.004389312748012152, "acc_norm": 0.2671778530173272, "acc_norm_stderr": 0.004415816696303084 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.19, "acc_stderr": 0.03942772444036624, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036624 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.18518518518518517, "acc_stderr": 0.0335567721631314, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.0335567721631314 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.21710526315789475, "acc_stderr": 0.033550453048829226, "acc_norm": 0.21710526315789475, "acc_norm_stderr": 0.033550453048829226 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.20754716981132076, "acc_stderr": 0.02495991802891127, "acc_norm": 0.20754716981132076, "acc_norm_stderr": 0.02495991802891127 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.19, "acc_stderr": 0.039427724440366234, "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.18497109826589594, "acc_stderr": 0.029605623981771204, "acc_norm": 0.18497109826589594, "acc_norm_stderr": 0.029605623981771204 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.2723404255319149, "acc_stderr": 0.029101290698386705, "acc_norm": 0.2723404255319149, "acc_norm_stderr": 0.029101290698386705 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.25517241379310346, "acc_stderr": 0.03632984052707842, "acc_norm": 0.25517241379310346, "acc_norm_stderr": 0.03632984052707842 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.24603174603174602, "acc_stderr": 0.022182037202948365, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.022182037202948365 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.2777777777777778, "acc_stderr": 0.04006168083848876, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.04006168083848876 }, "harness\|hendrycksTest-global_facts\|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness\|hendrycksTest-high_school_biology\|5": { "acc": 0.2, "acc_stderr": 0.022755204959542932, "acc_norm": 0.2, "acc_norm_stderr": 0.022755204959542932 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.22167487684729065, "acc_stderr": 0.029225575892489607, "acc_norm": 0.22167487684729065, "acc_norm_stderr": 0.029225575892489607 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.21, "acc_stderr": 0.04093601807403326, "acc_norm": 0.21, "acc_norm_stderr": 0.04093601807403326 }, "harness\|hendrycksTest-high_school_european_history\|5": { "acc": 0.2545454545454545, "acc_stderr": 0.0340150671524904, "acc_norm": 0.2545454545454545, "acc_norm_stderr": 0.0340150671524904 }, "harness\|hendrycksTest-high_school_geography\|5": { "acc": 0.20707070707070707, "acc_stderr": 0.02886977846026705, "acc_norm": 0.20707070707070707, "acc_norm_stderr": 0.02886977846026705 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.2849740932642487, "acc_stderr": 0.03257714077709661, "acc_norm": 0.2849740932642487, "acc_norm_stderr": 0.03257714077709661 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.23333333333333334, "acc_stderr": 0.021444547301560486, "acc_norm": 0.23333333333333334, "acc_norm_stderr": 0.021444547301560486 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.22592592592592592, "acc_stderr": 0.025497532639609542, "acc_norm": 0.22592592592592592, "acc_norm_stderr": 0.025497532639609542 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.19747899159663865, "acc_stderr": 0.025859164122051467, "acc_norm": 0.19747899159663865, "acc_norm_stderr": 0.025859164122051467 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.23841059602649006, "acc_stderr": 0.0347918557259966, "acc_norm": 0.23841059602649006, "acc_norm_stderr": 0.0347918557259966 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.21651376146788992, "acc_stderr": 0.017658710594443145, "acc_norm": 0.21651376146788992, "acc_norm_stderr": 0.017658710594443145 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.1712962962962963, "acc_stderr": 0.025695341643824685, "acc_norm": 0.1712962962962963, "acc_norm_stderr": 0.025695341643824685 }, "harness\|hendrycksTest-high_school_us_history\|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness\|hendrycksTest-high_school_world_history\|5": { "acc": 0.25738396624472576, "acc_stderr": 0.028458820991460302, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.028458820991460302 }, "harness\|hendrycksTest-human_aging\|5": { "acc": 0.2914798206278027, "acc_stderr": 0.030500283176545902, "acc_norm": 0.2914798206278027, "acc_norm_stderr": 0.030500283176545902 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.24427480916030533, "acc_stderr": 0.037683359597287434, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.037683359597287434 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.2809917355371901, "acc_stderr": 0.04103203830514511, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.04103203830514511 }, "harness\|hendrycksTest-jurisprudence\|5": { "acc": 0.25925925925925924, "acc_stderr": 0.042365112580946336, "acc_norm": 0.25925925925925924, "acc_norm_stderr": 0.042365112580946336 }, "harness\|hendrycksTest-logical_fallacies\|5": { "acc": 0.22699386503067484, "acc_stderr": 0.032910995786157686, "acc_norm": 0.22699386503067484, "acc_norm_stderr": 0.032910995786157686 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.2857142857142857, "acc_stderr": 0.04287858751340456, "acc_norm": 0.2857142857142857, "acc_norm_stderr": 0.04287858751340456 }, "harness\|hendrycksTest-management\|5": { "acc": 0.1941747572815534, "acc_stderr": 0.03916667762822584, "acc_norm": 0.1941747572815534, "acc_norm_stderr": 0.03916667762822584 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.25213675213675213, "acc_stderr": 0.02844796547623101, "acc_norm": 0.25213675213675213, "acc_norm_stderr": 0.02844796547623101 }, "harness\|hendrycksTest-medical_genetics\|5": { "acc": 0.27, "acc_stderr": 0.0446196043338474, "acc_norm": 0.27, "acc_norm_stderr": 0.0446196043338474 }, "harness\|hendrycksTest-miscellaneous\|5": { "acc": 0.26181353767560667, "acc_stderr": 0.015720838678445266, "acc_norm": 0.26181353767560667, "acc_norm_stderr": 0.015720838678445266 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.25251396648044694, "acc_stderr": 0.014530330201468645, "acc_norm": 0.25251396648044694, "acc_norm_stderr": 0.014530330201468645 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.2647058823529412, "acc_stderr": 0.025261691219729487, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.025261691219729487 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.2508038585209003, "acc_stderr": 0.024619771956697165, "acc_norm": 0.2508038585209003, "acc_norm_stderr": 0.024619771956697165 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.22530864197530864, "acc_stderr": 0.02324620264781975, "acc_norm": 0.22530864197530864, "acc_norm_stderr": 0.02324620264781975 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.2765957446808511, "acc_stderr": 0.026684564340461004, "acc_norm": 0.2765957446808511, "acc_norm_stderr": 0.026684564340461004 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.25358539765319427, "acc_stderr": 0.011111715336101136, "acc_norm": 0.25358539765319427, "acc_norm_stderr": 0.011111715336101136 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.18382352941176472, "acc_stderr": 0.02352924218519311, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.02352924218519311 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.24673202614379086, "acc_stderr": 0.017440820367402493, "acc_norm": 0.24673202614379086, "acc_norm_stderr": 0.017440820367402493 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.19090909090909092, "acc_stderr": 0.03764425585984927, "acc_norm": 0.19090909090909092, "acc_norm_stderr": 0.03764425585984927 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.23383084577114427, "acc_stderr": 0.029929415408348384, "acc_norm": 0.23383084577114427, "acc_norm_stderr": 0.029929415408348384 }, "harness\|hendrycksTest-us_foreign_policy\|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness\|hendrycksTest-virology\|5": { "acc": 0.25301204819277107, "acc_stderr": 0.03384429155233134, "acc_norm": 0.25301204819277107, "acc_norm_stderr": 0.03384429155233134 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.26900584795321636, "acc_stderr": 0.0340105262010409, "acc_norm": 0.26900584795321636, "acc_norm_stderr": 0.0340105262010409 }, "harness\|truthfulqa:mc\|0": { "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN } } ``` ### 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]	open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B	[ "region:us" ]	2023-09-14T05:34:49+00:00	{"pretty_name": "Evaluation run of AIDC-ai-business/Marcoroni-70B", "dataset_summary": "Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B](https://huggingface.co/AIDC-ai-business/Marcoroni-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-19T02:16:50.789886](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B/blob/main/results_2023-09-19T02-16-50.789886.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\": 0.23992448312110085,\n \"acc_stderr\": 0.031078389352549952,\n \"acc_norm\": 0.24054395860756556,\n \"acc_norm_stderr\": 0.03108725267744147,\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.24744027303754265,\n \"acc_stderr\": 0.012610352663292673,\n \"acc_norm\": 0.2790102389078498,\n \"acc_norm_stderr\": 0.013106784883601346\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.2621987651862179,\n \"acc_stderr\": 0.004389312748012152,\n \"acc_norm\": 0.2671778530173272,\n \"acc_norm_stderr\": 0.004415816696303084\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.0335567721631314,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.0335567721631314\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.21710526315789475,\n \"acc_stderr\": 0.033550453048829226,\n \"acc_norm\": 0.21710526315789475,\n \"acc_norm_stderr\": 0.033550453048829226\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.20754716981132076,\n \"acc_stderr\": 0.02495991802891127,\n \"acc_norm\": 0.20754716981132076,\n \"acc_norm_stderr\": 0.02495991802891127\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.2638888888888889,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.029605623981771204,\n \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.029605623981771204\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.029101290698386705,\n \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.029101290698386705\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.25517241379310346,\n \"acc_stderr\": 0.03632984052707842,\n \"acc_norm\": 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.24603174603174602,\n \"acc_stderr\": 0.022182037202948365,\n \"acc_norm\": 0.24603174603174602,\n \"acc_norm_stderr\": 0.022182037202948365\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.04006168083848876,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.04006168083848876\n },\n \"harness\|hendrycksTest-global_facts\|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness\|hendrycksTest-high_school_biology\|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.022755204959542932,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.022755204959542932\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.22167487684729065,\n \"acc_stderr\": 0.029225575892489607,\n \"acc_norm\": 0.22167487684729065,\n \"acc_norm_stderr\": 0.029225575892489607\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness\|hendrycksTest-high_school_european_history\|5\": {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.0340150671524904,\n \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.0340150671524904\n },\n \"harness\|hendrycksTest-high_school_geography\|5\": {\n \"acc\": 0.20707070707070707,\n \"acc_stderr\": 0.02886977846026705,\n \"acc_norm\": 0.20707070707070707,\n \"acc_norm_stderr\": 0.02886977846026705\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.2849740932642487,\n \"acc_stderr\": 0.03257714077709661,\n \"acc_norm\": 0.2849740932642487,\n \"acc_norm_stderr\": 0.03257714077709661\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.23333333333333334,\n \"acc_stderr\": 0.021444547301560486,\n \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.021444547301560486\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.19747899159663865,\n \"acc_stderr\": 0.025859164122051467,\n \"acc_norm\": 0.19747899159663865,\n \"acc_norm_stderr\": 0.025859164122051467\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.23841059602649006,\n \"acc_stderr\": 0.0347918557259966,\n \"acc_norm\": 0.23841059602649006,\n \"acc_norm_stderr\": 0.0347918557259966\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.21651376146788992,\n \"acc_stderr\": 0.017658710594443145,\n \"acc_norm\": 0.21651376146788992,\n \"acc_norm_stderr\": 0.017658710594443145\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.1712962962962963,\n \"acc_stderr\": 0.025695341643824685,\n \"acc_norm\": 0.1712962962962963,\n \"acc_norm_stderr\": 0.025695341643824685\n },\n \"harness\|hendrycksTest-high_school_us_history\|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness\|hendrycksTest-high_school_world_history\|5\": {\n \"acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460302,\n \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460302\n },\n \"harness\|hendrycksTest-human_aging\|5\": {\n \"acc\": 0.2914798206278027,\n \"acc_stderr\": 0.030500283176545902,\n \"acc_norm\": 0.2914798206278027,\n \"acc_norm_stderr\": 0.030500283176545902\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.037683359597287434,\n \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.037683359597287434\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.2809917355371901,\n \"acc_stderr\": 0.04103203830514511,\n \"acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.04103203830514511\n },\n \"harness\|hendrycksTest-jurisprudence\|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness\|hendrycksTest-logical_fallacies\|5\": {\n \"acc\": 0.22699386503067484,\n \"acc_stderr\": 0.032910995786157686,\n \"acc_norm\": 0.22699386503067484,\n \"acc_norm_stderr\": 0.032910995786157686\n },\n \"harness\|hendrycksTest-machine_learning\|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness\|hendrycksTest-management\|5\": {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.03916667762822584,\n \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.03916667762822584\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.25213675213675213,\n \"acc_stderr\": 0.02844796547623101,\n \"acc_norm\": 0.25213675213675213,\n \"acc_norm_stderr\": 0.02844796547623101\n },\n \"harness\|hendrycksTest-medical_genetics\|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness\|hendrycksTest-miscellaneous\|5\": {\n \"acc\": 0.26181353767560667,\n \"acc_stderr\": 0.015720838678445266,\n \"acc_norm\": 0.26181353767560667,\n \"acc_norm_stderr\": 0.015720838678445266\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.24566473988439305,\n \"acc_stderr\": 0.02317629820399201,\n \"acc_norm\": 0.24566473988439305,\n \"acc_norm_stderr\": 0.02317629820399201\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.25251396648044694,\n \"acc_stderr\": 0.014530330201468645,\n \"acc_norm\": 0.25251396648044694,\n \"acc_norm_stderr\": 0.014530330201468645\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.025261691219729487,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.025261691219729487\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.2508038585209003,\n \"acc_stderr\": 0.024619771956697165,\n \"acc_norm\": 0.2508038585209003,\n \"acc_norm_stderr\": 0.024619771956697165\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.22530864197530864,\n \"acc_stderr\": 0.02324620264781975,\n \"acc_norm\": 0.22530864197530864,\n \"acc_norm_stderr\": 0.02324620264781975\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.2765957446808511,\n \"acc_stderr\": 0.026684564340461004,\n \"acc_norm\": 0.2765957446808511,\n \"acc_norm_stderr\": 0.026684564340461004\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.25358539765319427,\n \"acc_stderr\": 0.011111715336101136,\n \"acc_norm\": 0.25358539765319427,\n \"acc_norm_stderr\": 0.011111715336101136\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.02352924218519311,\n \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.02352924218519311\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.24673202614379086,\n \"acc_stderr\": 0.017440820367402493,\n \"acc_norm\": 0.24673202614379086,\n \"acc_norm_stderr\": 0.017440820367402493\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.19090909090909092,\n \"acc_stderr\": 0.03764425585984927,\n \"acc_norm\": 0.19090909090909092,\n \"acc_norm_stderr\": 0.03764425585984927\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.23383084577114427,\n \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.23383084577114427,\n \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness\|hendrycksTest-us_foreign_policy\|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-virology\|5\": {\n \"acc\": 0.25301204819277107,\n \"acc_stderr\": 0.03384429155233134,\n \"acc_norm\": 0.25301204819277107,\n \"acc_norm_stderr\": 0.03384429155233134\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.26900584795321636,\n \"acc_stderr\": 0.0340105262010409,\n \"acc_norm\": 0.26900584795321636,\n \"acc_norm_stderr\": 0.0340105262010409\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n }\n}\n```", "repo_url": "https://huggingface.co/AIDC-ai-business/Marcoroni-70B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T06_34_33.473104", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T06-34-33.473104.parquet"]}, {"split": "2023_09_14T19_48_28.878729", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T19-48-28.878729.parquet"]}, {"split": "2023_09_19T01_46_19.012527", "path": ["/details_harness\|arc:challenge\|25_2023-09-19T01-46-19.012527.parquet"]}, {"split": "2023_09_19T02_16_50.789886", "path": ["/details_harness\|arc:challenge\|25_2023-09-19T02-16-50.789886.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-19T02-16-50.789886.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T06_34_33.473104", "path": ["/details_harness\|hellaswag\|10_2023-09-14T06-34-33.473104.parquet"]}, {"split": "2023_09_14T19_48_28.878729", "path": ["/details_harness\|hellaswag\|10_2023-09-14T19-48-28.878729.parquet"]}, {"split": "2023_09_19T01_46_19.012527", "path": ["/details_harness\|hellaswag\|10_2023-09-19T01-46-19.012527.parquet"]}, {"split": "2023_09_19T02_16_50.789886", "path": ["/details_harness\|hellaswag\|10_2023-09-19T02-16-50.789886.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-19T02-16-50.789886.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T06_34_33.473104", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T06-34-33.473104.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T06-34-33.473104.parquet", 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["/details_harness\|hendrycksTest-virology\|5_2023-09-19T02-16-50.789886.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_14T06_34_33.473104", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T06-34-33.473104.parquet"]}, {"split": "2023_09_14T19_48_28.878729", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T19-48-28.878729.parquet"]}, {"split": "2023_09_19T01_46_19.012527", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-19T01-46-19.012527.parquet"]}, {"split": "2023_09_19T02_16_50.789886", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-19T02-16-50.789886.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-19T02-16-50.789886.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_14T06_34_33.473104", "path": 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"latest", "path": ["results_2023-09-19T02-16-50.789886.parquet"]}]}]}	2023-09-19T01:18:15+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard. The dataset is composed of 61 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-09-19T02:16:50.789886(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-19T02:16:50.789886(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-19T02:16:50.789886(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 21, 31, 169, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-19T02:16:50.789886(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
191b42bf54afb83a552cb0fcf9e7deed68c9645a	# Dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls) This is the dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are `dark-skinned_female, dark_skin, short_hair, purple_eyes, breasts, black_hair, green_hair, large_breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 489 \| 487.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 489 \| 328.81 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1086 \| 654.61 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 489 \| 447.04 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1086 \| 855.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/natalia_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/natalia_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 \| 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, earrings, midriff, open_mouth, solo, bracelet, card_parody, character_name, star_(symbol), sun_symbol, black_thighhighs, cleavage, dancer, navel, :d, bow, panties \| \| 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) \| hair_flower, smile, 1girl, solo, cleavage, elbow_gloves, blush, earrings, open_mouth, wedding_dress, bare_shoulders \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, necklace, solo, white_dress, bare_shoulders, blush, looking_at_viewer, open_mouth, cleavage, :d, bracelet \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, elbow_gloves, looking_at_viewer, solo, blush, bridal_veil, rose, wedding_dress, white_gloves, bouquet, open_mouth, :d, cleavage, earrings, strapless \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, collarbone, looking_at_viewer, solo, white_shirt, short_sleeves, upper_body, wet_shirt, simple_background, white_background, :d, bangs, black_skirt, bra, cleavage, neckerchief, open_mouth, pleated_skirt, sailor_collar, see-through, serafuku \| \| 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, looking_at_viewer, nipples, nude, smile, solo, collarbone, doujin_cover, content_rating, open_mouth, simple_background, upper_body \| \| 6 \| 9 \| ![](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) \| blush, looking_at_viewer, 1girl, solo, underwear_only, white_background, cleavage, smile, white_panties, navel, simple_background, white_bra, open_mouth \| \| 7 \| 17 \| ![](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) \| 1boy, 1girl, blush, hetero, nipples, solo_focus, open_mouth, sex, sweat, censored, girl_on_top, nude, vaginal, penis, navel, cowgirl_position, smile, pussy \| \| 8 \| 12 \| ![](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) \| playboy_bunny, rabbit_ears, smile, 1girl, detached_collar, fake_animal_ears, looking_at_viewer, rabbit_tail, solo, fake_tail, strapless_leotard, wrist_cuffs, black_leotard, blush, simple_background, white_background, bare_shoulders, bowtie, cleavage, cowboy_shot, armpits, arms_up, brown_pantyhose, fishnet_pantyhose, open_mouth \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| earrings \| midriff \| open_mouth \| solo \| bracelet \| card_parody \| character_name \| star_(symbol) \| sun_symbol \| black_thighhighs \| cleavage \| dancer \| navel \| :d \| bow \| panties \| hair_flower \| smile \| elbow_gloves \| blush \| wedding_dress \| bare_shoulders \| necklace \| white_dress \| looking_at_viewer \| bridal_veil \| rose \| white_gloves \| bouquet \| strapless \| collarbone \| white_shirt \| short_sleeves \| upper_body \| wet_shirt \| simple_background \| white_background \| bangs \| black_skirt \| bra \| neckerchief \| pleated_skirt \| sailor_collar \| see-through \| serafuku \| nipples \| nude \| doujin_cover \| content_rating \| underwear_only \| white_panties \| white_bra \| 1boy \| hetero \| solo_focus \| sex \| sweat \| censored \| girl_on_top \| vaginal \| penis \| cowgirl_position \| pussy \| playboy_bunny \| rabbit_ears \| detached_collar \| fake_animal_ears \| rabbit_tail \| fake_tail \| strapless_leotard \| wrist_cuffs \| black_leotard \| bowtie \| cowboy_shot \| armpits \| arms_up \| brown_pantyhose \| fishnet_pantyhose \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:----------\|:-------------\|:-------\|:-----------\|:--------------\|:-----------------\|:----------------\|:-------------\|:-------------------\|:-----------\|:---------\|:--------\|:-----\|:------\|:----------\|:--------------\|:--------\|:---------------\|:--------\|:----------------\|:-----------------\|:-----------\|:--------------\|:--------------------\|:--------------\|:-------\|:---------------\|:----------\|:------------\|:-------------\|:--------------\|:----------------\|:-------------\|:------------\|:--------------------\|:-------------------\|:--------\|:--------------\|:------\|:--------------\|:----------------\|:----------------\|:--------------\|:-----------\|:----------\|:-------\|:---------------\|:-----------------\|:-----------------\|:----------------\|:------------\|:-------\|:---------\|:-------------\|:------\|:--------\|:-----------\|:--------------\|:----------\|:--------\|:-------------------\|:--------\|:----------------\|:--------------\|:------------------\|:-------------------\|:--------------\|:------------\|:--------------------\|:--------------\|:----------------\|:---------\|:--------------\|:----------\|:----------\|:------------------\|:--------------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| \| \| X \| X \| X \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| X \| X \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| X \| X \| X \| X \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 9 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 17 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 12 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/natalia_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T05:45:25+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T19:16:31+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls) =========================================================== This is the dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are 'dark-skinned\_female, dark\_skin, short\_hair, purple\_eyes, breasts, black\_hair, green\_hair, large\_breasts, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
6927da00136947b440a657c2773e1a706c1e1ca9	# Bangumi Image Base of Sakurasou No Pet Na Kanojo This is the image base of bangumi Sakurasou no Pet na Kanojo, we detected 24 characters, 4107 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 1328 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 405 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 313 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 33 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 18 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 46 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| ![preview 7](5/preview_7.png) \| ![preview 8](5/preview_8.png) \| \| 6 \| 47 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 74 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 580 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 105 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 43 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 523 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 43 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 71 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 11 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| 15 \| 21 \| [Download](15/dataset.zip) \| ![preview 1](15/preview_1.png) \| ![preview 2](15/preview_2.png) \| ![preview 3](15/preview_3.png) \| ![preview 4](15/preview_4.png) \| ![preview 5](15/preview_5.png) \| ![preview 6](15/preview_6.png) \| ![preview 7](15/preview_7.png) \| ![preview 8](15/preview_8.png) \| \| 16 \| 139 \| [Download](16/dataset.zip) \| ![preview 1](16/preview_1.png) \| ![preview 2](16/preview_2.png) \| ![preview 3](16/preview_3.png) \| ![preview 4](16/preview_4.png) \| ![preview 5](16/preview_5.png) \| ![preview 6](16/preview_6.png) \| ![preview 7](16/preview_7.png) \| ![preview 8](16/preview_8.png) \| \| 17 \| 13 \| [Download](17/dataset.zip) \| ![preview 1](17/preview_1.png) \| ![preview 2](17/preview_2.png) \| ![preview 3](17/preview_3.png) \| ![preview 4](17/preview_4.png) \| ![preview 5](17/preview_5.png) \| ![preview 6](17/preview_6.png) \| ![preview 7](17/preview_7.png) \| ![preview 8](17/preview_8.png) \| \| 18 \| 9 \| [Download](18/dataset.zip) \| ![preview 1](18/preview_1.png) \| ![preview 2](18/preview_2.png) \| ![preview 3](18/preview_3.png) \| ![preview 4](18/preview_4.png) \| ![preview 5](18/preview_5.png) \| ![preview 6](18/preview_6.png) \| ![preview 7](18/preview_7.png) \| ![preview 8](18/preview_8.png) \| \| 19 \| 28 \| [Download](19/dataset.zip) \| ![preview 1](19/preview_1.png) \| ![preview 2](19/preview_2.png) \| ![preview 3](19/preview_3.png) \| ![preview 4](19/preview_4.png) \| ![preview 5](19/preview_5.png) \| ![preview 6](19/preview_6.png) \| ![preview 7](19/preview_7.png) \| ![preview 8](19/preview_8.png) \| \| 20 \| 9 \| [Download](20/dataset.zip) \| ![preview 1](20/preview_1.png) \| ![preview 2](20/preview_2.png) \| ![preview 3](20/preview_3.png) \| ![preview 4](20/preview_4.png) \| ![preview 5](20/preview_5.png) \| ![preview 6](20/preview_6.png) \| ![preview 7](20/preview_7.png) \| ![preview 8](20/preview_8.png) \| \| 21 \| 20 \| [Download](21/dataset.zip) \| ![preview 1](21/preview_1.png) \| ![preview 2](21/preview_2.png) \| ![preview 3](21/preview_3.png) \| ![preview 4](21/preview_4.png) \| ![preview 5](21/preview_5.png) \| ![preview 6](21/preview_6.png) \| ![preview 7](21/preview_7.png) \| ![preview 8](21/preview_8.png) \| \| 22 \| 9 \| [Download](22/dataset.zip) \| ![preview 1](22/preview_1.png) \| ![preview 2](22/preview_2.png) \| ![preview 3](22/preview_3.png) \| ![preview 4](22/preview_4.png) \| ![preview 5](22/preview_5.png) \| ![preview 6](22/preview_6.png) \| ![preview 7](22/preview_7.png) \| ![preview 8](22/preview_8.png) \| \| noise \| 219 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|	BangumiBase/sakurasounopetnakanojo	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-09-14T05:45:43+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-09-29T06:39:39+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Sakurasou No Pet Na Kanojo ================================================ This is the image base of bangumi Sakurasou no Pet na Kanojo, we detected 24 characters, 4107 images in total. The full dataset is here. Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
f5c39d4691446dcc605d0ddf4a198a3286d4e0f5	# Dataset of shirayuki_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls) This is the dataset of shirayuki_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls), containing 323 images and their tags. The core tags of this character are `black_hair, short_hair, bangs, purple_eyes, 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 \| 323 \| 392.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 323 \| 225.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 741 \| 469.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 323 \| 349.86 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 741 \| 680.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_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/shirayuki_chiyo_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 \| 15 \| ![](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_gloves, black_serafuku, black_skirt, long_sleeves, pleated_skirt, red_neckerchief, solo, looking_at_viewer, black_shirt, simple_background, white_sailor_collar, bob_cut, closed_mouth, white_background \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, red_neckerchief, solo, upper_body, long_sleeves, simple_background, white_sailor_collar, black_gloves, black_shirt, closed_mouth, white_background, black_serafuku, blush, bob_cut \| \| 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_serafuku, black_shirt, bob_cut, looking_at_viewer, red_neckerchief, solo, upper_body, white_sailor_collar, parted_lips, shaded_face, disgust, simple_background, white_background \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, black_bikini, collarbone, solo, bare_shoulders, blush, looking_at_viewer, navel, small_breasts, white_background \| \| 4 \| 5 \| ![](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, black_dress, black_gloves, elbow_gloves, looking_at_viewer, solo, bare_shoulders, bracelet, frills, bob_cut, bowtie, flower, hair_ornament, parted_lips, ribbon, simple_background, sleeveless_dress, upper_body \| \| 5 \| 9 \| ![](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) \| looking_at_viewer, maid_headdress, 1girl, maid_apron, solo, black_gloves, bowtie, brooch, juliet_sleeves, black_dress, blush, grey_background, blue_bow, enmaided, simple_background, white_apron \| \| 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) \| ascot, black_gloves, feather_hair_ornament, holding_sword, looking_at_viewer, 1girl, belt, closed_mouth, long_sleeves, solo, white_background, blush, brooch, sheathed, simple_background, smile, black_thighhighs, blue_flower, blue_ribbon, bob_cut, cape, cowboy_shot, petals, scabbard, skirt, white_jacket \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| 1boy, 1girl, blush, hetero, vaginal, breasts, penis, serafuku, sex, solo_focus, pussy, black_gloves, missionary, mosaic_censoring, nipples, on_back, open_mouth, pubic_hair, bar_censor, navel, pantyhose, spread_legs, torn_clothes \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| black_gloves \| black_serafuku \| black_skirt \| long_sleeves \| pleated_skirt \| red_neckerchief \| solo \| looking_at_viewer \| black_shirt \| simple_background \| white_sailor_collar \| bob_cut \| closed_mouth \| white_background \| upper_body \| blush \| parted_lips \| shaded_face \| disgust \| black_bikini \| collarbone \| bare_shoulders \| navel \| small_breasts \| black_dress \| elbow_gloves \| bracelet \| frills \| bowtie \| flower \| hair_ornament \| ribbon \| sleeveless_dress \| maid_headdress \| maid_apron \| brooch \| juliet_sleeves \| grey_background \| blue_bow \| enmaided \| white_apron \| ascot \| feather_hair_ornament \| holding_sword \| belt \| sheathed \| smile \| black_thighhighs \| blue_flower \| blue_ribbon \| cape \| cowboy_shot \| petals \| scabbard \| skirt \| white_jacket \| 1boy \| hetero \| vaginal \| breasts \| penis \| serafuku \| sex \| solo_focus \| pussy \| missionary \| mosaic_censoring \| nipples \| on_back \| open_mouth \| pubic_hair \| bar_censor \| pantyhose \| spread_legs \| torn_clothes \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:-----------------\|:--------------\|:---------------\|:----------------\|:------------------\|:-------\|:--------------------\|:--------------\|:--------------------\|:----------------------\|:----------\|:---------------\|:-------------------\|:-------------\|:--------\|:--------------\|:--------------\|:----------\|:---------------\|:-------------\|:-----------------\|:--------\|:----------------\|:--------------\|:---------------\|:-----------\|:---------\|:---------\|:---------\|:----------------\|:---------\|:-------------------\|:-----------------\|:-------------\|:---------\|:-----------------\|:------------------\|:-----------\|:-----------\|:--------------\|:--------\|:------------------------\|:----------------\|:-------\|:-----------\|:--------\|:-------------------\|:--------------\|:--------------\|:-------\|:--------------\|:---------\|:-----------\|:--------\|:---------------\|:-------\|:---------\|:----------\|:----------\|:--------\|:-----------\|:------\|:-------------\|:--------\|:-------------\|:-------------------\|:----------\|:----------\|:-------------\|:-------------\|:-------------\|:------------\|:--------------\|:---------------\| \| 0 \| 15 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 11 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| X \| \| X \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| \| \| \| \| X \| X \| \| X \| \| X \| \| \| X \| \| X \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 9 \| ![](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 \| 5 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| \| X \| \| \| X \| X \| \| X \| \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 9 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/shirayuki_chiyo_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T06:12:02+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:48:10+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of shirayuki\_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls) =================================================================== This is the dataset of shirayuki\_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls), containing 323 images and their tags. The core tags of this character are 'black\_hair, short\_hair, bangs, purple\_eyes, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
82cbea549dcd66d035bfb866b6d806152327c00c	# Dataset Card for "election2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davidadamczyk/election2	[ "region:us" ]	2023-09-14T06:12:18+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "text_label", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 108283.95478723405, "num_examples": 526}, {"name": "test", "num_bytes": 46525.04521276596, "num_examples": 226}], "download_size": 84563, "dataset_size": 154809.0}}	2023-09-14T06:12:25+00:00	[]	[]	TAGS #region-us	# Dataset Card for "election2" More Information needed	[ "# Dataset Card for \"election2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"election2\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"election2\"\n\nMore Information needed" ]
e8558fb2527e2d1d63d55ba1f4030023e044ad8e	# Dataset Card for "corpus_1_clustered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	HydraLM/corpus_1_clustered	[ "region:us" ]	2023-09-14T06:18:45+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 99791008, "num_examples": 10000}], "download_size": 75705515, "dataset_size": 99791008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T06:34:35+00:00	[]	[]	TAGS #region-us	# Dataset Card for "corpus_1_clustered" More Information needed	[ "# Dataset Card for \"corpus_1_clustered\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"corpus_1_clustered\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered\"\n\nMore Information needed" ]
1ead7035f848c138e7091e0e103a0d8d3d87f9f1	# Dataset Card for "corpus_1_clustered_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	pharaouk/corpus_1_clustered_2	[ "region:us" ]	2023-09-14T06:37:05+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 99791008, "num_examples": 10000}], "download_size": 75705515, "dataset_size": 99791008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T06:37:18+00:00	[]	[]	TAGS #region-us	# Dataset Card for "corpus_1_clustered_2" More Information needed	[ "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]
f9257e15d84620f869fb7f33fa7ed5020c5fb59e	A collection of about 21k urls of news articles taken from RAI news sites (national and regionals). The file ("urls_train_set.csv") contains around 20k of them reffering to articles published in the period 01/01/2022 – 09/03/2023 while the file ("urls_test_set.csv") contains urls referring to articles published in the period 10/03/2023 - 04/05/2023.	raicrits/news_urls	[ "license:other", "region:us" ]	2023-09-14T06:38:55+00:00	{"license": "other"}	2023-09-14T06:43:51+00:00	[]	[]	TAGS #license-other #region-us	A collection of about 21k urls of news articles taken from RAI news sites (national and regionals). The file ("urls_train_set.csv") contains around 20k of them reffering to articles published in the period 01/01/2022 – 09/03/2023 while the file ("urls_test_set.csv") contains urls referring to articles published in the period 10/03/2023 - 04/05/2023.	[]	[ "TAGS\n#license-other #region-us \n" ]	[ 11 ]	[ "passage: TAGS\n#license-other #region-us \n" ]
ad19f815c8d8263b39b825cf275d797b7803c49e	# Dataset Card for "corpus_1_clustered_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	HydraLM/corpus_1_clustered_2	[ "region:us" ]	2023-09-14T06:39:25+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 99791008, "num_examples": 10000}], "download_size": 0, "dataset_size": 99791008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T06:42:45+00:00	[]	[]	TAGS #region-us	# Dataset Card for "corpus_1_clustered_2" More Information needed	[ "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]
e774e584e6f02954246f36048ec9eeeb15df2001	# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/vihangd/smartyplats-3b-v2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [vihangd/smartyplats-3b-v2](https://huggingface.co/vihangd/smartyplats-3b-v2) 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_vihangd__smartyplats-3b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T18:23:12.485611](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__smartyplats-3b-v2/blob/main/results_2023-10-28T18-23-12.485611.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": 0.0016778523489932886, "em_stderr": 0.00041913301788268375, "f1": 0.05385906040268484, "f1_stderr": 0.0013190145725969279, "acc": 0.3426093528318133, "acc_stderr": 0.008335127579695843 }, "harness\|drop\|3": { "em": 0.0016778523489932886, "em_stderr": 0.00041913301788268375, "f1": 0.05385906040268484, "f1_stderr": 0.0013190145725969279 }, "harness\|gsm8k\|5": { "acc": 0.01592115238817286, "acc_stderr": 0.0034478192723889915 }, "harness\|winogrande\|5": { "acc": 0.6692975532754538, "acc_stderr": 0.013222435887002695 } } ``` ### 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]	open-llm-leaderboard/details_vihangd__smartyplats-3b-v2	[ "region:us" ]	2023-09-14T06:53:24+00:00	{"pretty_name": "Evaluation run of vihangd/smartyplats-3b-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [vihangd/smartyplats-3b-v2](https://huggingface.co/vihangd/smartyplats-3b-v2) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vihangd__smartyplats-3b-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T18:23:12.485611](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__smartyplats-3b-v2/blob/main/results_2023-10-28T18-23-12.485611.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\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268375,\n \"f1\": 0.05385906040268484,\n \"f1_stderr\": 0.0013190145725969279,\n \"acc\": 0.3426093528318133,\n \"acc_stderr\": 0.008335127579695843\n },\n \"harness\|drop\|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268375,\n \"f1\": 0.05385906040268484,\n \"f1_stderr\": 0.0013190145725969279\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.01592115238817286,\n \"acc_stderr\": 0.0034478192723889915\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.6692975532754538,\n \"acc_stderr\": 0.013222435887002695\n }\n}\n```", "repo_url": "https://huggingface.co/vihangd/smartyplats-3b-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T07_53_11.714726", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T07-53-11.714726.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T07-53-11.714726.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T18_23_12.485611", "path": ["/details_harness\|drop\|3_2023-10-28T18-23-12.485611.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-28T18-23-12.485611.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T18_23_12.485611", "path": ["/details_harness\|gsm8k\|5_2023-10-28T18-23-12.485611.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-28T18-23-12.485611.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T07_53_11.714726", "path": ["/details_harness\|hellaswag\|10_2023-09-14T07-53-11.714726.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-14T07-53-11.714726.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T07_53_11.714726", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T07-53-11.714726.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T07-53-11.714726.parquet", 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clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-28T18:23:12.485611(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T18:23:12.485611(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T18:23:12.485611(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 21, 31, 169, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T18:23:12.485611(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
9cda9018e10a8dbf57a56efacb752415fc801aa0	# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1 ## Dataset Description - Homepage: - Repository: https://huggingface.co/HyperbeeAI/Tulpar-7b-v1 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [HyperbeeAI/Tulpar-7b-v1](https://huggingface.co/HyperbeeAI/Tulpar-7b-v1) 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_HyperbeeAI__Tulpar-7b-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T09:02:28.162757](https://huggingface.co/datasets/open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1/blob/main/results_2023-10-23T09-02-28.162757.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": 0.2915268456375839, "em_stderr": 0.004654152691335802, "f1": 0.3658179530201351, "f1_stderr": 0.004568137923093851, "acc": 0.3656847615417434, "acc_stderr": 0.0074116135789820126 }, "harness\|drop\|3": { "em": 0.2915268456375839, "em_stderr": 0.004654152691335802, "f1": 0.3658179530201351, "f1_stderr": 0.004568137923093851 }, "harness\|gsm8k\|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544944 }, "harness\|winogrande\|5": { "acc": 0.7245461720599842, "acc_stderr": 0.01255569005570953 } } ``` ### 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]	open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1	[ "region:us" ]	2023-09-14T06:59:55+00:00	{"pretty_name": "Evaluation run of HyperbeeAI/Tulpar-7b-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [HyperbeeAI/Tulpar-7b-v1](https://huggingface.co/HyperbeeAI/Tulpar-7b-v1) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T09:02:28.162757](https://huggingface.co/datasets/open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1/blob/main/results_2023-10-23T09-02-28.162757.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\": 0.2915268456375839,\n \"em_stderr\": 0.004654152691335802,\n \"f1\": 0.3658179530201351,\n \"f1_stderr\": 0.004568137923093851,\n \"acc\": 0.3656847615417434,\n \"acc_stderr\": 0.0074116135789820126\n },\n \"harness\|drop\|3\": {\n \"em\": 0.2915268456375839,\n \"em_stderr\": 0.004654152691335802,\n \"f1\": 0.3658179530201351,\n \"f1_stderr\": 0.004568137923093851\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.006823351023502654,\n \"acc_stderr\": 0.0022675371022544944\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7245461720599842,\n \"acc_stderr\": 0.01255569005570953\n }\n}\n```", "repo_url": "https://huggingface.co/HyperbeeAI/Tulpar-7b-v1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T07_59_39.326009", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T07-59-39.326009.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T07-59-39.326009.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T09_02_28.162757", "path": ["/details_harness\|drop\|3_2023-10-23T09-02-28.162757.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-23T09-02-28.162757.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T09_02_28.162757", "path": ["/details_harness\|gsm8k\|5_2023-10-23T09-02-28.162757.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-23T09-02-28.162757.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T07_59_39.326009", "path": ["/details_harness\|hellaswag\|10_2023-09-14T07-59-39.326009.parquet"]}, {"split": "latest", "path": 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["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T07-59-39.326009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T07-59-39.326009.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_14T07_59_39.326009", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T07-59-39.326009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T07-59-39.326009.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_14T07_59_39.326009", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T07-59-39.326009.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T07-59-39.326009.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_14T07_59_39.326009", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T07-59-39.326009.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T07-59-39.326009.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_23T09_02_28.162757", "path": ["/details_harness\|winogrande\|5_2023-10-23T09-02-28.162757.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-10-23T09-02-28.162757.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T07_59_39.326009", "path": ["results_2023-09-14T07-59-39.326009.parquet"]}, {"split": "2023_10_23T09_02_28.162757", "path": ["results_2023-10-23T09-02-28.162757.parquet"]}, {"split": "latest", "path": ["results_2023-10-23T09-02-28.162757.parquet"]}]}]}	2023-10-23T08:02:42+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-23T09:02:28.162757(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T09:02:28.162757(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T09:02:28.162757(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 22, 31, 170, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T09:02:28.162757(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
b7aa99eccfed2b60cd47b13827c2a7738a3dfae5	# Dataset Card for "oasst1-m2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	TinyPixel/oasst1-m2	[ "region:us" ]	2023-09-14T07:02:34+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9483398, "num_examples": 8274}], "download_size": 5130538, "dataset_size": 9483398}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-15T03:13:51+00:00	[]	[]	TAGS #region-us	# Dataset Card for "oasst1-m2" More Information needed	[ "# Dataset Card for \"oasst1-m2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"oasst1-m2\"\n\nMore Information needed" ]	[ 6, 16 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"oasst1-m2\"\n\nMore Information needed" ]
6cfaf7f49a3da66cc46700e7e4c89695a814342a	# Dataset Card for "finreport-llama2-5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Arrivedercis/finreport-llama2-5k	[ "region:us" ]	2023-09-14T07:14:03+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2293425, "num_examples": 10000}], "download_size": 1144776, "dataset_size": 2293425}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-16T01:49:04+00:00	[]	[]	TAGS #region-us	# Dataset Card for "finreport-llama2-5k" More Information needed	[ "# Dataset Card for \"finreport-llama2-5k\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"finreport-llama2-5k\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"finreport-llama2-5k\"\n\nMore Information needed" ]
de71631994c8a0926f58cc0c857d75b0cd542f3f	# Dataset Card for "clustered_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	HydraLM/clustered_1	[ "region:us" ]	2023-09-14T07:19:05+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 17476162280, "num_examples": 1472917}], "download_size": 12523176003, "dataset_size": 17476162280}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T09:21:47+00:00	[]	[]	TAGS #region-us	# Dataset Card for "clustered_1" More Information needed	[ "# Dataset Card for \"clustered_1\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"clustered_1\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"clustered_1\"\n\nMore Information needed" ]
fa53b1d472f1e35069d8154133b8445c4f47703b	# Dataset of morikubo_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls) This is the dataset of morikubo_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `brown_eyes, bangs, light_brown_hair, long_hair, drill_hair, earrings, brown_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 \| 500 \| 583.77 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 500 \| 323.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1042 \| 678.21 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 500 \| 504.02 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1042 \| 997.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_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/morikubo_nono_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 \| 16 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, blue_dress, simple_background, ringlets, upper_body, white_background, puffy_short_sleeves, medium_hair, open_mouth, blush, sweat, @_@, stud_earrings, tears, hair_ornament \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blue_dress, blue_footwear, chibi, puffy_short_sleeves, ringlets, solo, collared_dress, shoes, closed_mouth, holding, standing, :3, blush, full_body, outdoors, sitting, white_background, white_socks \| \| 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, blonde_hair, blush, looking_at_viewer, solo, upper_body, holding, flower, jewelry, long_sleeves, ascot, medium_hair, open_mouth, simple_background \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, simple_background, solo, white_background, bare_shoulders, smile, collarbone, looking_at_viewer, medium_hair, ringlets, sleeveless_dress, upper_body, blonde_hair, choker, jewelry \| \| 4 \| 13 \| ![](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, jewelry, solo, blush, sleeveless, black_gloves, looking_at_viewer, smile, corset, green_dress, holding_microphone, open_mouth, simple_background, white_background, blonde_hair, frills, lace, @_@, hair_bow, hair_ornament, thighhighs \| \| 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) \| 1boy, 1girl, blush, hetero, solo_focus, nipples, nude, open_mouth, simple_background, sweat, @_@, bar_censor, cum, medium_hair, penis, small_breasts, tears, white_background, blonde_hair, handjob, navel, pussy, ringlets, sex \| \| 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, hair_bow, ringlets, solo, blush, floral_print, looking_at_viewer, upper_body, wide_sleeves, frills, holding, jewelry, long_sleeves, open_mouth, print_kimono, ribbon, smile, blonde_hair \| \| 7 \| 15 \| ![](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, solo, blush, frilled_bikini, floral_print, hair_flower, navel, ringlets, white_bikini, collarbone, wavy_mouth, looking_at_viewer, outdoors, print_bikini, small_breasts, water, bare_shoulders, blonde_hair, open_mouth \| \| 8 \| 9 \| ![](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) \| 1boy, 1girl, blush, hetero, solo_focus, open_mouth, penis, pussy, tears, sweat, vaginal, long_sleeves, mosaic_censoring, on_back, short_hair, wavy_mouth, blonde_hair, clothed_sex, dress, hair_flower, jaggy_lines, jewelry, oekaki, panties, spread_legs \| \| 9 \| 5 \| ![](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, one_side_up, pleated_skirt, school_uniform, solo, hair_scrunchie, long_sleeves, stud_earrings, white_background, cardigan, kogal, looking_at_viewer, nail_polish, necklace, simple_background, blue_skirt, cellphone, charm_(object), flying_sweatdrops, from_below, green_bow, green_scrunchie, hairclip, holding_phone, loose_bowtie, loose_socks, medium_breasts, open_mouth, pantyshot, school_bag, sitting, striped_panties, white_shirt, white_socks \| \| 10 \| 5 \| ![](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) \| 1girl, cosplay, ringlets, simple_background, solo, white_background, belt_buckle, black_footwear, shoes, sweat, full_body, long_sleeves, nose_blush, open_jacket, white_shirt, >_<, @_@, black_shirt, blue_pants, boots, brown_footwear, chain, closed_eyes, collared_shirt, fingerless_gloves, holding_weapon, open_mouth, parted_lips, standing, tears, wavy_mouth, white_gloves, white_jacket \| \| 11 \| 13 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| 1girl, solo, bow, jewelry, blush, cape, long_sleeves, star_(symbol), fur-trimmed_cloak, looking_at_viewer, mini_crown, shorts, smile, side_ponytail, white_background \| \| 12 \| 9 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| maid_headdress, blush, enmaided, frills, 1girl, black_dress, jewelry, solo, long_sleeves, open_mouth, puffy_sleeves, ringlets, blonde_hair, bow, looking_at_viewer, maid_apron, wavy_mouth, simple_background, white_apron, white_background \| \| 13 \| 5 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-sample4.png) \| 1girl, @_@, fake_animal_ears, rabbit_ears, ringlets, solo, white_background, detached_collar, nose_blush, simple_background, sweat, bare_shoulders, black_bowtie, black_jacket, hand_up, navel, white_collar, wing_collar, wrist_cuffs, arm_behind_back, black_hairband, black_leotard, breasts, closed_mouth, hair_between_eyes, hands_up, long_sleeves, looking_away, no_pants, pantyhose, playboy_bunny, rabbit_tail, strapless_leotard, wavy_mouth, white_gloves, white_panties \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| blue_dress \| simple_background \| ringlets \| upper_body \| white_background \| puffy_short_sleeves \| medium_hair \| open_mouth \| blush \| sweat \| @_@ \| stud_earrings \| tears \| hair_ornament \| blue_footwear \| chibi \| collared_dress \| shoes \| closed_mouth \| holding \| standing \| :3 \| full_body \| outdoors \| sitting \| white_socks \| blonde_hair \| looking_at_viewer \| flower \| jewelry \| long_sleeves \| ascot \| bare_shoulders \| smile \| collarbone \| sleeveless_dress \| choker \| sleeveless \| black_gloves \| corset \| green_dress \| holding_microphone \| frills \| lace \| hair_bow \| thighhighs \| 1boy \| hetero \| solo_focus \| nipples \| nude \| bar_censor \| cum \| penis \| small_breasts \| handjob \| navel \| pussy \| sex \| floral_print \| wide_sleeves \| print_kimono \| ribbon \| frilled_bikini \| hair_flower \| white_bikini \| wavy_mouth \| print_bikini \| water \| vaginal \| mosaic_censoring \| on_back \| short_hair \| clothed_sex \| dress \| jaggy_lines \| oekaki \| panties \| spread_legs \| one_side_up \| pleated_skirt \| school_uniform \| hair_scrunchie \| cardigan \| kogal \| nail_polish \| necklace \| blue_skirt \| cellphone \| charm_(object) \| flying_sweatdrops \| from_below \| green_bow \| green_scrunchie \| hairclip \| holding_phone \| loose_bowtie \| loose_socks \| medium_breasts \| pantyshot \| school_bag \| striped_panties \| white_shirt \| cosplay \| belt_buckle \| black_footwear \| nose_blush \| open_jacket \| >_< \| black_shirt \| blue_pants \| boots \| brown_footwear \| chain \| closed_eyes \| collared_shirt \| fingerless_gloves \| holding_weapon \| parted_lips \| white_gloves \| white_jacket \| bow \| cape \| star_(symbol) \| fur-trimmed_cloak \| mini_crown \| shorts \| side_ponytail \| maid_headdress \| enmaided \| black_dress \| puffy_sleeves \| maid_apron \| white_apron \| fake_animal_ears \| rabbit_ears \| detached_collar \| black_bowtie \| black_jacket \| hand_up \| white_collar \| wing_collar \| wrist_cuffs \| arm_behind_back \| black_hairband \| black_leotard \| breasts \| hair_between_eyes \| hands_up \| looking_away \| no_pants \| pantyhose \| playboy_bunny \| rabbit_tail \| strapless_leotard \| white_panties \| \|----:\|----------:\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:----------------------------------\|:--------\|:-------\|:-------------\|:--------------------\|:-----------\|:-------------\|:-------------------\|:----------------------\|:--------------\|:-------------\|:--------\|:--------\|:------\|:----------------\|:--------\|:----------------\|:----------------\|:--------\|:-----------------\|:--------\|:---------------\|:----------\|:-----------\|:-----\|:------------\|:-----------\|:----------\|:--------------\|:--------------\|:--------------------\|:---------\|:----------\|:---------------\|:--------\|:-----------------\|:--------\|:-------------\|:-------------------\|:---------\|:-------------\|:---------------\|:---------\|:--------------\|:---------------------\|:---------\|:-------\|:-----------\|:-------------\|:-------\|:---------\|:-------------\|:----------\|:-------\|:-------------\|:------\|:--------\|:----------------\|:----------\|:--------\|:--------\|:------\|:---------------\|:---------------\|:---------------\|:---------\|:-----------------\|:--------------\|:---------------\|:-------------\|:---------------\|:--------\|:----------\|:-------------------\|:----------\|:-------------\|:--------------\|:--------\|:--------------\|:---------\|:----------\|:--------------\|:--------------\|:----------------\|:-----------------\|:-----------------\|:-----------\|:--------\|:--------------\|:-----------\|:-------------\|:------------\|:-----------------\|:--------------------\|:-------------\|:------------\|:------------------\|:-----------\|:----------------\|:---------------\|:--------------\|:-----------------\|:------------\|:-------------\|:------------------\|:--------------\|:----------\|:--------------\|:-----------------\|:-------------\|:--------------\|:------\|:--------------\|:-------------\|:--------\|:-----------------\|:--------\|:--------------\|:-----------------\|:--------------------\|:-----------------\|:--------------\|:---------------\|:---------------\|:------\|:-------\|:----------------\|:--------------------\|:-------------\|:---------\|:----------------\|:-----------------\|:-----------\|:--------------\|:----------------\|:-------------\|:--------------\|:-------------------\|:--------------\|:------------------\|:---------------\|:---------------\|:----------\|:---------------\|:--------------\|:--------------\|:------------------\|:-----------------\|:----------------\|:----------\|:--------------------\|:-----------\|:---------------\|:-----------\|:------------\|:----------------\|:--------------\|:--------------------\|:----------------\| \| 0 \| 16 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| X \| X \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| X \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 13 \| ![](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 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| \| X \| X \| \| X \| \| X \| X \| X \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 15 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 9 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| \| X \| \| \| X \| \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 10 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 11 \| 13 \| ![](samples/11/clu11-sample0.png) \| ![](samples/11/clu11-sample1.png) \| ![](samples/11/clu11-sample2.png) \| ![](samples/11/clu11-sample3.png) \| ![](samples/11/clu11-sample4.png) \| X \| X \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 12 \| 9 \| ![](samples/12/clu12-sample0.png) \| ![](samples/12/clu12-sample1.png) \| ![](samples/12/clu12-sample2.png) \| ![](samples/12/clu12-sample3.png) \| ![](samples/12/clu12-sample4.png) \| X \| X \| \| X \| X \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 13 \| 5 \| ![](samples/13/clu13-sample0.png) \| ![](samples/13/clu13-sample1.png) \| ![](samples/13/clu13-sample2.png) \| ![](samples/13/clu13-sample3.png) \| ![](samples/13/clu13-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 \|	CyberHarem/morikubo_nono_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T07:19:22+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T11:11:17+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of morikubo\_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls) ========================================================================= This is the dataset of morikubo\_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'brown\_eyes, bangs, light\_brown\_hair, long\_hair, drill\_hair, earrings, brown\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
fd3e012225362817e83b6e7ed6bf6a0e6551614a	# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w ## Dataset Description - Homepage: - Repository: https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w](https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w) 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_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T02:53:14.013945](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w/blob/main/results_2023-10-25T02-53-14.013945.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": 0.002936241610738255, "em_stderr": 0.0005541113054709996, "f1": 0.0881931627516778, "f1_stderr": 0.0017824166498732977, "acc": 0.43444724154830167, "acc_stderr": 0.01050882298494655 }, "harness\|drop\|3": { "em": 0.002936241610738255, "em_stderr": 0.0005541113054709996, "f1": 0.0881931627516778, "f1_stderr": 0.0017824166498732977 }, "harness\|gsm8k\|5": { "acc": 0.11751326762699014, "acc_stderr": 0.008870331256489993 }, "harness\|winogrande\|5": { "acc": 0.7513812154696132, "acc_stderr": 0.012147314713403108 } } ``` ### 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]	open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w	[ "region:us" ]	2023-09-14T07:19:31+00:00	{"pretty_name": "Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "dataset_summary": "Dataset automatically created during the evaluation run of model [lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w](https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T02:53:14.013945](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w/blob/main/results_2023-10-25T02-53-14.013945.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\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709996,\n \"f1\": 0.0881931627516778,\n \"f1_stderr\": 0.0017824166498732977,\n \"acc\": 0.43444724154830167,\n \"acc_stderr\": 0.01050882298494655\n },\n \"harness\|drop\|3\": {\n \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709996,\n \"f1\": 0.0881931627516778,\n \"f1_stderr\": 0.0017824166498732977\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.11751326762699014,\n \"acc_stderr\": 0.008870331256489993\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403108\n }\n}\n```", "repo_url": "https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T02_53_14.013945", "path": ["/details_harness\|drop\|3_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-25T02-53-14.013945.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T02_53_14.013945", "path": ["/details_harness\|gsm8k\|5_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-25T02-53-14.013945.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["/details_harness\|hellaswag\|10_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T08-19-15.271974.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T08-19-15.271974.parquet", 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"path": ["/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": 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["/details_harness\|truthfulqa:mc\|0_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_25T02_53_14.013945", "path": ["/details_harness\|winogrande\|5_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-10-25T02-53-14.013945.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["results_2023-09-14T08-19-15.271974.parquet"]}, {"split": "2023_10_25T02_53_14.013945", "path": ["results_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T02-53-14.013945.parquet"]}]}]}	2023-10-25T01:53:26+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-25T02:53:14.013945(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T02:53:14.013945(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T02:53:14.013945(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 33, 31, 181, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T02:53:14.013945(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
550931003d3619b88aef7c283db4b84eb26ce80d	# Dataset Card for "fabric_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	rikdas/fabric_dataset	[ "region:us" ]	2023-09-14T07:30:02+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41259319.0, "num_examples": 20}], "download_size": 41261924, "dataset_size": 41259319.0}}	2023-09-14T07:37:27+00:00	[]	[]	TAGS #region-us	# Dataset Card for "fabric_dataset" More Information needed	[ "# Dataset Card for \"fabric_dataset\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"fabric_dataset\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"fabric_dataset\"\n\nMore Information needed" ]
358840310535836cb7df9d872a8d345d35bf12b5	# Dataset Card for "processed_bert_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	pteron/processed_bert_dataset	[ "region:us" ]	2023-09-14T07:31:07+00:00	{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 173314800.0, "num_examples": 48143}], "download_size": 41856821, "dataset_size": 173314800.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}	2023-09-14T07:31:17+00:00	[]	[]	TAGS #region-us	# Dataset Card for "processed_bert_dataset" More Information needed	[ "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]
1031a2d0ed7441b050b2605bb0bcb993f4285bac	# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni ## Dataset Description - Homepage: - Repository: https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/ChatAYT-Lora-Assamble-Marcoroni](https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni) 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_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T13:33:47.797770](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni/blob/main/results_2023-10-24T13-33-47.797770.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": 0.008598993288590604, "em_stderr": 0.0009455579144542189, "f1": 0.1045532718120813, "f1_stderr": 0.0020198084132137728, "acc": 0.43109211314448, "acc_stderr": 0.009797803895878525 }, "harness\|drop\|3": { "em": 0.008598993288590604, "em_stderr": 0.0009455579144542189, "f1": 0.1045532718120813, "f1_stderr": 0.0020198084132137728 }, "harness\|gsm8k\|5": { "acc": 0.0887035633055345, "acc_stderr": 0.007831458737058703 }, "harness\|winogrande\|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698346 } } ``` ### 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]	open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni	[ "region:us" ]	2023-09-14T07:40:07+00:00	{"pretty_name": "Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/ChatAYT-Lora-Assamble-Marcoroni](https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T13:33:47.797770](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni/blob/main/results_2023-10-24T13-33-47.797770.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\": 0.008598993288590604,\n \"em_stderr\": 0.0009455579144542189,\n \"f1\": 0.1045532718120813,\n \"f1_stderr\": 0.0020198084132137728,\n \"acc\": 0.43109211314448,\n \"acc_stderr\": 0.009797803895878525\n },\n \"harness\|drop\|3\": {\n \"em\": 0.008598993288590604,\n \"em_stderr\": 0.0009455579144542189,\n \"f1\": 0.1045532718120813,\n \"f1_stderr\": 0.0020198084132137728\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.0887035633055345,\n \"acc_stderr\": 0.007831458737058703\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698346\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T13_33_47.797770", "path": ["/details_harness\|drop\|3_2023-10-24T13-33-47.797770.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-24T13-33-47.797770.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T13_33_47.797770", "path": ["/details_harness\|gsm8k\|5_2023-10-24T13-33-47.797770.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-24T13-33-47.797770.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hellaswag\|10_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T08-39-51.722063.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T08-39-51.722063.parquet", 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"path": ["/details_harness\|hendrycksTest-marketing\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T08-39-51.722063.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T08-39-51.722063.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T13_33_47.797770", "path": ["/details_harness\|winogrande\|5_2023-10-24T13-33-47.797770.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-10-24T13-33-47.797770.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T08_39_51.722063", "path": ["results_2023-09-14T08-39-51.722063.parquet"]}, {"split": "2023_10_24T13_33_47.797770", "path": ["results_2023-10-24T13-33-47.797770.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T13-33-47.797770.parquet"]}]}]}	2023-10-24T12:34:02+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-24T13:33:47.797770(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:33:47.797770(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:33:47.797770(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 27, 31, 175, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T13:33:47.797770(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
d08b7f6e4fc13cc40cb2def7513d8e5932666ea2	# Dataset Card for "fabric_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	rikdas/fabric_data	[ "region:us" ]	2023-09-14T07:40:30+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41259319.0, "num_examples": 20}], "download_size": 41261924, "dataset_size": 41259319.0}}	2023-09-14T07:40:34+00:00	[]	[]	TAGS #region-us	# Dataset Card for "fabric_data" More Information needed	[ "# Dataset Card for \"fabric_data\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"fabric_data\"\n\nMore Information needed" ]	[ 6, 14 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"fabric_data\"\n\nMore Information needed" ]
3fece45f78cb2b55bceca6722674c1ab46ba6ab0	# Dataset Card for "structs_token_size_4_reduced_labelled_eval" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the validation dataset. Train set at: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_train Data structures and labels are extracted from: https://zenodo.org/records/6537904 Thesis and associated scripts: https://zenodo.org/records/10053730	johannes-garstenauer/structs_token_size_4_reduced_labelled_eval	[ "digital forensics", "region:us" ]	2023-09-14T07:59:17+00:00	{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 65294030.35619895, "num_examples": 269087}], "download_size": 24102593, "dataset_size": 65294030.35619895}, "tags": ["digital forensics"]}	2023-10-30T13:26:35+00:00	[]	[]	TAGS #digital forensics #region-us	# Dataset Card for "structs_token_size_4_reduced_labelled_eval" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the validation dataset. Train set at: URL Data structures and labels are extracted from: URL Thesis and associated scripts: URL	[ "# Dataset Card for \"structs_token_size_4_reduced_labelled_eval\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the validation dataset. Train set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]	[ "TAGS\n#digital forensics #region-us \n", "# Dataset Card for \"structs_token_size_4_reduced_labelled_eval\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the validation dataset. Train set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]	[ 11, 109 ]	[ "passage: TAGS\n#digital forensics #region-us \n# Dataset Card for \"structs_token_size_4_reduced_labelled_eval\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the validation dataset. Train set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]
23f26b80cb775bfea60decd22d8b368b15bda920	# Dataset of Shiraki Hime This is the dataset of Shiraki Hime, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 655 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 655 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 655 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 655 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/shiraki_hime_watashinoyuriwaoshigotodesu	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T08:02:22+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:36:56+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Shiraki Hime ======================= This is the dataset of Shiraki Hime, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
f5ca64b81f06f354c65a1e10fcde6f3bc69a55a8	# Dataset of kurosaki_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls) This is the dataset of kurosaki_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls), containing 328 images and their tags. The core tags of this character are `blonde_hair, long_hair, bangs, red_eyes, breasts, hair_between_eyes, hairband, very_long_hair, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:--------------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 328 \| 532.94 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 328 \| 293.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 830 \| 626.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 328 \| 465.01 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 830 \| 913.70 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_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/kurosaki_chitose_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 \| 23 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, long_sleeves, looking_at_viewer, solo, open_mouth, white_shirt, black_hairband, :d, white_background, brooch, frills, simple_background, skirt, upper_body, blush, sleeves_past_wrists, thighhighs \| \| 1 \| 18 \| ![](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) \| serafuku, 1girl, long_sleeves, red_neckerchief, shirt, looking_at_viewer, solo, white_sailor_collar, blush, pleated_skirt, black_skirt, black_hairband, open_cardigan, open_mouth, white_background, :d, simple_background \| \| 2 \| 8 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, bare_shoulders, looking_at_viewer, white_dress, smile, solo, strapless_dress, wedding_dress, blush, bridal_veil, bride, cleavage, upper_body, collarbone, closed_mouth, holding_bouquet, necklace, red_rose, white_background \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, bare_shoulders, hair_flower, looking_at_viewer, medium_breasts, red_rose, solo, red_dress, smile, blush, cleavage, hair_intakes, wrist_cuffs, nail_polish, petals, red_nails, simple_background, strapless, upper_body, white_background \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, blush, looking_at_viewer, navel, solo, cleavage, collarbone, midriff, pink_shirt, groin, long_sleeves, open_mouth, pink_shorts, simple_background, sweat, white_background, :d, crop_top, heart, off_shoulder, pink_eyes, sleeves_past_wrists, stomach \| \| 5 \| 17 \| ![](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) \| looking_at_viewer, smile, 1girl, blush, collarbone, navel, solo, cleavage, bikini, bare_shoulders, sitting, black_hairband, closed_mouth, thighs \| \| 6 \| 7 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1boy, 1girl, blush, hetero, solo_focus, sweat, looking_at_viewer, nipples, penis, pov, smile, breast_grab, grabbing, mosaic_censoring, nude, open_mouth, paizuri, black_hairband, collarbone, male_pubic_hair, navel \| \| 7 \| 8 \| ![](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, fake_animal_ears, playboy_bunny, rabbit_ears, detached_collar, looking_at_viewer, solo, strapless_leotard, black_leotard, cleavage, smile, wrist_cuffs, bare_shoulders, blush, simple_background, thighhighs, white_background, black_hairband, bowtie, covered_navel, medium_breasts, nail_polish, pantyhose, rabbit_tail, red_nails \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| long_sleeves \| looking_at_viewer \| solo \| open_mouth \| white_shirt \| black_hairband \| :d \| white_background \| brooch \| frills \| simple_background \| skirt \| upper_body \| blush \| sleeves_past_wrists \| thighhighs \| serafuku \| red_neckerchief \| shirt \| white_sailor_collar \| pleated_skirt \| black_skirt \| open_cardigan \| bare_shoulders \| white_dress \| smile \| strapless_dress \| wedding_dress \| bridal_veil \| bride \| cleavage \| collarbone \| closed_mouth \| holding_bouquet \| necklace \| red_rose \| hair_flower \| medium_breasts \| red_dress \| hair_intakes \| wrist_cuffs \| nail_polish \| petals \| red_nails \| strapless \| navel \| midriff \| pink_shirt \| groin \| pink_shorts \| sweat \| crop_top \| heart \| off_shoulder \| pink_eyes \| stomach \| bikini \| sitting \| thighs \| 1boy \| hetero \| solo_focus \| nipples \| penis \| pov \| breast_grab \| grabbing \| mosaic_censoring \| nude \| paizuri \| male_pubic_hair \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| detached_collar \| strapless_leotard \| black_leotard \| bowtie \| covered_navel \| pantyhose \| rabbit_tail \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:---------------\|:--------------------\|:-------\|:-------------\|:--------------\|:-----------------\|:-----\|:-------------------\|:---------\|:---------\|:--------------------\|:--------\|:-------------\|:--------\|:----------------------\|:-------------\|:-----------\|:------------------\|:--------\|:----------------------\|:----------------\|:--------------\|:----------------\|:-----------------\|:--------------\|:--------\|:------------------\|:----------------\|:--------------\|:--------\|:-----------\|:-------------\|:---------------\|:------------------\|:-----------\|:-----------\|:--------------\|:-----------------\|:------------\|:---------------\|:--------------\|:--------------\|:---------\|:------------\|:------------\|:--------\|:----------\|:-------------\|:--------\|:--------------\|:--------\|:-----------\|:--------\|:---------------\|:------------\|:----------\|:---------\|:----------\|:---------\|:-------\|:---------\|:-------------\|:----------\|:--------\|:------\|:--------------\|:-----------\|:-------------------\|:-------\|:----------\|:------------------\|:-------------------\|:----------------\|:--------------\|:------------------\|:--------------------\|:----------------\|:---------\|:----------------\|:------------\|:--------------\| \| 0 \| 23 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 18 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| \| X \| X \| X \| \| \| X \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| X \| \| \| \| \| X \| \| \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| X \| \| \| X \| X \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 17 \| ![](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 \| 7 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| \| X \| \| X \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 8 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| \| X \| X \| \| \| X \| \| X \| \| \| X \| \| \| X \| \| X \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| X \| \| \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/kurosaki_chitose_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T08:02:34+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T19:14:50+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of kurosaki\_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls) ===================================================================== This is the dataset of kurosaki\_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls), containing 328 images and their tags. The core tags of this character are 'blonde\_hair, long\_hair, bangs, red\_eyes, breasts, hair\_between\_eyes, hairband, very\_long\_hair, large\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
4a499ac608ed3e37435d85f945bcf8707a3e23f7	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	strumber/objectDatasetLetsMOD	[ "region:us" ]	2023-09-14T08:09:37+00:00	{}	2023-09-14T14:33:06+00:00	[]	[]	TAGS #region-us	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
7e8533a9671f94a60f2ddcf3a02a5afb1e7395fe	# Dataset Card for "MedText-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	maximegmd/MedText-alpaca	[ "region:us" ]	2023-09-14T08:22:46+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 949136, "num_examples": 1412}], "download_size": 494828, "dataset_size": 949136}}	2023-09-14T08:23:08+00:00	[]	[]	TAGS #region-us	# Dataset Card for "MedText-alpaca" More Information needed	[ "# Dataset Card for \"MedText-alpaca\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"MedText-alpaca\"\n\nMore Information needed" ]	[ 6, 15 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"MedText-alpaca\"\n\nMore Information needed" ]
f92c8a01fc750a81cebbf8cafcbfa53a96c87a0c	# Dataset of Yano Mitsuki This is the dataset of Yano Mitsuki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 643 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 643 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 643 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 643 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/yano_mitsuki_watashinoyuriwaoshigotodesu	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T08:25:36+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:01+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Yano Mitsuki ======================= This is the dataset of Yano Mitsuki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
762eb77131a095a55f8bc163ba2e211c90ae424c	# Dataset Card for "celeba" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Diego1234/celeba	[ "region:us" ]	2023-09-14T08:46:43+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "validation", "path": "data/validation-"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "female", "1": "male"}}}}], "splits": [{"name": "train", "num_bytes": 2768237832.0, "num_examples": 28000}, {"name": "validation", "num_bytes": 194932418.0, "num_examples": 2000}], "download_size": 2963322017, "dataset_size": 2963170250.0}}	2023-09-19T10:49:37+00:00	[]	[]	TAGS #region-us	# Dataset Card for "celeba" More Information needed	[ "# Dataset Card for \"celeba\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"celeba\"\n\nMore Information needed" ]	[ 6, 12 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"celeba\"\n\nMore Information needed" ]
863d412c86a8b835beec63aa18d24e7e045af3d5	# Dataset of Mamiya Kanoko This is the dataset of Mamiya Kanoko, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 672 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 672 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 672 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 672 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/mamiya_kanoko_watashinoyuriwaoshigotodesu	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T08:48:48+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:03+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Mamiya Kanoko ======================== This is the dataset of Mamiya Kanoko, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
ce9f1350c8f11103a45cd50c63992fcff97d1923	# Dataset Card for "fine-tune-hebrew-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sabuhi1997/fine-tune-hebrew-dataset	[ "region:us" ]	2023-09-14T08:56:06+00:00	{"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": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train", "2": "validation"}}}}], "splits": [{"name": "train", "num_bytes": 5714802.0, "num_examples": 8}, {"name": "validation", "num_bytes": 1759819.0, "num_examples": 3}, {"name": "test", "num_bytes": 1625529.0, "num_examples": 4}], "download_size": 7719156, "dataset_size": 9100150.0}}	2023-09-14T09:40:44+00:00	[]	[]	TAGS #region-us	# Dataset Card for "fine-tune-hebrew-dataset" More Information needed	[ "# Dataset Card for \"fine-tune-hebrew-dataset\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"fine-tune-hebrew-dataset\"\n\nMore Information needed" ]	[ 6, 19 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"fine-tune-hebrew-dataset\"\n\nMore Information needed" ]
9a557b2e65d5b3b3afd6e0524273130cbb69ecfd	# 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 \|	CyberHarem/sajou_yukimi_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T08:56:59+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T15:00:58+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
8165669c49a748c91736e00cb31da3412ac7edc6	# Dataset Card for "l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	johannes-garstenauer/l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3	[ "region:us" ]	2023-09-14T08:57:29+00:00	{"dataset_info": {"features": [{"name": "last_cls", "sequence": "float32"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1542000, "num_examples": 500}], "download_size": 2136798, "dataset_size": 1542000}}	2023-09-14T08:57:36+00:00	[]	[]	TAGS #region-us	# Dataset Card for "l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3" More Information needed	[ "# Dataset Card for \"l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3\"\n\nMore Information needed" ]	[ 6, 34 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3\"\n\nMore Information needed" ]
998d048d80c67c26ad0dc2c4ffc41df5d3111545	# Dataset of Chibana Sumika This is the dataset of Chibana Sumika, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 681 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 681 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 681 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 681 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/chibana_sumika_watashinoyuriwaoshigotodesu	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T09:14:14+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:07+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Chibana Sumika ========================= This is the dataset of Chibana Sumika, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
9836639743110569310f492b56bf3d68543268cb	# Dataset of Shiina Mashiro This is the dataset of Shiina Mashiro, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 688 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 688 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 688 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 688 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/shiina_mashiro_sakurasounopetnakanojo	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T09:24:17+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:09+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Shiina Mashiro ========================= This is the dataset of Shiina Mashiro, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
2ba0483f840e4a891ff0d9cf07e80b07d6a50ce6	# Dataset of Koshiba Mai This is the dataset of Koshiba Mai, containing 220 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 220 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 507 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 220 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 220 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 220 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 220 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 220 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 507 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 507 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 507 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/koshiba_mai_watashinoyuriwaoshigotodesu	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T09:29:50+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:11+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Koshiba Mai ====================== This is the dataset of Koshiba Mai, containing 220 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
2750339c06c47074a37fde4d6b585fb12349fca5	# Dataset Card for "fine-tune-hebrew-dataset-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sabuhi1997/fine-tune-hebrew-dataset-2	[ "region:us" ]	2023-09-14T09:51:15+00:00	{"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": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5715697.0, "num_examples": 8}, {"name": "validation", "num_bytes": 1760186.0, "num_examples": 3}, {"name": "test", "num_bytes": 1625785.0, "num_examples": 4}], "download_size": 3211475, "dataset_size": 9101668.0}}	2023-09-14T09:59:14+00:00	[]	[]	TAGS #region-us	# Dataset Card for "fine-tune-hebrew-dataset-2" More Information needed	[ "# Dataset Card for \"fine-tune-hebrew-dataset-2\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"fine-tune-hebrew-dataset-2\"\n\nMore Information needed" ]	[ 6, 20 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"fine-tune-hebrew-dataset-2\"\n\nMore Information needed" ]
7061eadbc707c7aa275a9cfb85a8c9ce6ac5318a	# Dataset of hayasaka_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls) This is the dataset of hayasaka_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls), containing 374 images and their tags. The core tags of this character are `eyepatch, purple_hair, multicolored_hair, brown_eyes, short_hair, red_hair, streaked_hair, fang, 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 \| 374 \| 325.07 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 374 \| 234.34 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 802 \| 461.20 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 374 \| 305.09 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 802 \| 584.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_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/hayasaka_mirei_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 \| 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, looking_at_viewer, solo, open_mouth, simple_background, upper_body, white_background, :d, blush, claw_pose, collarbone, green_jacket, heart, long_sleeves, black_shirt, choker, horns \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, cat_hood, heart, black_shirt, looking_at_viewer, red_jacket, solo, blush, hood_up, belt_buckle, belt_collar, crescent, open_mouth, simple_background, white_background, black_belt, black_collar, claws, upper_body, short_over_long_sleeves, button_badge, collarbone, skirt, smile \| \| 2 \| 8 \| ![](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, belt, cat_hood, hoodie, skirt, solo, blush, collar, striped_thighhighs, heart, claws, open_mouth, smile, white_background \| \| 3 \| 12 \| ![](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, navel, solo, bikini_top_only, midriff, belt, claws, horns, skirt, chain, collar, looking_at_viewer, cleavage, hoodie, smile \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| 1girl, fingerless_gloves, horns, looking_at_viewer, nail_polish, solo, belt, navel, open_mouth, skirt, smile, thighhighs, collar, microphone, midriff, chain, headphones \| \| 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) \| blush, looking_at_viewer, navel, open_mouth, solo, 1girl, flat_chest, side-tie_bikini_bottom, simple_background, small_breasts, white_background, white_bikini, black_bikini, black_hair, collarbone, groin, hair_over_one_eye, horns, standing, sweat \| \| 6 \| 14 \| ![](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) \| blush, white_shirt, 1girl, pleated_skirt, solo, long_sleeves, collared_shirt, grey_skirt, simple_background, striped_bow, dress_shirt, heart, looking_at_viewer, white_background, bangs, black_thighhighs, open_mouth, school_uniform, blue_jacket, hood_down, :d, black_jacket, hooded_jacket, open_jacket, backpack, blue_bowtie \| \| 7 \| 13 \| ![](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) \| short_sleeves, looking_at_viewer, collared_shirt, hair_ornament, open_mouth, :d, fake_horns, plaid_skirt, solo_focus, two-tone_hair, 1girl, bangs, layered_skirt, pointing, purple_skirt, red_necktie, thighhighs, v-shaped_eyebrows, black_belt, blush, 2girls, belt_buckle, checkered_clothes \| \| 8 \| 10 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| 1girl, solo, looking_at_viewer, blush, cat_ears, open_mouth, shoulder_cutout, wrist_cuffs, black_necktie, garter_straps, hairband, maid_headdress, short_sleeves, waist_apron, white_apron, fake_animal_ears, frilled_apron, heart, two-tone_hair, button_badge, red_dress, shirt, simple_background, smile, striped_thighhighs, white_background \| \| 9 \| 5 \| ![](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) \| halo, maid_headdress, open_mouth, twintails, 1girl, plaid_skirt, wrist_cuffs, :d, angel_wings, looking_at_viewer, simple_background, sleeveless, solo, white_sailor_collar, blush, claw_pose, collarbone, feathered_wings, frills, midriff, multiple_girls, pink_skirt, ribbon, shirt, swimsuit, white_background, white_bow \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| looking_at_viewer \| solo \| open_mouth \| simple_background \| upper_body \| white_background \| :d \| blush \| claw_pose \| collarbone \| green_jacket \| heart \| long_sleeves \| black_shirt \| choker \| horns \| cat_hood \| red_jacket \| hood_up \| belt_buckle \| belt_collar \| crescent \| black_belt \| black_collar \| claws \| short_over_long_sleeves \| button_badge \| skirt \| smile \| belt \| hoodie \| collar \| striped_thighhighs \| navel \| bikini_top_only \| midriff \| chain \| cleavage \| fingerless_gloves \| nail_polish \| thighhighs \| microphone \| headphones \| flat_chest \| side-tie_bikini_bottom \| small_breasts \| white_bikini \| black_bikini \| black_hair \| groin \| hair_over_one_eye \| standing \| sweat \| white_shirt \| pleated_skirt \| collared_shirt \| grey_skirt \| striped_bow \| dress_shirt \| bangs \| black_thighhighs \| school_uniform \| blue_jacket \| hood_down \| black_jacket \| hooded_jacket \| open_jacket \| backpack \| blue_bowtie \| short_sleeves \| hair_ornament \| fake_horns \| plaid_skirt \| solo_focus \| two-tone_hair \| layered_skirt \| pointing \| purple_skirt \| red_necktie \| v-shaped_eyebrows \| 2girls \| checkered_clothes \| cat_ears \| shoulder_cutout \| wrist_cuffs \| black_necktie \| garter_straps \| hairband \| maid_headdress \| waist_apron \| white_apron \| fake_animal_ears \| frilled_apron \| red_dress \| shirt \| halo \| twintails \| angel_wings \| sleeveless \| white_sailor_collar \| feathered_wings \| frills \| multiple_girls \| pink_skirt \| ribbon \| swimsuit \| white_bow \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------------------\|:-------\|:-------------\|:--------------------\|:-------------\|:-------------------\|:-----\|:--------\|:------------\|:-------------\|:---------------\|:--------\|:---------------\|:--------------\|:---------\|:--------\|:-----------\|:-------------\|:----------\|:--------------\|:--------------\|:-----------\|:-------------\|:---------------\|:--------\|:--------------------------\|:---------------\|:--------\|:--------\|:-------\|:---------\|:---------\|:---------------------\|:--------\|:------------------\|:----------\|:--------\|:-----------\|:--------------------\|:--------------\|:-------------\|:-------------\|:-------------\|:-------------\|:-------------------------\|:----------------\|:---------------\|:---------------\|:-------------\|:--------\|:--------------------\|:-----------\|:--------\|:--------------\|:----------------\|:-----------------\|:-------------\|:--------------\|:--------------\|:--------\|:-------------------\|:-----------------\|:--------------\|:------------\|:---------------\|:----------------\|:--------------\|:-----------\|:--------------\|:----------------\|:----------------\|:-------------\|:--------------\|:-------------\|:----------------\|:----------------\|:-----------\|:---------------\|:--------------\|:--------------------\|:---------\|:--------------------\|:-----------\|:------------------\|:--------------\|:----------------\|:----------------\|:-----------\|:-----------------\|:--------------\|:--------------\|:-------------------\|:----------------\|:------------\|:--------\|:-------\|:------------\|:--------------\|:-------------\|:----------------------\|:------------------\|:---------\|:-----------------\|:-------------\|:---------\|:-----------\|:------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 10 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| \| X \| \| X \| \| X \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](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 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 6 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| \| X \| \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 5 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 14 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 13 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 10 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 5 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| X \| X \| X \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/hayasaka_mirei_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T09:52:36+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:02:01+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of hayasaka\_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of hayasaka\_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls), containing 374 images and their tags. The core tags of this character are 'eyepatch, purple\_hair, multicolored\_hair, brown\_eyes, short\_hair, red\_hair, streaked\_hair, fang, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
a12907a46c0e981583a17026378758e97e516b26	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	mindchain/demo_25	[ "region:us" ]	2023-09-14T09:53:18+00:00	{}	2023-09-24T10:59:44+00:00	[]	[]	TAGS #region-us	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
6d49d849d293f37df562a8a5d4780719cd8a97a5	# Dataset Card for "messi-ronaldo-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	pranjal0198/messi-ronaldo-dataset	[ "task_categories:text-to-image", "region:us" ]	2023-09-14T09:55:32+00:00	{"task_categories": ["text-to-image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61573451, "num_examples": 136}], "download_size": 0, "dataset_size": 61573451}}	2023-10-16T11:48:03+00:00	[]	[]	TAGS #task_categories-text-to-image #region-us	# Dataset Card for "messi-ronaldo-dataset" More Information needed	[ "# Dataset Card for \"messi-ronaldo-dataset\"\n\nMore Information needed" ]	[ "TAGS\n#task_categories-text-to-image #region-us \n", "# Dataset Card for \"messi-ronaldo-dataset\"\n\nMore Information needed" ]	[ 18, 18 ]	[ "passage: TAGS\n#task_categories-text-to-image #region-us \n# Dataset Card for \"messi-ronaldo-dataset\"\n\nMore Information needed" ]
6569bdd801e8a83b4a496ea4833173caf076cdfd	# Dataset of Aoyama Nanami This is the dataset of Aoyama Nanami, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 709 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 709 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 709 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 709 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/aoyama_nanami_sakurasounopetnakanojo	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T09:55:35+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:15+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Aoyama Nanami ======================== This is the dataset of Aoyama Nanami, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
38c359b169454d06d4801df316e5098aea41f0c8	# Dataset Card for "donut_vqa_ISynHMP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	warshakhan/donut_vqa_ISynHMP	[ "task_categories:visual-question-answering", "language:en", "license:unknown", "medical", " prescriptions", "region:us" ]	2023-09-14T10:10:50+00:00	{"language": ["en"], "license": "unknown", "task_categories": ["visual-question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-"}, {"split": "valid", "path": "data/valid-"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 578804498, "num_examples": 2800}, {"name": "valid", "num_bytes": 85350687, "num_examples": 400}, {"name": "test", "num_bytes": 172300907, "num_examples": 800}], "download_size": 804418576, "dataset_size": 836456092}, "tags": ["medical", " prescriptions"]}	2023-09-15T06:12:51+00:00	[]	[ "en" ]	TAGS #task_categories-visual-question-answering #language-English #license-unknown #medical # prescriptions #region-us	# Dataset Card for "donut_vqa_ISynHMP" More Information needed	[ "# Dataset Card for \"donut_vqa_ISynHMP\"\n\nMore Information needed" ]	[ "TAGS\n#task_categories-visual-question-answering #language-English #license-unknown #medical # prescriptions #region-us \n", "# Dataset Card for \"donut_vqa_ISynHMP\"\n\nMore Information needed" ]	[ 39, 20 ]	[ "passage: TAGS\n#task_categories-visual-question-answering #language-English #license-unknown #medical # prescriptions #region-us \n# Dataset Card for \"donut_vqa_ISynHMP\"\n\nMore Information needed" ]
7e7074252d0cf9cb9d5cf52e3f936a643581453d	# Dataset Card for "args_me_sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	nailiamirzakhmedova/args_me_10k	[ "region:us" ]	2023-09-14T10:26:41+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "argument", "dtype": "string"}, {"name": "stance", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12851963, "num_examples": 10000}], "download_size": 7839623, "dataset_size": 12851963}}	2023-09-18T11:36:19+00:00	[]	[]	TAGS #region-us	# Dataset Card for "args_me_sampled" More Information needed	[ "# Dataset Card for \"args_me_sampled\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"args_me_sampled\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"args_me_sampled\"\n\nMore Information needed" ]
e1f8ee836ef59a8116aac9bc34b9ab43e3939654	# Dataset of Kamiigusa Misaki This is the dataset of Kamiigusa Misaki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 300 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 725 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 300 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 300 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 300 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 300 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 300 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 725 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 725 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 725 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/kamiigusa_misaki_sakurasounopetnakanojo	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T10:30:23+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:17+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Kamiigusa Misaki =========================== This is the dataset of Kamiigusa Misaki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
ab49146f3c8452a43af41721e4f969abe85be546	# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble ## Dataset Description - Homepage: - Repository: https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-70B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_oh-yeontaek__llama-2-70B-LoRA-assemble", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-14T11:41:03.022396](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble/blob/main/results_2023-09-14T11-41-03.022396.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": 0.6934330265245879, "acc_stderr": 0.031312838620430335, "acc_norm": 0.697335554746802, "acc_norm_stderr": 0.03128337547678218, "mc1": 0.46511627906976744, "mc1_stderr": 0.01746084997587397, "mc2": 0.6479539766332348, "mc2_stderr": 0.014916593992436448 }, "harness\|arc:challenge\|25": { "acc": 0.6851535836177475, "acc_stderr": 0.01357265770308495, "acc_norm": 0.7184300341296929, "acc_norm_stderr": 0.013143376735009022 }, "harness\|hellaswag\|10": { "acc": 0.6707827126070504, "acc_stderr": 0.00468968597815517, "acc_norm": 0.867755427205736, "acc_norm_stderr": 0.0033806414709899157 }, "harness\|hendrycksTest-abstract_algebra\|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness\|hendrycksTest-anatomy\|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness\|hendrycksTest-astronomy\|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343603, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343603 }, "harness\|hendrycksTest-business_ethics\|5": { "acc": 0.73, "acc_stderr": 0.04461960433384741, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384741 }, "harness\|hendrycksTest-clinical_knowledge\|5": { "acc": 0.7358490566037735, "acc_stderr": 0.027134291628741702, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.027134291628741702 }, "harness\|hendrycksTest-college_biology\|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness\|hendrycksTest-college_chemistry\|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness\|hendrycksTest-college_computer_science\|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness\|hendrycksTest-college_mathematics\|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness\|hendrycksTest-college_medicine\|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness\|hendrycksTest-college_physics\|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness\|hendrycksTest-computer_security\|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness\|hendrycksTest-conceptual_physics\|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness\|hendrycksTest-econometrics\|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness\|hendrycksTest-electrical_engineering\|5": { "acc": 0.6275862068965518, "acc_stderr": 0.040287315329475576, "acc_norm": 0.6275862068965518, "acc_norm_stderr": 0.040287315329475576 }, "harness\|hendrycksTest-elementary_mathematics\|5": { "acc": 0.4656084656084656, "acc_stderr": 0.025690321762493844, "acc_norm": 0.4656084656084656, "acc_norm_stderr": 0.025690321762493844 }, "harness\|hendrycksTest-formal_logic\|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "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": 0.8290322580645161, "acc_stderr": 0.021417242936321582, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.021417242936321582 }, "harness\|hendrycksTest-high_school_chemistry\|5": { "acc": 0.5320197044334976, "acc_stderr": 0.035107665979592154, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.035107665979592154 }, "harness\|hendrycksTest-high_school_computer_science\|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "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": 0.8939393939393939, "acc_stderr": 0.021938047738853113, "acc_norm": 0.8939393939393939, "acc_norm_stderr": 0.021938047738853113 }, "harness\|hendrycksTest-high_school_government_and_politics\|5": { "acc": 0.927461139896373, "acc_stderr": 0.018718998520678178, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678178 }, "harness\|hendrycksTest-high_school_macroeconomics\|5": { "acc": 0.6948717948717948, "acc_stderr": 0.023346335293325887, "acc_norm": 0.6948717948717948, "acc_norm_stderr": 0.023346335293325887 }, "harness\|hendrycksTest-high_school_mathematics\|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness\|hendrycksTest-high_school_microeconomics\|5": { "acc": 0.773109243697479, "acc_stderr": 0.02720537153827947, "acc_norm": 0.773109243697479, "acc_norm_stderr": 0.02720537153827947 }, "harness\|hendrycksTest-high_school_physics\|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness\|hendrycksTest-high_school_psychology\|5": { "acc": 0.8862385321100917, "acc_stderr": 0.013613614800232805, "acc_norm": 0.8862385321100917, "acc_norm_stderr": 0.013613614800232805 }, "harness\|hendrycksTest-high_school_statistics\|5": { "acc": 0.5740740740740741, "acc_stderr": 0.033723432716530624, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.033723432716530624 }, "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": 0.7757847533632287, "acc_stderr": 0.02799153425851952, "acc_norm": 0.7757847533632287, "acc_norm_stderr": 0.02799153425851952 }, "harness\|hendrycksTest-human_sexuality\|5": { "acc": 0.8396946564885496, "acc_stderr": 0.03217829420744633, "acc_norm": 0.8396946564885496, "acc_norm_stderr": 0.03217829420744633 }, "harness\|hendrycksTest-international_law\|5": { "acc": 0.8512396694214877, "acc_stderr": 0.03248470083807194, "acc_norm": 0.8512396694214877, "acc_norm_stderr": 0.03248470083807194 }, "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": 0.8220858895705522, "acc_stderr": 0.03004735765580663, "acc_norm": 0.8220858895705522, "acc_norm_stderr": 0.03004735765580663 }, "harness\|hendrycksTest-machine_learning\|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness\|hendrycksTest-management\|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness\|hendrycksTest-marketing\|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "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": 0.8633461047254151, "acc_stderr": 0.012282876868629234, "acc_norm": 0.8633461047254151, "acc_norm_stderr": 0.012282876868629234 }, "harness\|hendrycksTest-moral_disputes\|5": { "acc": 0.7716763005780347, "acc_stderr": 0.022598703804321635, "acc_norm": 0.7716763005780347, "acc_norm_stderr": 0.022598703804321635 }, "harness\|hendrycksTest-moral_scenarios\|5": { "acc": 0.5743016759776536, "acc_stderr": 0.01653682964899712, "acc_norm": 0.5743016759776536, "acc_norm_stderr": 0.01653682964899712 }, "harness\|hendrycksTest-nutrition\|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness\|hendrycksTest-philosophy\|5": { "acc": 0.7556270096463023, "acc_stderr": 0.024406162094668907, "acc_norm": 0.7556270096463023, "acc_norm_stderr": 0.024406162094668907 }, "harness\|hendrycksTest-prehistory\|5": { "acc": 0.7993827160493827, "acc_stderr": 0.02228231394977488, "acc_norm": 0.7993827160493827, "acc_norm_stderr": 0.02228231394977488 }, "harness\|hendrycksTest-professional_accounting\|5": { "acc": 0.5567375886524822, "acc_stderr": 0.029634838473766006, "acc_norm": 0.5567375886524822, "acc_norm_stderr": 0.029634838473766006 }, "harness\|hendrycksTest-professional_law\|5": { "acc": 0.5645371577574967, "acc_stderr": 0.012663412101248345, "acc_norm": 0.5645371577574967, "acc_norm_stderr": 0.012663412101248345 }, "harness\|hendrycksTest-professional_medicine\|5": { "acc": 0.7426470588235294, "acc_stderr": 0.0265565194700415, "acc_norm": 0.7426470588235294, "acc_norm_stderr": 0.0265565194700415 }, "harness\|hendrycksTest-professional_psychology\|5": { "acc": 0.7418300653594772, "acc_stderr": 0.017704531653250078, "acc_norm": 0.7418300653594772, "acc_norm_stderr": 0.017704531653250078 }, "harness\|hendrycksTest-public_relations\|5": { "acc": 0.7545454545454545, "acc_stderr": 0.041220665028782855, "acc_norm": 0.7545454545454545, "acc_norm_stderr": 0.041220665028782855 }, "harness\|hendrycksTest-security_studies\|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866764, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866764 }, "harness\|hendrycksTest-sociology\|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "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": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness\|hendrycksTest-world_religions\|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness\|truthfulqa:mc\|0": { "mc1": 0.46511627906976744, "mc1_stderr": 0.01746084997587397, "mc2": 0.6479539766332348, "mc2_stderr": 0.014916593992436448 } } ``` ### 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]	open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble	[ "region:us" ]	2023-09-14T10:41:18+00:00	{"pretty_name": "Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble", "dataset_summary": "Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-70B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-14T11:41:03.022396](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble/blob/main/results_2023-09-14T11-41-03.022396.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\": 0.6934330265245879,\n \"acc_stderr\": 0.031312838620430335,\n \"acc_norm\": 0.697335554746802,\n \"acc_norm_stderr\": 0.03128337547678218,\n \"mc1\": 0.46511627906976744,\n \"mc1_stderr\": 0.01746084997587397,\n \"mc2\": 0.6479539766332348,\n \"mc2_stderr\": 0.014916593992436448\n },\n \"harness\|arc:challenge\|25\": {\n \"acc\": 0.6851535836177475,\n \"acc_stderr\": 0.01357265770308495,\n \"acc_norm\": 0.7184300341296929,\n \"acc_norm_stderr\": 0.013143376735009022\n },\n \"harness\|hellaswag\|10\": {\n \"acc\": 0.6707827126070504,\n \"acc_stderr\": 0.00468968597815517,\n \"acc_norm\": 0.867755427205736,\n \"acc_norm_stderr\": 0.0033806414709899157\n },\n \"harness\|hendrycksTest-abstract_algebra\|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness\|hendrycksTest-anatomy\|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness\|hendrycksTest-astronomy\|5\": {\n \"acc\": 0.7763157894736842,\n \"acc_stderr\": 0.03391160934343603,\n \"acc_norm\": 0.7763157894736842,\n \"acc_norm_stderr\": 0.03391160934343603\n },\n \"harness\|hendrycksTest-business_ethics\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness\|hendrycksTest-clinical_knowledge\|5\": {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.027134291628741702,\n \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.027134291628741702\n },\n \"harness\|hendrycksTest-college_biology\|5\": {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness\|hendrycksTest-college_chemistry\|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness\|hendrycksTest-college_computer_science\|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness\|hendrycksTest-college_mathematics\|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness\|hendrycksTest-college_medicine\|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness\|hendrycksTest-college_physics\|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n },\n \"harness\|hendrycksTest-computer_security\|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness\|hendrycksTest-conceptual_physics\|5\": {\n \"acc\": 0.6638297872340425,\n \"acc_stderr\": 0.030881618520676942,\n \"acc_norm\": 0.6638297872340425,\n \"acc_norm_stderr\": 0.030881618520676942\n },\n \"harness\|hendrycksTest-econometrics\|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness\|hendrycksTest-electrical_engineering\|5\": {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.040287315329475576,\n \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.040287315329475576\n },\n \"harness\|hendrycksTest-elementary_mathematics\|5\": {\n \"acc\": 0.4656084656084656,\n \"acc_stderr\": 0.025690321762493844,\n \"acc_norm\": 0.4656084656084656,\n \"acc_norm_stderr\": 0.025690321762493844\n },\n \"harness\|hendrycksTest-formal_logic\|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\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\": 0.8290322580645161,\n \"acc_stderr\": 0.021417242936321582,\n \"acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.021417242936321582\n },\n \"harness\|hendrycksTest-high_school_chemistry\|5\": {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness\|hendrycksTest-high_school_computer_science\|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\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\": 0.8939393939393939,\n \"acc_stderr\": 0.021938047738853113,\n \"acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.021938047738853113\n },\n \"harness\|hendrycksTest-high_school_government_and_politics\|5\": {\n \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n },\n \"harness\|hendrycksTest-high_school_macroeconomics\|5\": {\n \"acc\": 0.6948717948717948,\n \"acc_stderr\": 0.023346335293325887,\n \"acc_norm\": 0.6948717948717948,\n \"acc_norm_stderr\": 0.023346335293325887\n },\n \"harness\|hendrycksTest-high_school_mathematics\|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness\|hendrycksTest-high_school_microeconomics\|5\": {\n \"acc\": 0.773109243697479,\n \"acc_stderr\": 0.02720537153827947,\n \"acc_norm\": 0.773109243697479,\n \"acc_norm_stderr\": 0.02720537153827947\n },\n \"harness\|hendrycksTest-high_school_physics\|5\": {\n \"acc\": 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n },\n \"harness\|hendrycksTest-high_school_psychology\|5\": {\n \"acc\": 0.8862385321100917,\n \"acc_stderr\": 0.013613614800232805,\n \"acc_norm\": 0.8862385321100917,\n \"acc_norm_stderr\": 0.013613614800232805\n },\n \"harness\|hendrycksTest-high_school_statistics\|5\": {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.033723432716530624,\n \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.033723432716530624\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\": 0.7757847533632287,\n \"acc_stderr\": 0.02799153425851952,\n \"acc_norm\": 0.7757847533632287,\n \"acc_norm_stderr\": 0.02799153425851952\n },\n \"harness\|hendrycksTest-human_sexuality\|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744633,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744633\n },\n \"harness\|hendrycksTest-international_law\|5\": {\n \"acc\": 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\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\": 0.8220858895705522,\n \"acc_stderr\": 0.03004735765580663,\n \"acc_norm\": 0.8220858895705522,\n \"acc_norm_stderr\": 0.03004735765580663\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\": 0.03760178006026621,\n \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n },\n \"harness\|hendrycksTest-marketing\|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.020588491316092375\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\": 0.8633461047254151,\n \"acc_stderr\": 0.012282876868629234,\n \"acc_norm\": 0.8633461047254151,\n \"acc_norm_stderr\": 0.012282876868629234\n },\n \"harness\|hendrycksTest-moral_disputes\|5\": {\n \"acc\": 0.7716763005780347,\n \"acc_stderr\": 0.022598703804321635,\n \"acc_norm\": 0.7716763005780347,\n \"acc_norm_stderr\": 0.022598703804321635\n },\n \"harness\|hendrycksTest-moral_scenarios\|5\": {\n \"acc\": 0.5743016759776536,\n \"acc_stderr\": 0.01653682964899712,\n \"acc_norm\": 0.5743016759776536,\n \"acc_norm_stderr\": 0.01653682964899712\n },\n \"harness\|hendrycksTest-nutrition\|5\": {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n },\n \"harness\|hendrycksTest-philosophy\|5\": {\n \"acc\": 0.7556270096463023,\n \"acc_stderr\": 0.024406162094668907,\n \"acc_norm\": 0.7556270096463023,\n \"acc_norm_stderr\": 0.024406162094668907\n },\n \"harness\|hendrycksTest-prehistory\|5\": {\n \"acc\": 0.7993827160493827,\n \"acc_stderr\": 0.02228231394977488,\n \"acc_norm\": 0.7993827160493827,\n \"acc_norm_stderr\": 0.02228231394977488\n },\n \"harness\|hendrycksTest-professional_accounting\|5\": {\n \"acc\": 0.5567375886524822,\n \"acc_stderr\": 0.029634838473766006,\n \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.029634838473766006\n },\n \"harness\|hendrycksTest-professional_law\|5\": {\n \"acc\": 0.5645371577574967,\n \"acc_stderr\": 0.012663412101248345,\n \"acc_norm\": 0.5645371577574967,\n \"acc_norm_stderr\": 0.012663412101248345\n },\n \"harness\|hendrycksTest-professional_medicine\|5\": {\n \"acc\": 0.7426470588235294,\n \"acc_stderr\": 0.0265565194700415,\n \"acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.0265565194700415\n },\n \"harness\|hendrycksTest-professional_psychology\|5\": {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.017704531653250078,\n \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.017704531653250078\n },\n \"harness\|hendrycksTest-public_relations\|5\": {\n \"acc\": 0.7545454545454545,\n \"acc_stderr\": 0.041220665028782855,\n \"acc_norm\": 0.7545454545454545,\n \"acc_norm_stderr\": 0.041220665028782855\n },\n \"harness\|hendrycksTest-security_studies\|5\": {\n \"acc\": 0.7877551020408163,\n \"acc_stderr\": 0.026176967197866764,\n \"acc_norm\": 0.7877551020408163,\n \"acc_norm_stderr\": 0.026176967197866764\n },\n \"harness\|hendrycksTest-sociology\|5\": {\n \"acc\": 0.8805970149253731,\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\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n },\n \"harness\|hendrycksTest-virology\|5\": {\n \"acc\": 0.5240963855421686,\n \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness\|hendrycksTest-world_religions\|5\": {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n },\n \"harness\|truthfulqa:mc\|0\": {\n \"mc1\": 0.46511627906976744,\n \"mc1_stderr\": 0.01746084997587397,\n \"mc2\": 0.6479539766332348,\n \"mc2_stderr\": 0.014916593992436448\n }\n}\n```", "repo_url": "https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T11_41_03.022396", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T11-41-03.022396.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T11-41-03.022396.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T11_41_03.022396", "path": ["/details_harness\|hellaswag\|10_2023-09-14T11-41-03.022396.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-14T11-41-03.022396.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T11_41_03.022396", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T11-41-03.022396.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T11-41-03.022396.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T11-41-03.022396.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T11-41-03.022396.parquet", 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["results_2023-09-14T11-41-03.022396.parquet"]}, {"split": "latest", "path": ["results_2023-09-14T11-41-03.022396.parquet"]}]}]}	2023-09-14T10:42:18+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard. The dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-09-14T11:41:03.022396(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T11:41:03.022396(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T11:41:03.022396(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 25, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-14T11:41:03.022396(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
3f714e7148503f9ab51debc388bb664b93222cd8	# Dataset of yorita_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls) This is the dataset of yorita_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls), containing 465 images and their tags. The core tags of this character are `brown_hair, long_hair, brown_eyes, bangs, bow, very_long_hair, hair_bow, ponytail`, 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 \| 465 \| 614.23 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 465 \| 368.97 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1111 \| 777.40 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 465 \| 548.14 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1111 \| 1.04 GiB \| [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_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/yorita_yoshino_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 \| 27 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, looking_at_viewer, blush, floral_print, obi, smile, striped_kimono, wide_sleeves, white_background, upper_body, long_sleeves, ribbon, simple_background \| \| 1 \| 22 \| ![](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, detached_sleeves, looking_at_viewer, solo, smile, blush, kimono, wide_sleeves, hair_ornament, bare_shoulders, open_mouth, ribbon_trim, skirt, flower, white_background, white_thighhighs \| \| 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, hakama_skirt, looking_at_viewer, solo, blush, cherry_blossoms, floral_print, petals, pink_kimono, red_bow, wide_sleeves, long_sleeves, :d, flower, meiji_schoolgirl_uniform, open_mouth, outdoors, red_hakama \| \| 3 \| 12 \| ![](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, dress, solo, bare_shoulders, hair_flower, looking_at_viewer, smile, simple_background, blue_bow, blush, collarbone, white_background, bracelet, halterneck, high_ponytail, sidelocks \| \| 4 \| 35 \| ![](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, serafuku, solo, blush, pleated_skirt, long_sleeves, white_shirt, red_bow, smile, blue_skirt, blue_neckerchief, blue_sailor_collar, hair_ribbon, black_skirt, simple_background, white_background, parted_lips, red_ribbon \| \| 5 \| 21 \| ![](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, puffy_short_sleeves, solo, white_shirt, beret, blush, looking_at_viewer, brown_headwear, suspender_skirt, wrist_cuffs, brown_skirt, brown_bow, center_frills, smile, plaid_skirt, simple_background, white_background, collared_shirt, plaid_bow, petals, pleated_skirt \| \| 6 \| 18 \| ![](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, solo, collarbone, simple_background, navel, small_breasts, striped_bikini, blunt_bangs, jacket, white_background, shorts, smile, ribbon \| \| 7 \| 8 \| ![](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, looking_at_viewer, solo, maid_headdress, white_thighhighs, wrist_cuffs, blush, puffy_short_sleeves, frills, black_dress, breasts, detached_collar, sitting, :o, enmaided, hair_ribbon, low_ponytail, maid_apron, simple_background, underwear \| \| 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, black_leotard, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, strapless_leotard, wrist_cuffs, small_breasts, black_pantyhose, low_ponytail, red_bowtie, simple_background, smile, white_background, black_bowtie, sitting, thighband_pantyhose \| \| 9 \| 9 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| 1boy, 1girl, blush, hetero, solo_focus, penis, nipples, sex, small_breasts, open_mouth, pussy, vaginal, kimono, lying, mosaic_censoring, nude, blunt_bangs, pubic_hair, spread_legs, sweat \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| blush \| floral_print \| obi \| smile \| striped_kimono \| wide_sleeves \| white_background \| upper_body \| long_sleeves \| ribbon \| simple_background \| detached_sleeves \| kimono \| hair_ornament \| bare_shoulders \| open_mouth \| ribbon_trim \| skirt \| flower \| white_thighhighs \| hakama_skirt \| cherry_blossoms \| petals \| pink_kimono \| red_bow \| :d \| meiji_schoolgirl_uniform \| outdoors \| red_hakama \| dress \| hair_flower \| blue_bow \| collarbone \| bracelet \| halterneck \| high_ponytail \| sidelocks \| serafuku \| pleated_skirt \| white_shirt \| blue_skirt \| blue_neckerchief \| blue_sailor_collar \| hair_ribbon \| black_skirt \| parted_lips \| red_ribbon \| puffy_short_sleeves \| beret \| brown_headwear \| suspender_skirt \| wrist_cuffs \| brown_skirt \| brown_bow \| center_frills \| plaid_skirt \| collared_shirt \| plaid_bow \| navel \| small_breasts \| striped_bikini \| blunt_bangs \| jacket \| shorts \| maid_headdress \| frills \| black_dress \| breasts \| detached_collar \| sitting \| :o \| enmaided \| low_ponytail \| maid_apron \| underwear \| black_leotard \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| strapless_leotard \| black_pantyhose \| red_bowtie \| black_bowtie \| thighband_pantyhose \| 1boy \| hetero \| solo_focus \| penis \| nipples \| sex \| pussy \| vaginal \| lying \| mosaic_censoring \| nude \| pubic_hair \| spread_legs \| sweat \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------\|:---------------\|:------\|:--------\|:-----------------\|:---------------\|:-------------------\|:-------------\|:---------------\|:---------\|:--------------------\|:-------------------\|:---------\|:----------------\|:-----------------\|:-------------\|:--------------\|:--------\|:---------\|:-------------------\|:---------------\|:------------------\|:---------\|:--------------\|:----------\|:-----\|:---------------------------\|:-----------\|:-------------\|:--------\|:--------------\|:-----------\|:-------------\|:-----------\|:-------------\|:----------------\|:------------\|:-----------\|:----------------\|:--------------\|:-------------\|:-------------------\|:---------------------\|:--------------\|:--------------\|:--------------\|:-------------\|:----------------------\|:--------\|:-----------------\|:------------------\|:--------------\|:--------------\|:------------\|:----------------\|:--------------\|:-----------------\|:------------\|:--------\|:----------------\|:-----------------\|:--------------\|:---------\|:---------\|:-----------------\|:---------\|:--------------\|:----------\|:------------------\|:----------\|:-----\|:-----------\|:---------------\|:-------------\|:------------\|:----------------\|:-------------------\|:----------------\|:--------------\|:--------------------\|:------------------\|:-------------\|:---------------\|:----------------------\|:-------\|:---------\|:-------------\|:--------\|:----------\|:------\|:--------\|:----------\|:--------\|:-------------------\|:-------\|:-------------\|:--------------\|:--------\| \| 0 \| 27 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 22 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 12 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 35 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 21 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| X \| \| \| X \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 18 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| X \| X \| \| \| X \| \| \| X \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 8 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 6 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| X \| X \| \| \| \| X \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| X \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 9 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/yorita_yoshino_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T10:56:29+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T13:52:22+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of yorita\_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of yorita\_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls), containing 465 images and their tags. The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, bangs, bow, very\_long\_hair, hair\_bow, ponytail', 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
6f6e6725cd508aff37e74886ff59b7c506c956cf	# Dataset Card for "hatilsofa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sohug/hatilsofa	[ "region:us" ]	2023-09-14T10:59:56+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 549167.0, "num_examples": 11}], "download_size": 300783, "dataset_size": 549167.0}}	2023-09-14T11:03:27+00:00	[]	[]	TAGS #region-us	# Dataset Card for "hatilsofa" More Information needed	[ "# Dataset Card for \"hatilsofa\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"hatilsofa\"\n\nMore Information needed" ]	[ 6, 13 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"hatilsofa\"\n\nMore Information needed" ]
785ee99ec40cd71a3c2fb764b1b46d3f4d133513	# Dataset of Sengoku Chihiro This is the dataset of Sengoku Chihiro, containing 71 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 71 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 170 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 71 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 71 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 71 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 71 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 71 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 170 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 170 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 170 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/sengoku_chihiro_sakurasounopetnakanojo	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T11:01:29+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:21+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Sengoku Chihiro ========================== This is the dataset of Sengoku Chihiro, containing 71 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
ed3323e631e7acbf5fe49c2fdfb156b591c476aa	# Dataset of mizumoto_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls) This is the dataset of mizumoto_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls), containing 369 images and their tags. The core tags of this character are `brown_hair, long_hair, brown_eyes, bangs, 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 \| 369 \| 356.62 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 369 \| 240.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 763 \| 462.06 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 369 \| 331.41 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 763 \| 600.62 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_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/mizumoto_yukari_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 \| 11 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, solo, looking_at_viewer, simple_background, short_sleeves, white_background, blue_skirt, collared_shirt, school_uniform, white_shirt, blush, open_mouth, :d, pleated_skirt, blue_bow, blue_ribbon, closed_mouth, neck_ribbon \| \| 1 \| 8 \| ![](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, school_uniform, skirt, smile, looking_at_viewer, solo, blush, flute \| \| 2 \| 7 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, looking_at_viewer, open_mouth, smile, solo, dress, hair_ornament, necklace, blush, microphone, bare_shoulders, braid, bracelet, earrings, medium_breasts \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, looking_at_viewer, navel, smile, solo, medium_breasts, cleavage, sailor_bikini, white_bikini \| \| 4 \| 15 \| ![](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, competition_swimsuit, looking_at_viewer, medium_breasts, solo, cowboy_shot, red_one-piece_swimsuit, blush, collarbone, covered_navel, smile, white_background, simple_background, standing, pink_one-piece_swimsuit \| \| 5 \| 6 \| ![](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) \| smile, strapless_dress, wedding_dress, 1girl, bare_shoulders, necklace, solo, bridal_veil, earrings, hair_flower, looking_at_viewer, white_dress, white_gloves, blush, bouquet, collarbone, holding, open_mouth \| \| 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) \| 1boy, blush, hetero, penis, 1girl, mosaic_censoring, solo_focus, sweat, vaginal, nipples, open_mouth, pussy, white_shirt, long_sleeves, medium_breasts, clothed_sex, navel, skirt, spread_legs, heart, on_back, panties_aside, saliva, tongue_out \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| solo \| looking_at_viewer \| simple_background \| short_sleeves \| white_background \| blue_skirt \| collared_shirt \| school_uniform \| white_shirt \| blush \| open_mouth \| :d \| pleated_skirt \| blue_bow \| blue_ribbon \| closed_mouth \| neck_ribbon \| skirt \| smile \| flute \| dress \| hair_ornament \| necklace \| microphone \| bare_shoulders \| braid \| bracelet \| earrings \| medium_breasts \| navel \| cleavage \| sailor_bikini \| white_bikini \| competition_swimsuit \| cowboy_shot \| red_one-piece_swimsuit \| collarbone \| covered_navel \| standing \| pink_one-piece_swimsuit \| strapless_dress \| wedding_dress \| bridal_veil \| hair_flower \| white_dress \| white_gloves \| bouquet \| holding \| 1boy \| hetero \| penis \| mosaic_censoring \| solo_focus \| sweat \| vaginal \| nipples \| pussy \| long_sleeves \| clothed_sex \| spread_legs \| heart \| on_back \| panties_aside \| saliva \| tongue_out \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-------\|:--------------------\|:--------------------\|:----------------\|:-------------------\|:-------------\|:-----------------\|:-----------------\|:--------------\|:--------\|:-------------\|:-----\|:----------------\|:-----------\|:--------------\|:---------------\|:--------------\|:--------\|:--------\|:--------\|:--------\|:----------------\|:-----------\|:-------------\|:-----------------\|:--------\|:-----------\|:-----------\|:-----------------\|:--------\|:-----------\|:----------------\|:---------------\|:-----------------------\|:--------------\|:-------------------------\|:-------------\|:----------------\|:-----------\|:--------------------------\|:------------------\|:----------------\|:--------------\|:--------------\|:--------------\|:---------------\|:----------\|:----------\|:-------\|:---------\|:--------\|:-------------------\|:-------------\|:--------\|:----------\|:----------\|:--------\|:---------------\|:--------------\|:--------------\|:--------\|:----------\|:----------------\|:---------\|:-------------\| \| 0 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 8 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 7 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 7 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 15 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| \| \| \| \| \| \| \| X \| X \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \|	CyberHarem/mizumoto_yukari_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T11:09:05+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:41:08+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of mizumoto\_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls) ==================================================================== This is the dataset of mizumoto\_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls), containing 369 images and their tags. The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, bangs, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
555a1265868e260121f9385be2f121b6c520a211	# Dataset of Rita Ainsworth This is the dataset of Rita Ainsworth, containing 100 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). \| Name \| Images \| Download \| Description \| \|:------------\|---------:\|:------------------------------------\|:-------------------------------------------------------------------------\| \| raw \| 100 \| [Download](dataset-raw.zip) \| Raw data with meta information. \| \| raw-stage3 \| 229 \| [Download](dataset-raw-stage3.zip) \| 3-stage cropped raw data with meta information. \| \| 384x512 \| 100 \| [Download](dataset-384x512.zip) \| 384x512 aligned dataset. \| \| 512x512 \| 100 \| [Download](dataset-512x512.zip) \| 512x512 aligned dataset. \| \| 512x704 \| 100 \| [Download](dataset-512x704.zip) \| 512x704 aligned dataset. \| \| 640x640 \| 100 \| [Download](dataset-640x640.zip) \| 640x640 aligned dataset. \| \| 640x880 \| 100 \| [Download](dataset-640x880.zip) \| 640x880 aligned dataset. \| \| stage3-640 \| 229 \| [Download](dataset-stage3-640.zip) \| 3-stage cropped dataset with the shorter side not exceeding 640 pixels. \| \| stage3-800 \| 229 \| [Download](dataset-stage3-800.zip) \| 3-stage cropped dataset with the shorter side not exceeding 800 pixels. \| \| stage3-1200 \| 229 \| [Download](dataset-stage3-1200.zip) \| 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. \|	CyberHarem/rita_ainsworth_sakurasounopetnakanojo	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T11:13:55+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2023-09-17T16:37:25+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of Rita Ainsworth ========================= This is the dataset of Rita Ainsworth, containing 100 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).	[]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]	[ 44 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]
f41986eaa5b3080a6e76572279d7fadfdf354ca2	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [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]	acumplido/HF_DATASET_NAME	[ "region:us" ]	2023-09-14T11:22:31+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]}	2023-09-14T12:34:20+00:00	[]	[]	TAGS #region-us	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 8, 24, 6, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
8109b61ffa9ca499edf4ce21fa145521903909e5	# Bangumi Image Base of Edomae Elf This is the image base of bangumi Edomae Elf, we detected 16 characters, 1946 images in total. The full dataset is [here](all.zip). Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: \| # \| Images \| Download \| Preview 1 \| Preview 2 \| Preview 3 \| Preview 4 \| Preview 5 \| Preview 6 \| Preview 7 \| Preview 8 \| \|:------\|---------:\|:---------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\|:-------------------------------\| \| 0 \| 658 \| [Download](0/dataset.zip) \| ![preview 1](0/preview_1.png) \| ![preview 2](0/preview_2.png) \| ![preview 3](0/preview_3.png) \| ![preview 4](0/preview_4.png) \| ![preview 5](0/preview_5.png) \| ![preview 6](0/preview_6.png) \| ![preview 7](0/preview_7.png) \| ![preview 8](0/preview_8.png) \| \| 1 \| 20 \| [Download](1/dataset.zip) \| ![preview 1](1/preview_1.png) \| ![preview 2](1/preview_2.png) \| ![preview 3](1/preview_3.png) \| ![preview 4](1/preview_4.png) \| ![preview 5](1/preview_5.png) \| ![preview 6](1/preview_6.png) \| ![preview 7](1/preview_7.png) \| ![preview 8](1/preview_8.png) \| \| 2 \| 40 \| [Download](2/dataset.zip) \| ![preview 1](2/preview_1.png) \| ![preview 2](2/preview_2.png) \| ![preview 3](2/preview_3.png) \| ![preview 4](2/preview_4.png) \| ![preview 5](2/preview_5.png) \| ![preview 6](2/preview_6.png) \| ![preview 7](2/preview_7.png) \| ![preview 8](2/preview_8.png) \| \| 3 \| 80 \| [Download](3/dataset.zip) \| ![preview 1](3/preview_1.png) \| ![preview 2](3/preview_2.png) \| ![preview 3](3/preview_3.png) \| ![preview 4](3/preview_4.png) \| ![preview 5](3/preview_5.png) \| ![preview 6](3/preview_6.png) \| ![preview 7](3/preview_7.png) \| ![preview 8](3/preview_8.png) \| \| 4 \| 10 \| [Download](4/dataset.zip) \| ![preview 1](4/preview_1.png) \| ![preview 2](4/preview_2.png) \| ![preview 3](4/preview_3.png) \| ![preview 4](4/preview_4.png) \| ![preview 5](4/preview_5.png) \| ![preview 6](4/preview_6.png) \| ![preview 7](4/preview_7.png) \| ![preview 8](4/preview_8.png) \| \| 5 \| 6 \| [Download](5/dataset.zip) \| ![preview 1](5/preview_1.png) \| ![preview 2](5/preview_2.png) \| ![preview 3](5/preview_3.png) \| ![preview 4](5/preview_4.png) \| ![preview 5](5/preview_5.png) \| ![preview 6](5/preview_6.png) \| N/A \| N/A \| \| 6 \| 734 \| [Download](6/dataset.zip) \| ![preview 1](6/preview_1.png) \| ![preview 2](6/preview_2.png) \| ![preview 3](6/preview_3.png) \| ![preview 4](6/preview_4.png) \| ![preview 5](6/preview_5.png) \| ![preview 6](6/preview_6.png) \| ![preview 7](6/preview_7.png) \| ![preview 8](6/preview_8.png) \| \| 7 \| 79 \| [Download](7/dataset.zip) \| ![preview 1](7/preview_1.png) \| ![preview 2](7/preview_2.png) \| ![preview 3](7/preview_3.png) \| ![preview 4](7/preview_4.png) \| ![preview 5](7/preview_5.png) \| ![preview 6](7/preview_6.png) \| ![preview 7](7/preview_7.png) \| ![preview 8](7/preview_8.png) \| \| 8 \| 14 \| [Download](8/dataset.zip) \| ![preview 1](8/preview_1.png) \| ![preview 2](8/preview_2.png) \| ![preview 3](8/preview_3.png) \| ![preview 4](8/preview_4.png) \| ![preview 5](8/preview_5.png) \| ![preview 6](8/preview_6.png) \| ![preview 7](8/preview_7.png) \| ![preview 8](8/preview_8.png) \| \| 9 \| 13 \| [Download](9/dataset.zip) \| ![preview 1](9/preview_1.png) \| ![preview 2](9/preview_2.png) \| ![preview 3](9/preview_3.png) \| ![preview 4](9/preview_4.png) \| ![preview 5](9/preview_5.png) \| ![preview 6](9/preview_6.png) \| ![preview 7](9/preview_7.png) \| ![preview 8](9/preview_8.png) \| \| 10 \| 94 \| [Download](10/dataset.zip) \| ![preview 1](10/preview_1.png) \| ![preview 2](10/preview_2.png) \| ![preview 3](10/preview_3.png) \| ![preview 4](10/preview_4.png) \| ![preview 5](10/preview_5.png) \| ![preview 6](10/preview_6.png) \| ![preview 7](10/preview_7.png) \| ![preview 8](10/preview_8.png) \| \| 11 \| 58 \| [Download](11/dataset.zip) \| ![preview 1](11/preview_1.png) \| ![preview 2](11/preview_2.png) \| ![preview 3](11/preview_3.png) \| ![preview 4](11/preview_4.png) \| ![preview 5](11/preview_5.png) \| ![preview 6](11/preview_6.png) \| ![preview 7](11/preview_7.png) \| ![preview 8](11/preview_8.png) \| \| 12 \| 32 \| [Download](12/dataset.zip) \| ![preview 1](12/preview_1.png) \| ![preview 2](12/preview_2.png) \| ![preview 3](12/preview_3.png) \| ![preview 4](12/preview_4.png) \| ![preview 5](12/preview_5.png) \| ![preview 6](12/preview_6.png) \| ![preview 7](12/preview_7.png) \| ![preview 8](12/preview_8.png) \| \| 13 \| 21 \| [Download](13/dataset.zip) \| ![preview 1](13/preview_1.png) \| ![preview 2](13/preview_2.png) \| ![preview 3](13/preview_3.png) \| ![preview 4](13/preview_4.png) \| ![preview 5](13/preview_5.png) \| ![preview 6](13/preview_6.png) \| ![preview 7](13/preview_7.png) \| ![preview 8](13/preview_8.png) \| \| 14 \| 11 \| [Download](14/dataset.zip) \| ![preview 1](14/preview_1.png) \| ![preview 2](14/preview_2.png) \| ![preview 3](14/preview_3.png) \| ![preview 4](14/preview_4.png) \| ![preview 5](14/preview_5.png) \| ![preview 6](14/preview_6.png) \| ![preview 7](14/preview_7.png) \| ![preview 8](14/preview_8.png) \| \| noise \| 76 \| [Download](-1/dataset.zip) \| ![preview 1](-1/preview_1.png) \| ![preview 2](-1/preview_2.png) \| ![preview 3](-1/preview_3.png) \| ![preview 4](-1/preview_4.png) \| ![preview 5](-1/preview_5.png) \| ![preview 6](-1/preview_6.png) \| ![preview 7](-1/preview_7.png) \| ![preview 8](-1/preview_8.png) \|	BangumiBase/edomaeelf	[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]	2023-09-14T11:37:02+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}	2023-09-29T06:50:59+00:00	[]	[]	TAGS #size_categories-1K<n<10K #license-mit #art #region-us	Bangumi Image Base of Edomae Elf ================================ This is the image base of bangumi Edomae Elf, we detected 16 characters, 1946 images in total. The full dataset is here. Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview:	[]	[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]	[ 25 ]	[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]
495a68c6ec4c030effcf0f5580e353274a577d21	A RL environment called AirHockey for the Godot Game Engine. This environment was created with: https://github.com/edbeeching/godot_rl_agents ## Downloading the environment After installing Godot RL Agents, download the environment with: ``` gdrl.env_from_hub -r edbeeching/godot_rl_AirHockey ```	edbeeching/godot_rl_AirHockey	[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]	2023-09-14T11:42:36+00:00	{"library_name": "godot-rl", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games"]}	2024-01-07T09:46:34+00:00	[]	[]	TAGS #deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us	A RL environment called AirHockey for the Godot Game Engine. This environment was created with: URL ## Downloading the environment After installing Godot RL Agents, download the environment with:	[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]	[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]	[ 32, 20 ]	[ "passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]
1a8d171aa2c218743c4b474e3627939b6b549c2c	# Dataset Card for "pooling_net_embeddings_dim_16" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	johannes-garstenauer/pooling_net_embeddings_dim_16	[ "region:us" ]	2023-09-14T11:50:02+00:00	{"dataset_info": {"features": [{"name": "last_cls", "sequence": "float32"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3800, "num_examples": 50}], "download_size": 5640, "dataset_size": 3800}}	2023-09-14T11:50:04+00:00	[]	[]	TAGS #region-us	# Dataset Card for "pooling_net_embeddings_dim_16" More Information needed	[ "# Dataset Card for \"pooling_net_embeddings_dim_16\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"pooling_net_embeddings_dim_16\"\n\nMore Information needed" ]	[ 6, 22 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"pooling_net_embeddings_dim_16\"\n\nMore Information needed" ]
35dc1964537f71a977092d10835bbfb7c8a5a09e	# 学習データセット OpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。このデータセットは、LINE社のInstruction Tuningに利用されている。	ToPo-ToPo/oasst1-89k-ja	[ "region:us" ]	2023-09-14T11:52:11+00:00	{}	2023-09-26T00:14:46+00:00	[]	[]	TAGS #region-us	# 学習データセット OpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。このデータセットは、LINE社のInstruction Tuningに利用されている。	[ "# 学習データセット\nOpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。\nこのデータセットは、LINE社のInstruction Tuningに利用されている。" ]	[ "TAGS\n#region-us \n", "# 学習データセット\nOpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。\nこのデータセットは、LINE社のInstruction Tuningに利用されている。" ]	[ 6, 55 ]	[ "passage: TAGS\n#region-us \n# 学習データセット\nOpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。\nこのデータセットは、LINE社のInstruction Tuningに利用されている。" ]
d223b6f631053d6b8a896c78a37ad49d94ed54b3	# Dataset Card for "prs-v2-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	loubnabnl/prs-v2-sample	[ "region:us" ]	2023-09-14T11:55:08+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "pull_request.guid", "dtype": "string"}, {"name": "pull_request.code_review_events", "dtype": "string"}, {"name": "pull_request.events", "dtype": "string"}, {"name": "pull_request.issue_events", "dtype": "string"}, {"name": "bucket", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 201909231, "num_examples": 10000}], "download_size": 38860265, "dataset_size": 201909231}}	2023-09-14T11:55:12+00:00	[]	[]	TAGS #region-us	# Dataset Card for "prs-v2-sample" More Information needed	[ "# Dataset Card for \"prs-v2-sample\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"prs-v2-sample\"\n\nMore Information needed" ]	[ 6, 17 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"prs-v2-sample\"\n\nMore Information needed" ]
14bcf5d0ebd2ba94e2e286e3912f91cd21c5f07d	# Dataset Card for "structs_token_size_4_reduced_labelled_train" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the training dataset. Validation set at: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_eval Data structures and labels are extracted from: https://zenodo.org/records/6537904 Thesis and associated scripts: https://zenodo.org/records/10053730	johannes-garstenauer/structs_token_size_4_reduced_labelled_train	[ "region:us" ]	2023-09-14T12:12:00+00:00	{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 372362495.3356041, "num_examples": 1518855}], "download_size": 138213330, "dataset_size": 372362495.3356041}}	2023-10-30T13:26:23+00:00	[]	[]	TAGS #region-us	# Dataset Card for "structs_token_size_4_reduced_labelled_train" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the training dataset. Validation set at: URL Data structures and labels are extracted from: URL Thesis and associated scripts: URL	[ "# Dataset Card for \"structs_token_size_4_reduced_labelled_train\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the training dataset. Validation set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"structs_token_size_4_reduced_labelled_train\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the training dataset. Validation set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]	[ 6, 110 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"structs_token_size_4_reduced_labelled_train\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the training dataset. Validation set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]
67bc56c60548f0c764e7c7f83be8d10d1ec5b716	As of 2019.2.7	shaowenchen/wiki_zh	[ "region:us" ]	2023-09-14T12:35:14+00:00	{}	2023-09-15T04:43:18+00:00	[]	[]	TAGS #region-us	As of 2019.2.7	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
8158a98771bae3d80213240e67d47901c328136f	# Dataset Card for "celeba_attr_feliu" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fformosa/celeba_attr_feliu	[ "region:us" ]	2023-09-14T12:35:38+00:00	{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "attributes", "struct": [{"name": "5_o_Clock_Shadow", "dtype": "string"}, {"name": "Arched_Eyebrows", "dtype": "string"}, {"name": "Attractive", "dtype": "string"}, {"name": "Bags_Under_Eyes", "dtype": "string"}, {"name": "Bald", "dtype": "string"}, {"name": "Bangs", "dtype": "string"}, {"name": "Big_Lips", "dtype": "string"}, {"name": "Big_Nose", "dtype": "string"}, {"name": "Black_Hair", "dtype": "string"}, {"name": "Blond_Hair", "dtype": "string"}, {"name": "Blurry", "dtype": "string"}, {"name": "Brown_Hair", "dtype": "string"}, {"name": "Bushy_Eyebrows", "dtype": "string"}, {"name": "Chubby", "dtype": "string"}, {"name": "Double_Chin", "dtype": "string"}, {"name": "Eyeglasses", "dtype": "string"}, {"name": "Goatee", "dtype": "string"}, {"name": "Gray_Hair", "dtype": "string"}, {"name": "Heavy_Makeup", "dtype": "string"}, {"name": "High_Cheekbones", "dtype": "string"}, {"name": "Male", "dtype": "string"}, {"name": "Mouth_Slightly_Open", "dtype": "string"}, {"name": "Mustache", "dtype": "string"}, {"name": "Narrow_Eyes", "dtype": "string"}, {"name": "No_Beard", "dtype": "string"}, {"name": "Oval_Face", "dtype": "string"}, {"name": "Pale_Skin", "dtype": "string"}, {"name": "Pointy_Nose", "dtype": "string"}, {"name": "Receding_Hairline", "dtype": "string"}, {"name": "Rosy_Cheeks", "dtype": "string"}, {"name": "Sideburns", "dtype": "string"}, {"name": "Smiling", "dtype": "string"}, {"name": "Straight_Hair", "dtype": "string"}, {"name": "Wavy_Hair", "dtype": "string"}, {"name": "Wearing_Earrings", "dtype": "string"}, {"name": "Wearing_Hat", "dtype": "string"}, {"name": "Wearing_Lipstick", "dtype": "string"}, {"name": "Wearing_Necklace", "dtype": "string"}, {"name": "Wearing_Necktie", "dtype": "string"}, {"name": "Young", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1456173097.427, "num_examples": 202599}], "download_size": 1410042939, "dataset_size": 1456173097.427}}	2023-09-14T13:27:11+00:00	[]	[]	TAGS #region-us	# Dataset Card for "celeba_attr_feliu" More Information needed	[ "# Dataset Card for \"celeba_attr_feliu\"\n\nMore Information needed" ]	[ "TAGS\n#region-us \n", "# Dataset Card for \"celeba_attr_feliu\"\n\nMore Information needed" ]	[ 6, 18 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for \"celeba_attr_feliu\"\n\nMore Information needed" ]
b2ace1d80cdf90d2a6437d4ba288e122768a1c80	# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E ## Dataset Description - Homepage: - Repository: https://huggingface.co/wei123602/llama2-13b-fintune2-4E - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-fintune2-4E](https://huggingface.co/wei123602/llama2-13b-fintune2-4E) 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_wei123602__llama2-13b-fintune2-4E", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T08:37:29.290046](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E/blob/main/results_2023-10-23T08-37-29.290046.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": 0.33913590604026844, "em_stderr": 0.004848223319148492, "f1": 0.3781501677852353, "f1_stderr": 0.004773695048987946, "acc": 0.42001695814855794, "acc_stderr": 0.01052750062588995 }, "harness\|drop\|3": { "em": 0.33913590604026844, "em_stderr": 0.004848223319148492, "f1": 0.3781501677852353, "f1_stderr": 0.004773695048987946 }, "harness\|gsm8k\|5": { "acc": 0.10917361637604246, "acc_stderr": 0.00859008930051116 }, "harness\|winogrande\|5": { "acc": 0.7308602999210734, "acc_stderr": 0.012464911951268738 } } ``` ### 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]	open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E	[ "region:us" ]	2023-09-14T12:46:07+00:00	{"pretty_name": "Evaluation run of wei123602/llama2-13b-fintune2-4E", "dataset_summary": "Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-fintune2-4E](https://huggingface.co/wei123602/llama2-13b-fintune2-4E) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T08:37:29.290046](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E/blob/main/results_2023-10-23T08-37-29.290046.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\": 0.33913590604026844,\n \"em_stderr\": 0.004848223319148492,\n \"f1\": 0.3781501677852353,\n \"f1_stderr\": 0.004773695048987946,\n \"acc\": 0.42001695814855794,\n \"acc_stderr\": 0.01052750062588995\n },\n \"harness\|drop\|3\": {\n \"em\": 0.33913590604026844,\n \"em_stderr\": 0.004848223319148492,\n \"f1\": 0.3781501677852353,\n \"f1_stderr\": 0.004773695048987946\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.10917361637604246,\n \"acc_stderr\": 0.00859008930051116\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7308602999210734,\n \"acc_stderr\": 0.012464911951268738\n }\n}\n```", "repo_url": "https://huggingface.co/wei123602/llama2-13b-fintune2-4E", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T08_37_29.290046", "path": ["/details_harness\|drop\|3_2023-10-23T08-37-29.290046.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-23T08-37-29.290046.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T08_37_29.290046", "path": ["/details_harness\|gsm8k\|5_2023-10-23T08-37-29.290046.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-23T08-37-29.290046.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hellaswag\|10_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": 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"/details_harness\|hendrycksTest-high_school_microeconomics\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-high_school_physics\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-high_school_psychology\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-high_school_statistics\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-high_school_us_history\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-high_school_world_history\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-human_aging\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-human_sexuality\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-international_law\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-jurisprudence\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-logical_fallacies\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-machine_learning\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-management\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-marketing\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-medical_genetics\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-miscellaneous\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-moral_disputes\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-moral_scenarios\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-nutrition\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-45-51.161008.parquet", "/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_abstract_algebra_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_anatomy_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_astronomy_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_business_ethics_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_clinical_knowledge_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_biology_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_chemistry_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_computer_science_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": "harness_hendrycksTest_college_mathematics_5", "data_files": [{"split": "2023_09_14T13_45_51.161008", "path": ["/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-45-51.161008.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-45-51.161008.parquet"]}]}, {"config_name": 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clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-23T08:37:29.290046(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T08:37:29.290046(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T08:37:29.290046(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 25, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T08:37:29.290046(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
701b1e0fde0ba57091540d5fdc8b77a33b491edf	# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST ## Dataset Description - Homepage: - Repository: https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST) 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_wei123602__llama2-13b-FINETUNE3_TEST", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T13:49:39.272665](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST/blob/main/results_2023-10-24T13-49-39.272665.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": 0.21885486577181207, "em_stderr": 0.004234319461313102, "f1": 0.2578481543624162, "f1_stderr": 0.004224663408638886, "acc": 0.45241934856534904, "acc_stderr": 0.010864913504975222 }, "harness\|drop\|3": { "em": 0.21885486577181207, "em_stderr": 0.004234319461313102, "f1": 0.2578481543624162, "f1_stderr": 0.004224663408638886 }, "harness\|gsm8k\|5": { "acc": 0.14556482183472327, "acc_stderr": 0.009714267797726266 }, "harness\|winogrande\|5": { "acc": 0.7592738752959748, "acc_stderr": 0.01201555921222418 } } ``` ### 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]	open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST	[ "region:us" ]	2023-09-14T12:48:42+00:00	{"pretty_name": "Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST", "dataset_summary": "Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T13:49:39.272665](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST/blob/main/results_2023-10-24T13-49-39.272665.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\": 0.21885486577181207,\n \"em_stderr\": 0.004234319461313102,\n \"f1\": 0.2578481543624162,\n \"f1_stderr\": 0.004224663408638886,\n \"acc\": 0.45241934856534904,\n \"acc_stderr\": 0.010864913504975222\n },\n \"harness\|drop\|3\": {\n \"em\": 0.21885486577181207,\n \"em_stderr\": 0.004234319461313102,\n \"f1\": 0.2578481543624162,\n \"f1_stderr\": 0.004224663408638886\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.14556482183472327,\n \"acc_stderr\": 0.009714267797726266\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.01201555921222418\n }\n}\n```", "repo_url": "https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T13_49_39.272665", "path": ["/details_harness\|drop\|3_2023-10-24T13-49-39.272665.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-24T13-49-39.272665.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T13_49_39.272665", "path": ["/details_harness\|gsm8k\|5_2023-10-24T13-49-39.272665.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-24T13-49-39.272665.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hellaswag\|10_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hellaswag\|10_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-abstract_algebra\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-anatomy\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-astronomy\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-business_ethics\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-clinical_knowledge\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-college_biology\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-college_chemistry\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-college_computer_science\|5_2023-09-14T13-48-26.265439.parquet", "/details_harness\|hendrycksTest-college_mathematics\|5_2023-09-14T13-48-26.265439.parquet", 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["/details_harness\|hendrycksTest-philosophy\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-prehistory\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_accounting\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_law\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_medicine\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-professional_psychology\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-public_relations\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-security_studies\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-sociology\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-us_foreign_policy\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-virology\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|hendrycksTest-world_religions\|5_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T13-48-26.265439.parquet"]}, {"split": "latest", "path": ["/details_harness\|truthfulqa:mc\|0_2023-09-14T13-48-26.265439.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T13_49_39.272665", "path": ["/details_harness\|winogrande\|5_2023-10-24T13-49-39.272665.parquet"]}, {"split": "latest", "path": ["/details_harness\|winogrande\|5_2023-10-24T13-49-39.272665.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T13_48_26.265439", "path": ["results_2023-09-14T13-48-26.265439.parquet"]}, {"split": "2023_10_24T13_49_39.272665", "path": ["results_2023-10-24T13-49-39.272665.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T13-49-39.272665.parquet"]}]}]}	2023-10-24T12:49:53+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-24T13:49:39.272665(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:49:39.272665(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:49:39.272665(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 27, 31, 175, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T13:49:39.272665(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
c9086f8da87902e4a469e60d9ee64b8d1a25f4ae	# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 ## Dataset Description - Homepage: - Repository: https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2 - Paper: - Leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - Point of Contact: [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884, "acc": 0.4441419290522286, "acc_stderr": 0.010548755752104734 }, "harness\|drop\|3": { "em": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884 }, "harness\|gsm8k\|5": { "acc": 0.12585291887793784, "acc_stderr": 0.009136212598406319 }, "harness\|winogrande\|5": { "acc": 0.7624309392265194, "acc_stderr": 0.01196129890580315 } } ``` ### 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]	open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2	[ "region:us" ]	2023-09-14T12:51:50+00:00	{"pretty_name": "Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2", "dataset_summary": "Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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\": 0.2633179530201342,\n \"em_stderr\": 0.004510450588757746,\n \"f1\": 0.3047556627516783,\n \"f1_stderr\": 0.004459334625484884,\n \"acc\": 0.4441419290522286,\n \"acc_stderr\": 0.010548755752104734\n },\n \"harness\|drop\|3\": {\n \"em\": 0.2633179530201342,\n \"em_stderr\": 0.004510450588757746,\n \"f1\": 0.3047556627516783,\n \"f1_stderr\": 0.004459334625484884\n },\n \"harness\|gsm8k\|5\": {\n \"acc\": 0.12585291887793784,\n \"acc_stderr\": 0.009136212598406319\n },\n \"harness\|winogrande\|5\": {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.01196129890580315\n }\n}\n```", "repo_url": "https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T13_51_34.438102", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T13-51-34.438102.parquet"]}, {"split": "latest", "path": ["/details_harness\|arc:challenge\|25_2023-09-14T13-51-34.438102.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T06_56_58.916586", "path": ["/details_harness\|drop\|3_2023-10-28T06-56-58.916586.parquet"]}, {"split": "2023_10_28T09_53_17.709619", "path": ["/details_harness\|drop\|3_2023-10-28T09-53-17.709619.parquet"]}, {"split": "latest", "path": ["/details_harness\|drop\|3_2023-10-28T09-53-17.709619.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T06_56_58.916586", "path": ["/details_harness\|gsm8k\|5_2023-10-28T06-56-58.916586.parquet"]}, {"split": "2023_10_28T09_53_17.709619", "path": ["/details_harness\|gsm8k\|5_2023-10-28T09-53-17.709619.parquet"]}, {"split": "latest", "path": ["/details_harness\|gsm8k\|5_2023-10-28T09-53-17.709619.parquet"]}]}, {"config_name": 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["/details_harness\|winogrande\|5_2023-10-28T09-53-17.709619.parquet"]}, {"split": "latest", "path": ["**/details_harness\|winogrande\|5_2023-10-28T09-53-17.709619.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T13_51_34.438102", "path": ["results_2023-09-14T13-51-34.438102.parquet"]}, {"split": "2023_10_28T06_56_58.916586", "path": ["results_2023-10-28T06-56-58.916586.parquet"]}, {"split": "2023_10_28T09_53_17.709619", "path": ["results_2023-10-28T09-53-17.709619.parquet"]}, {"split": "latest", "path": ["results_2023-10-28T09-53-17.709619.parquet"]}]}]}	2023-10-28T08:53:25+00:00	[]	[]	TAGS #region-us	# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard. The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-28T09:53:17.709619(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions	[ "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T09:53:17.709619(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T09:53:17.709619(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]	[ 6, 28, 31, 176, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]	[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T09:53:17.709619(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]
a1a8f3821be022cf59f7b3038bc1d95d092b9694	# Dataset of takafuji_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls) This is the dataset of takafuji_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls), containing 341 images and their tags. The core tags of this character are `black_hair, short_hair, breasts, yellow_eyes, bangs, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 341 \| 446.15 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 341 \| 259.42 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 830 \| 551.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 341 \| 398.92 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 830 \| 772.03 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_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/takafuji_kako_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 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, cleavage, looking_at_viewer, necklace, smile, solo, navel, open_mouth, blush, frilled_bikini, outdoors, bracelet, collarbone, day, hair_flower, medium_breasts, beach, blue_bikini, floral_print, front-tie_top, side-tie_bikini_bottom \| \| 1 \| 18 \| ![](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) \| kimono, smile, 1girl, hair_flower, looking_at_viewer, solo, blush, obi, floral_print, upper_body \| \| 2 \| 34 \| ![](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, solo, smile, hair_bow, looking_at_viewer, detached_sleeves, cleavage, medium_breasts, navel, blush, midriff, bare_shoulders, open_mouth, japanese_clothes \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, blush, collarbone, looking_at_viewer, navel, nipples, solo, completely_nude, brown_eyes, cowboy_shot, medium_breasts, simple_background, smile, white_background \| \| 4 \| 7 \| ![](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) \| cleavage, detached_collar, playboy_bunny, rabbit_ears, 1girl, brown_eyes, wrist_cuffs, bowtie, looking_at_viewer, rabbit_tail, smile, solo, open_mouth, strapless_leotard, black_leotard, black_pantyhose, cowboy_shot, fake_animal_ears, fishnets, white_background \| \| 5 \| 6 \| ![](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, hetero, solo_focus, nipples, smile, breast_grab, collarbone, grabbing, looking_at_viewer, nude, penis, pov, censored, cum_on_body, male_pubic_hair, on_back, open_mouth, paizuri, sweat \| \| 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, onsen, solo, blush, looking_at_viewer, naked_towel, smile, collarbone, full_moon, water, closed_mouth, medium_breasts, night_sky, nipples, ponytail, snow, wet \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| cleavage \| looking_at_viewer \| necklace \| smile \| solo \| navel \| open_mouth \| blush \| frilled_bikini \| outdoors \| bracelet \| collarbone \| day \| hair_flower \| medium_breasts \| beach \| blue_bikini \| floral_print \| front-tie_top \| side-tie_bikini_bottom \| kimono \| obi \| upper_body \| hair_bow \| detached_sleeves \| midriff \| bare_shoulders \| japanese_clothes \| nipples \| completely_nude \| brown_eyes \| cowboy_shot \| simple_background \| white_background \| detached_collar \| playboy_bunny \| rabbit_ears \| wrist_cuffs \| bowtie \| rabbit_tail \| strapless_leotard \| black_leotard \| black_pantyhose \| fake_animal_ears \| fishnets \| 1boy \| hetero \| solo_focus \| breast_grab \| grabbing \| nude \| penis \| pov \| censored \| cum_on_body \| male_pubic_hair \| on_back \| paizuri \| sweat \| onsen \| naked_towel \| full_moon \| water \| closed_mouth \| night_sky \| ponytail \| snow \| wet \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:-----------\|:--------------------\|:-----------\|:--------\|:-------\|:--------\|:-------------\|:--------\|:-----------------\|:-----------\|:-----------\|:-------------\|:------\|:--------------\|:-----------------\|:--------\|:--------------\|:---------------\|:----------------\|:-------------------------\|:---------\|:------\|:-------------\|:-----------\|:-------------------\|:----------\|:-----------------\|:-------------------\|:----------\|:------------------\|:-------------\|:--------------\|:--------------------\|:-------------------\|:------------------\|:----------------\|:--------------\|:--------------\|:---------\|:--------------\|:--------------------\|:----------------\|:------------------\|:-------------------\|:-----------\|:-------\|:---------\|:-------------\|:--------------\|:-----------\|:-------\|:--------\|:------\|:-----------\|:--------------\|:------------------\|:----------\|:----------\|:--------\|:--------\|:--------------\|:------------\|:--------\|:---------------\|:------------\|:-----------\|:-------\|:------\| \| 0 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 18 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 34 \| ![](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 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| \| X \| \| X \| X \| X \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 7 \| ![](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 \| 6 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| \| X \| \| X \| \| \| X \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| 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 \|	CyberHarem/takafuji_kako_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T12:52:28+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T19:09:29+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of takafuji\_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of takafuji\_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls), containing 341 images and their tags. The core tags of this character are 'black\_hair, short\_hair, breasts, yellow\_eyes, bangs, large\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
7d6407ebe4e8b760e0222038d1dab562e1d345b7	# Dataset of mukai_takumi (THE iDOLM@STER: Cinderella Girls) This is the dataset of mukai_takumi (THE iDOLM@STER: Cinderella Girls), containing 467 images and their tags. The core tags of this character are `breasts, long_hair, black_hair, large_breasts, brown_hair, green_eyes, brown_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 \| 467 \| 541.96 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 467 \| 323.11 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 1087 \| 656.63 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 467 \| 484.29 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 1087 \| 916.37 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_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/mukai_takumi_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 \| 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, blush, cleavage, navel, solo, side-tie_bikini_bottom, looking_at_viewer, simple_background, white_background, yellow_eyes, open_clothes \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, looking_at_viewer, simple_background, solo, white_background, cleavage, upper_body, grin, jacket, sarashi, collarbone, hand_on_hip, open_clothes \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| 1girl, blush, cleavage, dress, earrings, necklace, looking_at_viewer, solo, bare_shoulders, smile, collarbone, ponytail, sideboob \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| 1girl, cleavage, looking_at_viewer, open_jacket, ponytail, smile, solo, black_skirt, crop_top, earrings, midriff, miniskirt, navel, necklace, blush, bracelet, collarbone, hand_on_hip, parted_bangs, suspender_skirt, tattoo, thighs, white_jacket, black_choker, closed_mouth, cropped_jacket, hair_flower, idol, sidelocks, thigh_strap, white_background, white_belt, white_gloves \| \| 4 \| 11 \| ![](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) \| 1boy, 1girl, blush, hetero, solo_focus, sweat, nipples, nude, penis, huge_breasts, mosaic_censoring, open_mouth, vaginal, dark-skinned_male, sex_from_behind \| \| 5 \| 15 \| ![](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, hetero, solo_focus, nipples, blush, paizuri, sweat, cum_on_breasts, huge_breasts, penis, censored, pov, ejaculation, smile, teeth, breasts_squeezed_together, looking_at_viewer \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| 1boy, 1girl, blush, hetero, nipples, solo_focus, sex, cum_in_pussy, sweat, vaginal, navel, open_mouth, completely_nude, pov, spread_legs, bar_censor, cowgirl_position, female_pubic_hair, girl_on_top, huge_breasts, penis \| \| 7 \| 8 \| ![](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, playboy_bunny, rabbit_ears, solo, blush, fake_animal_ears, wrist_cuffs, cleavage, bowtie, looking_at_viewer, rabbit_tail, bangs, bare_shoulders, covered_navel, strapless_leotard, anger_vein, black_leotard, cowboy_shot, fishnet_pantyhose, grin, open_mouth \| \| 8 \| 5 \| ![](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, blush, red_neckerchief, looking_at_viewer, simple_background, solo, white_background, bangs, black_sailor_collar, black_serafuku, black_skirt, collarbone, covering_mouth, crying_with_eyes_open, pleated_skirt, short_sleeves, sitting, upper_body, white_shirt, yellow_eyes \| \| 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) \| maid_apron, blush, looking_at_viewer, 1girl, black_dress, enmaided, maid_headdress, simple_background, solo, white_apron, white_background, bangs, black_footwear, frilled_apron, full_body, juliet_sleeves, shoes, sidelocks, smile, standing \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| cleavage \| navel \| solo \| side-tie_bikini_bottom \| looking_at_viewer \| simple_background \| white_background \| yellow_eyes \| open_clothes \| upper_body \| grin \| jacket \| sarashi \| collarbone \| hand_on_hip \| dress \| earrings \| necklace \| bare_shoulders \| smile \| ponytail \| sideboob \| open_jacket \| black_skirt \| crop_top \| midriff \| miniskirt \| bracelet \| parted_bangs \| suspender_skirt \| tattoo \| thighs \| white_jacket \| black_choker \| closed_mouth \| cropped_jacket \| hair_flower \| idol \| sidelocks \| thigh_strap \| white_belt \| white_gloves \| 1boy \| hetero \| solo_focus \| sweat \| nipples \| nude \| penis \| huge_breasts \| mosaic_censoring \| open_mouth \| vaginal \| dark-skinned_male \| sex_from_behind \| paizuri \| cum_on_breasts \| censored \| pov \| ejaculation \| teeth \| breasts_squeezed_together \| sex \| cum_in_pussy \| completely_nude \| spread_legs \| bar_censor \| cowgirl_position \| female_pubic_hair \| girl_on_top \| detached_collar \| playboy_bunny \| rabbit_ears \| fake_animal_ears \| wrist_cuffs \| bowtie \| rabbit_tail \| bangs \| covered_navel \| strapless_leotard \| anger_vein \| black_leotard \| cowboy_shot \| fishnet_pantyhose \| red_neckerchief \| black_sailor_collar \| black_serafuku \| covering_mouth \| crying_with_eyes_open \| pleated_skirt \| short_sleeves \| sitting \| white_shirt \| maid_apron \| black_dress \| enmaided \| maid_headdress \| white_apron \| black_footwear \| frilled_apron \| full_body \| juliet_sleeves \| shoes \| standing \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-----------\|:--------\|:-------\|:-------------------------\|:--------------------\|:--------------------\|:-------------------\|:--------------\|:---------------\|:-------------\|:-------\|:---------\|:----------\|:-------------\|:--------------\|:--------\|:-----------\|:-----------\|:-----------------\|:--------\|:-----------\|:-----------\|:--------------\|:--------------\|:-----------\|:----------\|:------------\|:-----------\|:---------------\|:------------------\|:---------\|:---------\|:---------------\|:---------------\|:---------------\|:-----------------\|:--------------\|:-------\|:------------\|:--------------\|:-------------\|:---------------\|:-------\|:---------\|:-------------\|:--------\|:----------\|:-------\|:--------\|:---------------\|:-------------------\|:-------------\|:----------\|:--------------------\|:------------------\|:----------\|:-----------------\|:-----------\|:------\|:--------------\|:--------\|:----------------------------\|:------\|:---------------\|:------------------\|:--------------\|:-------------\|:-------------------\|:--------------------\|:--------------\|:------------------\|:----------------\|:--------------\|:-------------------\|:--------------\|:---------\|:--------------\|:--------\|:----------------\|:--------------------\|:-------------\|:----------------\|:--------------\|:--------------------\|:------------------\|:----------------------\|:-----------------\|:-----------------\|:------------------------\|:----------------\|:----------------\|:----------\|:--------------\|:-------------\|:--------------\|:-----------\|:-----------------\|:--------------\|:-----------------\|:----------------\|:------------\|:-----------------\|:--------\|:-----------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 5 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| \| X \| \| X \| \| X \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 6 \| ![](samples/2/clu2-sample0.png) \| ![](samples/2/clu2-sample1.png) \| ![](samples/2/clu2-sample2.png) \| ![](samples/2/clu2-sample3.png) \| ![](samples/2/clu2-sample4.png) \| X \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| \| \| X \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 3 \| 6 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| \| X \| \| X \| \| \| \| \| \| \| X \| X \| \| X \| X \| \| X \| X \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 11 \| ![](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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 15 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| X \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 6 \| 10 \| ![](samples/6/clu6-sample0.png) \| ![](samples/6/clu6-sample1.png) \| ![](samples/6/clu6-sample2.png) \| ![](samples/6/clu6-sample3.png) \| ![](samples/6/clu6-sample4.png) \| X \| X \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| \| X \| X \| \| X \| X \| \| \| \| \| \| X \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 7 \| 8 \| ![](samples/7/clu7-sample0.png) \| ![](samples/7/clu7-sample1.png) \| ![](samples/7/clu7-sample2.png) \| ![](samples/7/clu7-sample3.png) \| ![](samples/7/clu7-sample4.png) \| X \| X \| X \| \| X \| \| X \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 8 \| 5 \| ![](samples/8/clu8-sample0.png) \| ![](samples/8/clu8-sample1.png) \| ![](samples/8/clu8-sample2.png) \| ![](samples/8/clu8-sample3.png) \| ![](samples/8/clu8-sample4.png) \| X \| X \| \| \| X \| \| X \| X \| X \| X \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| 9 \| 6 \| ![](samples/9/clu9-sample0.png) \| ![](samples/9/clu9-sample1.png) \| ![](samples/9/clu9-sample2.png) \| ![](samples/9/clu9-sample3.png) \| ![](samples/9/clu9-sample4.png) \| X \| X \| \| \| X \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/mukai_takumi_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T12:56:03+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-17T00:16:40+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of mukai\_takumi (THE iDOLM@STER: Cinderella Girls) =========================================================== This is the dataset of mukai\_takumi (THE iDOLM@STER: Cinderella Girls), containing 467 images and their tags. The core tags of this character are 'breasts, long\_hair, black\_hair, large\_breasts, brown\_hair, green\_eyes, brown\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
a9730f479f045b8300988877ad10effc266a9ff1	# Dataset Card for "simplepile-lite" Interleaved dataset using 'first exhausted' strategy. Counts: ```python DatasetDict({ train: Dataset({ features: ['text'], num_rows: 452432 }) validation: Dataset({ features: ['text'], num_rows: 1000 }) test: Dataset({ features: ['text'], num_rows: 11908 }) }) ``` ## token counts - train using GPTNeoX Tokenizer: \| \| token_count \| \|:------\|-----------------:\| \| count \| 452432 \| \| mean \| 868.642 \| \| std \| 4791.71 \| \| min \| 3 \| \| 25% \| 88 \| \| 50% \| 232 \| \| 75% \| 590 \| \| max \| 1.39747e+06 \| ---	pszemraj/simplepile-lite	[ "task_categories:fill-mask", "task_categories:text-generation", "size_categories:100K<n<1M", "source_datasets:pszemraj/simple_wikipedia_LM", "source_datasets:JeanKaddour/minipile", "language:en", "license:apache-2.0", "region:us" ]	2023-09-14T13:17:39+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "source_datasets": ["pszemraj/simple_wikipedia_LM", "JeanKaddour/minipile"], "task_categories": ["fill-mask", "text-generation"], "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": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1552622685, "num_examples": 452432}, {"name": "validation", "num_bytes": 3202346, "num_examples": 1000}, {"name": "test", "num_bytes": 41145686, "num_examples": 11908}], "download_size": 867798625, "dataset_size": 1596970717}}	2023-10-04T06:50:40+00:00	[]	[ "en" ]	TAGS #task_categories-fill-mask #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-pszemraj/simple_wikipedia_LM #source_datasets-JeanKaddour/minipile #language-English #license-apache-2.0 #region-us	Dataset Card for "simplepile-lite" ================================== Interleaved dataset using 'first exhausted' strategy. Counts: token counts - train -------------------- using GPTNeoX Tokenizer: ---	[]	[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-pszemraj/simple_wikipedia_LM #source_datasets-JeanKaddour/minipile #language-English #license-apache-2.0 #region-us \n" ]	[ 84 ]	[ "passage: TAGS\n#task_categories-fill-mask #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-pszemraj/simple_wikipedia_LM #source_datasets-JeanKaddour/minipile #language-English #license-apache-2.0 #region-us \n" ]
83edecb2dd5b5a25259047ebd2f4be6c4f2666a0	# Overview This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist. The dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse. ## Dataset details The dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset consists of 990 sentences and the test dataset consists of 660 sentences. ### Dataset annotations - 0: non-offensive - 1: offensive	metabloit/offensive-swahili-text	[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:sw", "license:mit", "region:us" ]	2023-09-14T13:18:50+00:00	{"language": ["sw"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "viewer": true}	2023-09-14T13:33:52+00:00	[]	[ "sw" ]	TAGS #task_categories-text-classification #size_categories-1K<n<10K #language-Swahili (macrolanguage) #license-mit #region-us	# Overview This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist. The dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse. ## Dataset details The dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset consists of 990 sentences and the test dataset consists of 660 sentences. ### Dataset annotations - 0: non-offensive - 1: offensive	[ "# Overview\nThis dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.\nThe dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse.", "## Dataset details\nThe dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset\nconsists of 990 sentences and the test dataset consists of 660 sentences.", "### Dataset annotations\n- 0: non-offensive\n- 1: offensive" ]	[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Swahili (macrolanguage) #license-mit #region-us \n", "# Overview\nThis dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.\nThe dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse.", "## Dataset details\nThe dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset\nconsists of 990 sentences and the test dataset consists of 660 sentences.", "### Dataset annotations\n- 0: non-offensive\n- 1: offensive" ]	[ 45, 65, 55, 18 ]	[ "passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Swahili (macrolanguage) #license-mit #region-us \n# Overview\nThis dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.\nThe dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse.## Dataset details\nThe dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset\nconsists of 990 sentences and the test dataset consists of 660 sentences.### Dataset annotations\n- 0: non-offensive\n- 1: offensive" ]
fec05d7e8614fa94e6033204bb6c4a264cbc4a09	This dataset uses the system instructions from the orca-wizardlm dataset, but attaches the original evolved instruction responses and two cluster solutions. The dolphin clusters are clusters for the instructions only, using sgpt approach with the dolphin-7b model. The GTE clusters are clusters for the instructions and responses, as rough topic mapping. I use these cluster solutions to downsample the dataset by topic, whilst preserving clusters of 'evolved instructions'.	KnutJaegersberg/orca-wizardlm-v1-clustered	[ "license:cc-by-nc-4.0", "region:us" ]	2023-09-14T13:31:53+00:00	{"license": "cc-by-nc-4.0"}	2023-09-14T13:39:25+00:00	[]	[]	TAGS #license-cc-by-nc-4.0 #region-us	This dataset uses the system instructions from the orca-wizardlm dataset, but attaches the original evolved instruction responses and two cluster solutions. The dolphin clusters are clusters for the instructions only, using sgpt approach with the dolphin-7b model. The GTE clusters are clusters for the instructions and responses, as rough topic mapping. I use these cluster solutions to downsample the dataset by topic, whilst preserving clusters of 'evolved instructions'.	[]	[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]	[ 17 ]	[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]
b1078c0aa2f4fc7eb09b2ccb8499e4471a6819ea	This dataset is the gpt-4 generated subset of the dolphin / orca dataset, with clusters from: - gte embeddings - dolphin-7b sgpt embeddings - bigbird document embeddings (simsce sentence transformer based, for the whole document)	KnutJaegersberg/dolphin_orca_clustered	[ "license:cc-by-nc-4.0", "region:us" ]	2023-09-14T13:50:21+00:00	{"license": "cc-by-nc-4.0"}	2023-09-14T14:24:42+00:00	[]	[]	TAGS #license-cc-by-nc-4.0 #region-us	This dataset is the gpt-4 generated subset of the dolphin / orca dataset, with clusters from: - gte embeddings - dolphin-7b sgpt embeddings - bigbird document embeddings (simsce sentence transformer based, for the whole document)	[]	[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]	[ 17 ]	[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]
f48bf1ad11a8036c69031cdf1904d498115f9b97	Synthetic languages for nmt testing	aatherton2024/eng-nah-svo	[ "region:us" ]	2023-09-14T13:55:30+00:00	{"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": "en", "dtype": "string"}, {"name": "fr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 291262, "num_examples": 7292}, {"name": "validation", "num_bytes": 39653, "num_examples": 1001}, {"name": "test", "num_bytes": 39857, "num_examples": 1001}], "download_size": 207430, "dataset_size": 370772}}	2023-09-15T14:15:08+00:00	[]	[]	TAGS #region-us	Synthetic languages for nmt testing	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]
e65f0362ca7e4d0e4ed64c5922b3c60bedc7b785	# Dataset Card for Geo-Tagged Social Media Posts with Timestamps ## Dataset Description - Homepage: https://huggingface.co/datasets/yachay/text_coordinates_seasons - Repository: https://github.com/Yachay-AI/byt5-geotagging#datasets - Paper: https://dev.to/yachayai/applying-machine-learning-to-geolocate-twitter-posts-2m1d - Leaderboard: - Point of Contact: [email protected] ### Dataset Summary The "Seasons" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages Multilingual Dataset Mainly contains English, Spanish, Persian, Russian, Korean, and Arabic. ## Dataset Structure ### Data Instances The "Seasons" dataset consists of over 600,000 data instances, each representing a social media post. ### Data Fields Text (text): This field contains the textual content. Timestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format. Geographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format. ```json { "text": "sample text", "geo_geo_bbox": "[-67.220209, 9.934294, -65.428322, 10.6496277]" }, { "created_at": { "$numberLong": "1633049378000" } ``` ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation ### Curation Rationale The "Seasons" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Seasons" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT	yachay/text_coordinates_seasons	[ "task_categories:feature-extraction", "task_categories:token-classification", "task_categories:text-classification", "size_categories:100M<n<1B", "language:en", "language:es", "language:ru", "language:co", "language:ar", "language:fa", "license:mit", "multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit", "region:us" ]	2023-09-14T14:27:55+00:00	{"language": ["en", "es", "ru", "co", "ar", "fa"], "license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["feature-extraction", "token-classification", "text-classification"], "pretty_name": "Geo-Tagged Social Media Posts with Timestamps", "tags": ["multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit"]}	2023-09-22T12:28:15+00:00	[]	[ "en", "es", "ru", "co", "ar", "fa" ]	TAGS #task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Spanish #language-Russian #language-Corsican #language-Arabic #language-Persian #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us	# Dataset Card for Geo-Tagged Social Media Posts with Timestamps ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: admin-team@URL ### Dataset Summary The "Seasons" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages Multilingual Dataset Mainly contains English, Spanish, Persian, Russian, Korean, and Arabic. ## Dataset Structure ### Data Instances The "Seasons" dataset consists of over 600,000 data instances, each representing a social media post. ### Data Fields Text (text): This field contains the textual content. Timestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format. Geographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format. ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation ### Curation Rationale The "Seasons" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Seasons" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT	[ "# Dataset Card for Geo-Tagged Social Media Posts with Timestamps", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Seasons\" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nMainly contains English, Spanish, Persian, Russian, Korean, and Arabic.", "## Dataset Structure", "### Data Instances\n\nThe \"Seasons\" dataset consists of over 600,000 data instances, each representing a social media post.", "### Data Fields\n\nText (text): This field contains the textual content.\n\nTimestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format.\n\nGeographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation", "### Curation Rationale\n\nThe \"Seasons\" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Seasons\" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]	[ "TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Spanish #language-Russian #language-Corsican #language-Arabic #language-Persian #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n", "# Dataset Card for Geo-Tagged Social Media Posts with Timestamps", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Seasons\" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nMainly contains English, Spanish, Persian, Russian, Korean, and Arabic.", "## Dataset Structure", "### Data Instances\n\nThe \"Seasons\" dataset consists of over 600,000 data instances, each representing a social media post.", "### Data Fields\n\nText (text): This field contains the textual content.\n\nTimestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format.\n\nGeographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation", "### Curation Rationale\n\nThe \"Seasons\" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Seasons\" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]	[ 149, 17, 32, 144, 67, 27, 6, 33, 120, 68, 5, 77, 4, 37, 12, 5, 21, 21, 8, 42, 64, 88, 5, 10, 7 ]	[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Spanish #language-Russian #language-Corsican #language-Arabic #language-Persian #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n# Dataset Card for Geo-Tagged Social Media Posts with Timestamps## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL### Dataset Summary\n\nThe \"Seasons\" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results.### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.### Languages\n\nMultilingual Dataset\n\nMainly contains English, Spanish, Persian, Russian, Korean, and Arabic.## Dataset Structure### Data Instances\n\nThe \"Seasons\" dataset consists of over 600,000 data instances, each representing a social media post." ]
b9fa48181e3c93791d0613382c77aeb91214f324	# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions) ## Dataset Description - Homepage: https://huggingface.co/datasets/yachay/text_coordinates_regions - Repository: https://github.com/Yachay-AI/byt5-geotagging#datasets - Paper: https://dev.to/yachayai/applying-machine-learning-to-geolocate-twitter-posts-2m1d - Leaderboard: - Point of Contact: [email protected] ### Dataset Summary The "Regions" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates. Key Features: - Textual Data: The dataset contains 500,000 text samples. - Geocoordinates: Each text sample is associated with geocoordinates. - Regional Coverage: The dataset encompasses 123 of the world's most populated regions. - Tweet Data: Within each region, there are 5,000 individual tweets/comments. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages Multilingual Dataset This dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset. ## Dataset Structure Structure and Naming Convention: The naming convention for the JSON files follows the format "c_0.json" to "c_122.json," where "c_" represents the region category followed by a unique identifier ```bash / ├── .gitattributes ├── README.md ├── c_0.json # Each .json file attributes to one of 123 regions ├── c_1.json ├── ... ├── c_122.json ``` ### Data Instances The Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file. ### Data Fields Text (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content. Coordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text. ```json { "text": "sample text", "coordinates": [ "-75.04057630341867", "40.01714225600481" ] } ``` ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation 2021 ### Curation Rationale The "Regions" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Regions" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT	yachay/text_coordinates_regions	[ "task_categories:feature-extraction", "task_categories:token-classification", "task_categories:text-classification", "size_categories:100M<n<1B", "language:en", "language:zh", "language:es", "language:hi", "language:ar", "language:bn", "language:pt", "language:ru", "language:ja", "language:pa", "language:de", "language:jv", "language:ms", "language:te", "language:vi", "language:ko", "language:fr", "language:mr", "language:ta", "language:ur", "language:tr", "language:it", "language:th", "language:gu", "language:fa", "language:pl", "license:mit", "multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit", "region:us" ]	2023-09-14T14:28:51+00:00	{"language": ["en", "zh", "es", "hi", "ar", "bn", "pt", "ru", "ja", "pa", "de", "jv", "ms", "te", "vi", "ko", "fr", "mr", "ta", "ur", "tr", "it", "th", "gu", "fa", "pl"], "license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["feature-extraction", "token-classification", "text-classification"], "pretty_name": "Multilingual Geo-Tagged Social Media Posts (by 123 world regions)", "tags": ["multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit"]}	2023-09-21T15:19:16+00:00	[]	[ "en", "zh", "es", "hi", "ar", "bn", "pt", "ru", "ja", "pa", "de", "jv", "ms", "te", "vi", "ko", "fr", "mr", "ta", "ur", "tr", "it", "th", "gu", "fa", "pl" ]	TAGS #task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Chinese #language-Spanish #language-Hindi #language-Arabic #language-Bengali #language-Portuguese #language-Russian #language-Japanese #language-Panjabi #language-German #language-Javanese #language-Malay (macrolanguage) #language-Telugu #language-Vietnamese #language-Korean #language-French #language-Marathi #language-Tamil #language-Urdu #language-Turkish #language-Italian #language-Thai #language-Gujarati #language-Persian #language-Polish #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us	# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions) ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: admin-team@URL ### Dataset Summary The "Regions" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates. Key Features: - Textual Data: The dataset contains 500,000 text samples. - Geocoordinates: Each text sample is associated with geocoordinates. - Regional Coverage: The dataset encompasses 123 of the world's most populated regions. - Tweet Data: Within each region, there are 5,000 individual tweets/comments. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages Multilingual Dataset This dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset. ## Dataset Structure Structure and Naming Convention: The naming convention for the JSON files follows the format "c_0.json" to "c_122.json," where "c_" represents the region category followed by a unique identifier ### Data Instances The Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file. ### Data Fields Text (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content. Coordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text. ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation 2021 ### Curation Rationale The "Regions" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Regions" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT	[ "# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions)", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Regions\" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates.\n\nKey Features:\n\n- Textual Data: The dataset contains 500,000 text samples.\n- Geocoordinates: Each text sample is associated with geocoordinates.\n- Regional Coverage: The dataset encompasses 123 of the world's most populated regions.\n- Tweet Data: Within each region, there are 5,000 individual tweets/comments.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nThis dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset.", "## Dataset Structure\n\nStructure and Naming Convention:\n\nThe naming convention for the JSON files follows the format \"c_0.json\" to \"c_122.json,\" where \"c_\" represents the region category followed by a unique identifier", "### Data Instances\n\nThe Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file.", "### Data Fields\n\nText (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content.\n\nCoordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation\n\n2021", "### Curation Rationale\n\nThe \"Regions\" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Regions\" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]	[ "TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Chinese #language-Spanish #language-Hindi #language-Arabic #language-Bengali #language-Portuguese #language-Russian #language-Japanese #language-Panjabi #language-German #language-Javanese #language-Malay (macrolanguage) #language-Telugu #language-Vietnamese #language-Korean #language-French #language-Marathi #language-Tamil #language-Urdu #language-Turkish #language-Italian #language-Thai #language-Gujarati #language-Persian #language-Polish #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n", "# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions)", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Regions\" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates.\n\nKey Features:\n\n- Textual Data: The dataset contains 500,000 text samples.\n- Geocoordinates: Each text sample is associated with geocoordinates.\n- Regional Coverage: The dataset encompasses 123 of the world's most populated regions.\n- Tweet Data: Within each region, there are 5,000 individual tweets/comments.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nThis dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset.", "## Dataset Structure\n\nStructure and Naming Convention:\n\nThe naming convention for the JSON files follows the format \"c_0.json\" to \"c_122.json,\" where \"c_\" represents the region category followed by a unique identifier", "### Data Instances\n\nThe Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file.", "### Data Fields\n\nText (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content.\n\nCoordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation\n\n2021", "### Curation Rationale\n\nThe \"Regions\" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Regions\" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]	[ 258, 23, 32, 157, 67, 59, 61, 54, 72, 68, 6, 94, 4, 37, 12, 5, 21, 21, 8, 77, 64, 84, 5, 10, 7 ]	[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Chinese #language-Spanish #language-Hindi #language-Arabic #language-Bengali #language-Portuguese #language-Russian #language-Japanese #language-Panjabi #language-German #language-Javanese #language-Malay (macrolanguage) #language-Telugu #language-Vietnamese #language-Korean #language-French #language-Marathi #language-Tamil #language-Urdu #language-Turkish #language-Italian #language-Thai #language-Gujarati #language-Persian #language-Polish #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions)## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL### Dataset Summary\n\nThe \"Regions\" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates.\n\nKey Features:\n\n- Textual Data: The dataset contains 500,000 text samples.\n- Geocoordinates: Each text sample is associated with geocoordinates.\n- Regional Coverage: The dataset encompasses 123 of the world's most populated regions.\n- Tweet Data: Within each region, there are 5,000 individual tweets/comments.", "passage: ### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.### Languages\n\nMultilingual Dataset\n\nThis dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset.## Dataset Structure\n\nStructure and Naming Convention:\n\nThe naming convention for the JSON files follows the format \"c_0.json\" to \"c_122.json,\" where \"c_\" represents the region category followed by a unique identifier### Data Instances\n\nThe Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file.### Data Fields\n\nText (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content.\n\nCoordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text.### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.## Dataset Creation\n\n2021### Curation Rationale\n\nThe \"Regions\" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool.### Source Data#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.#### Who are the source language producers?\n\nTwitter Community### Annotations#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources." ]
474c9ac0b09f0fc7732812e675e5b164c466fc5d	# Dataset of yusa_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls) This is the dataset of yusa_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls), containing 382 images and their tags. The core tags of this character are `blonde_hair, green_eyes, ahoge, twintails, long_hair, low_twintails, 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 \| 382 \| 475.64 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 382 \| 270.08 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 893 \| 576.60 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 382 \| 422.43 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 893 \| 843.44 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_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/yusa_kozue_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 \| 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, blush, cat_ears, cat_girl, cat_tail, dress, looking_at_viewer, open_mouth, solo, animal_ear_fluff, simple_background, white_background, bell, between_legs, collarbone, long_sleeves, sitting \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, solo, dress, open_mouth, looking_at_viewer, socks, sitting, wrist_cuffs \| \| 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) \| 1girl, solo, blush, looking_at_viewer, open_mouth, white_background, brown_dress, hair_bow, simple_background, :d, frilled_dress, long_sleeves, plaid_dress, kneehighs, shoes, white_socks \| \| 3 \| 15 \| ![](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) \| blush, hair_flower, looking_at_viewer, 1girl, head_wreath, solo, navel, open_mouth, white_background, wrist_cuffs, skirt, bare_shoulders, dress, pink_flower, collarbone, :o, flower_necklace, simple_background, :d, fairy_wings, flower_wreath, sandals, shirt, swept_bangs, upper_body \| \| 4 \| 7 \| ![](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, loli, navel, simple_background, solo, white_background, groin, nipples, flat_chest, looking_at_viewer, open_mouth, ass_visible_through_thighs, clothes_lift, lifted_by_self, nude, pussy, shirt \| \| 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, loli, open_mouth, 1boy, flat_chest, hetero, navel, nipples, sex, spread_legs, censored, completely_nude, cum_in_pussy, penis, solo_focus, thighs, vaginal, girl_on_top, overflow, pov, smile, straddling \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| cat_ears \| cat_girl \| cat_tail \| dress \| looking_at_viewer \| open_mouth \| solo \| animal_ear_fluff \| simple_background \| white_background \| bell \| between_legs \| collarbone \| long_sleeves \| sitting \| socks \| wrist_cuffs \| brown_dress \| hair_bow \| :d \| frilled_dress \| plaid_dress \| kneehighs \| shoes \| white_socks \| hair_flower \| head_wreath \| navel \| skirt \| bare_shoulders \| pink_flower \| :o \| flower_necklace \| fairy_wings \| flower_wreath \| sandals \| shirt \| swept_bangs \| upper_body \| loli \| groin \| nipples \| flat_chest \| ass_visible_through_thighs \| clothes_lift \| lifted_by_self \| nude \| pussy \| 1boy \| hetero \| sex \| spread_legs \| censored \| completely_nude \| cum_in_pussy \| penis \| solo_focus \| thighs \| vaginal \| girl_on_top \| overflow \| pov \| smile \| straddling \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-----------\|:-----------\|:-----------\|:--------\|:--------------------\|:-------------\|:-------\|:-------------------\|:--------------------\|:-------------------\|:-------\|:---------------\|:-------------\|:---------------\|:----------\|:--------\|:--------------\|:--------------\|:-----------\|:-----\|:----------------\|:--------------\|:------------\|:--------\|:--------------\|:--------------\|:--------------\|:--------\|:--------\|:-----------------\|:--------------\|:-----\|:------------------\|:--------------\|:----------------\|:----------\|:--------\|:--------------\|:-------------\|:-------\|:--------\|:----------\|:-------------\|:-----------------------------\|:---------------\|:-----------------\|:-------\|:--------\|:-------\|:---------\|:------\|:--------------\|:-----------\|:------------------\|:---------------\|:--------\|:-------------\|:---------\|:----------\|:--------------\|:-----------\|:------\|:--------\|:-------------\| \| 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 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 14 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| \| \| \| X \| X \| X \| X \| \| \| \| \| \| \| \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 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 \| 15 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| \| \| \| X \| X \| X \| X \| \| X \| X \| \| \| X \| \| \| \| X \| \| \| X \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 7 \| ![](samples/4/clu4-sample0.png) \| ![](samples/4/clu4-sample1.png) \| ![](samples/4/clu4-sample2.png) \| ![](samples/4/clu4-sample3.png) \| ![](samples/4/clu4-sample4.png) \| X \| X \| \| \| \| \| X \| X \| X \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 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 \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/yusa_kozue_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T14:44:19+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T16:47:46+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of yusa\_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls) ===================================================================== This is the dataset of yusa\_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls), containing 382 images and their tags. The core tags of this character are 'blonde\_hair, green\_eyes, ahoge, twintails, long\_hair, low\_twintails, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]
4a426e9a8a9ffa006b060a695f9f813fbe4b5eae	# Dataset of himekawa_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls) This is the dataset of himekawa_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls), containing 334 images and their tags. The core tags of this character are `long_hair, brown_hair, green_eyes, hair_ornament, hairclip, breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages \| Name \| Images \| Size \| Download \| Type \| Description \| \|:-----------------\|---------:\|:-----------\|:-----------------------------------------------------------------------------------------------------------------------------------------\|:-----------\|:---------------------------------------------------------------------\| \| raw \| 334 \| 300.16 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) \| Waifuc-Raw \| Raw data with meta information (min edge aligned to 1400 if larger). \| \| 800 \| 334 \| 217.45 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 800 pixels. \| \| stage3-p480-800 \| 756 \| 429.49 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) \| IMG+TXT \| 3-stage cropped dataset with the area not less than 480x480 pixels. \| \| 1200 \| 334 \| 285.71 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) \| IMG+TXT \| dataset with the shorter side not exceeding 1200 pixels. \| \| stage3-p480-1200 \| 756 \| 529.98 MiB \| [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_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/himekawa_yuki_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 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| 1girl, blush, open_mouth, solo, :d, looking_at_viewer, baseball_cap, skirt \| \| 1 \| 12 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| 1girl, blush, midriff, navel, open_mouth, skirt, solo, cleavage, :d, crop_top, looking_at_viewer, pom_pom_(cheerleading), cheerleader, baseball_cap, bow, detached_sleeves, thighhighs \| \| 2 \| 8 \| ![](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, navel, open_mouth, smile, cleavage, solo, ;d, blush, looking_at_viewer, one_eye_closed, orange_bikini, large_breasts, character_name, side-tie_bikini_bottom \| \| 3 \| 9 \| ![](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, cleavage, looking_at_viewer, solo, bangs, collarbone, open_mouth, teeth, cross-laced_clothes, navel, shark_hood, spiked_bracelet, :d, bare_shoulders, blue_sky, day, front-tie_bikini_top, large_breasts, outdoors, spiked_collar, stomach, hair_between_eyes, hood_up, cloud \| \| 4 \| 5 \| ![](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, cleavage, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, simple_background, smile, solo, strapless_leotard, black_pantyhose, open_mouth, white_background, wrist_cuffs, alcohol, bangs, black_leotard, covered_navel, dated, holding, large_breasts, teeth, bare_shoulders, black_bowtie, bottle, cowboy_shot, fishnet_pantyhose, hair_between_eyes, highleg_leotard, orange_leotard, rabbit_tail, signature, sitting, yellow_bowtie \| \| 5 \| 17 \| ![](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, hetero, solo_focus, 1boy, nipples, penis, open_mouth, sweat, sex, bar_censor, navel, smile, vaginal, large_breasts, cum_in_pussy, female_pubic_hair, heart, completely_nude \| ### Table Version \| # \| Samples \| Img-1 \| Img-2 \| Img-3 \| Img-4 \| Img-5 \| 1girl \| blush \| open_mouth \| solo \| :d \| looking_at_viewer \| baseball_cap \| skirt \| midriff \| navel \| cleavage \| crop_top \| pom_pom_(cheerleading) \| cheerleader \| bow \| detached_sleeves \| thighhighs \| smile \| ;d \| one_eye_closed \| orange_bikini \| large_breasts \| character_name \| side-tie_bikini_bottom \| bangs \| collarbone \| teeth \| cross-laced_clothes \| shark_hood \| spiked_bracelet \| bare_shoulders \| blue_sky \| day \| front-tie_bikini_top \| outdoors \| spiked_collar \| stomach \| hair_between_eyes \| hood_up \| cloud \| detached_collar \| fake_animal_ears \| playboy_bunny \| rabbit_ears \| simple_background \| strapless_leotard \| black_pantyhose \| white_background \| wrist_cuffs \| alcohol \| black_leotard \| covered_navel \| dated \| holding \| black_bowtie \| bottle \| cowboy_shot \| fishnet_pantyhose \| highleg_leotard \| orange_leotard \| rabbit_tail \| signature \| sitting \| yellow_bowtie \| hetero \| solo_focus \| 1boy \| nipples \| penis \| sweat \| sex \| bar_censor \| vaginal \| cum_in_pussy \| female_pubic_hair \| heart \| completely_nude \| \|----:\|----------:\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------------------------------\|:--------\|:--------\|:-------------\|:-------\|:-----\|:--------------------\|:---------------\|:--------\|:----------\|:--------\|:-----------\|:-----------\|:-------------------------\|:--------------\|:------\|:-------------------\|:-------------\|:--------\|:-----\|:-----------------\|:----------------\|:----------------\|:-----------------\|:-------------------------\|:--------\|:-------------\|:--------\|:----------------------\|:-------------\|:------------------\|:-----------------\|:-----------\|:------\|:-----------------------\|:-----------\|:----------------\|:----------\|:--------------------\|:----------\|:--------\|:------------------\|:-------------------\|:----------------\|:--------------\|:--------------------\|:--------------------\|:------------------\|:-------------------\|:--------------\|:----------\|:----------------\|:----------------\|:--------\|:----------\|:---------------\|:---------\|:--------------\|:--------------------\|:------------------\|:-----------------\|:--------------\|:------------\|:----------\|:----------------\|:---------\|:-------------\|:-------\|:----------\|:--------\|:--------\|:------\|:-------------\|:----------\|:---------------\|:--------------------\|:--------\|:------------------\| \| 0 \| 9 \| ![](samples/0/clu0-sample0.png) \| ![](samples/0/clu0-sample1.png) \| ![](samples/0/clu0-sample2.png) \| ![](samples/0/clu0-sample3.png) \| ![](samples/0/clu0-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 1 \| 12 \| ![](samples/1/clu1-sample0.png) \| ![](samples/1/clu1-sample1.png) \| ![](samples/1/clu1-sample2.png) \| ![](samples/1/clu1-sample3.png) \| ![](samples/1/clu1-sample4.png) \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 2 \| 8 \| ![](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 \| 9 \| ![](samples/3/clu3-sample0.png) \| ![](samples/3/clu3-sample1.png) \| ![](samples/3/clu3-sample2.png) \| ![](samples/3/clu3-sample3.png) \| ![](samples/3/clu3-sample4.png) \| X \| X \| X \| X \| X \| X \| \| \| \| X \| X \| \| \| \| \| \| \| \| \| \| \| X \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 4 \| 5 \| ![](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 \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| 5 \| 17 \| ![](samples/5/clu5-sample0.png) \| ![](samples/5/clu5-sample1.png) \| ![](samples/5/clu5-sample2.png) \| ![](samples/5/clu5-sample3.png) \| ![](samples/5/clu5-sample4.png) \| X \| X \| X \| \| \| \| \| \| \| X \| \| \| \| \| \| \| \| X \| \| \| \| X \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \| X \|	CyberHarem/himekawa_yuki_idolmastercinderellagirls	[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]	2023-09-14T14:50:50+00:00	{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}	2024-01-16T17:19:30+00:00	[]	[]	TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us	Dataset of himekawa\_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls) ======================================================================== This is the dataset of himekawa\_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls), containing 334 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, green\_eyes, hair\_ornament, hairclip, breasts, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version	[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]	[ 44, 61, 5, 4 ]	[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

7c7002a5c4fb939587f214826e00504b1ec89355

# Dataset of otokura_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls) This is the dataset of otokura_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, grey_hair, bangs, black_eyes, grey_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 | 500 | 637.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 367.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1215 | 783.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 574.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1215 | 1.09 GiB | [Download](https://huggingface.co/datasets/CyberHarem/otokura_yuuki_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/otokura_yuuki_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, blush, looking_at_viewer, open_mouth, sailor_collar, sailor_dress, simple_background, solo, white_background, white_dress, wrist_cuffs, blue_bow, frills, :d, sleeveless_dress, bowtie, hair_ornament, collarbone, hair_bow, skirt_hold | | 1 | 13 | ![](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, blazer, school_uniform, smile, school_bag, skirt, solo, looking_at_viewer, blush, neck_ribbon, open_mouth, blue_ribbon, shirt | | 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) | blazer, blue_ribbon, blush, long_sleeves, neck_ribbon, school_uniform, white_background, white_shirt, 1girl, looking_at_viewer, simple_background, solo, upper_body, collared_shirt, :d, black_jacket, blue_jacket, closed_mouth, hand_up, open_jacket, open_mouth, vest | | 3 | 10 | ![](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, open_mouth, looking_at_viewer, midriff, solo, blush, navel, black_hair, shorts, :d, belt, suspenders, white_gloves, fingerless_gloves, mini_hat, simple_background, sleeveless, white_background | | 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, blush, floral_print, looking_at_viewer, solo, obi, hair_flower, wide_sleeves, print_kimono, long_sleeves, open_mouth, upper_body, white_kimono, yukata, :d, closed_mouth, fur_collar, head_tilt, holding, pink_kimono | | 5 | 15 | ![](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, frilled_bikini, navel, solo, blush, looking_at_viewer, open_mouth, bikini_skirt, collarbone, day, pink_bikini, outdoors, sun_hat, :d, blue_sky, halterneck, straw_hat, cloud, ocean, plaid, hair_between_eyes, bare_shoulders, cowboy_shot, hat_bow, standing, water | | 6 | 17 | ![](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, solo, blush, looking_at_viewer, navel, open_mouth, collarbone, :d, midriff, sweat, short_shorts, simple_background, small_breasts, sports_bra, white_background, open_jacket, shoes | | 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, blue_sky, cloud, denim_shorts, earrings, looking_at_viewer, necklace, open_mouth, short_shorts, short_sleeves, solo, tied_shirt, hairband, midriff, navel, orange_shirt, outdoors, :d, belt, blue_shorts, blush, breasts, collarbone, cutoffs, day, flower, bracelet, clothes_writing, cowboy_shot, salute, sunlight, thighs, watch | | 8 | 5 | ![](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, blush, striped_shirt, collarbone, looking_at_viewer, open_mouth, solo, hooded_jacket, hoodie, pink_jacket, shorts, simple_background, smile, white_background, key_necklace | | 9 | 5 | ![](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, pink_shirt, smile, solo, looking_at_viewer, outdoors, short_sleeves, sitting, open_mouth, blurry_background, collarbone, short_shorts, sweat, water_bottle | | 10 | 12 | ![](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) | 1girl, looking_at_viewer, solo, small_breasts, blush, armpits, covered_navel, cowboy_shot, school_swimsuit, smile, arms_up, collarbone, standing, blue_one-piece_swimsuit, white_background | | 11 | 11 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, blush, solo, looking_at_viewer, pink_bow, fur-trimmed_gloves, hair_bow, striped_bow, beret, black_headwear, brown_gloves, heart_earrings, sleeveless, brown_headwear, polka_dot, skirt, :d, breasts, jingle_bell, navel, open_mouth, plaid, rose, see-through, upper_body, white_background, bare_shoulders, closed_mouth, hair_between_eyes, pink_dress, sitting | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | open_mouth | sailor_collar | sailor_dress | simple_background | solo | white_background | white_dress | wrist_cuffs | blue_bow | frills | :d | sleeveless_dress | bowtie | hair_ornament | collarbone | hair_bow | skirt_hold | blazer | school_uniform | smile | school_bag | skirt | neck_ribbon | blue_ribbon | shirt | long_sleeves | white_shirt | upper_body | collared_shirt | black_jacket | blue_jacket | closed_mouth | hand_up | open_jacket | vest | midriff | navel | black_hair | shorts | belt | suspenders | white_gloves | fingerless_gloves | mini_hat | sleeveless | floral_print | obi | hair_flower | wide_sleeves | print_kimono | white_kimono | yukata | fur_collar | head_tilt | holding | pink_kimono | frilled_bikini | bikini_skirt | day | pink_bikini | outdoors | sun_hat | blue_sky | halterneck | straw_hat | cloud | ocean | plaid | hair_between_eyes | bare_shoulders | cowboy_shot | hat_bow | standing | water | sweat | short_shorts | small_breasts | sports_bra | shoes | denim_shorts | earrings | necklace | short_sleeves | tied_shirt | hairband | orange_shirt | blue_shorts | breasts | cutoffs | flower | bracelet | clothes_writing | salute | sunlight | thighs | watch | striped_shirt | hooded_jacket | hoodie | pink_jacket | key_necklace | pink_shirt | sitting | blurry_background | water_bottle | armpits | covered_navel | school_swimsuit | arms_up | blue_one-piece_swimsuit | pink_bow | fur-trimmed_gloves | striped_bow | beret | black_headwear | brown_gloves | heart_earrings | brown_headwear | polka_dot | jingle_bell | rose | see-through | pink_dress | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------------|:-------------|:----------------|:---------------|:--------------------|:-------|:-------------------|:--------------|:--------------|:-----------|:---------|:-----|:-------------------|:---------|:----------------|:-------------|:-----------|:-------------|:---------|:-----------------|:--------|:-------------|:--------|:--------------|:--------------|:--------|:---------------|:--------------|:-------------|:-----------------|:---------------|:--------------|:---------------|:----------|:--------------|:-------|:----------|:--------|:-------------|:---------|:-------|:-------------|:---------------|:--------------------|:-----------|:-------------|:---------------|:------|:--------------|:---------------|:---------------|:---------------|:---------|:-------------|:------------|:----------|:--------------|:-----------------|:---------------|:------|:--------------|:-----------|:----------|:-----------|:-------------|:------------|:--------|:--------|:--------|:--------------------|:-----------------|:--------------|:----------|:-----------|:--------|:--------|:---------------|:----------------|:-------------|:--------|:---------------|:-----------|:-----------|:----------------|:-------------|:-----------|:---------------|:--------------|:----------|:----------|:---------|:-----------|:------------------|:---------|:-----------|:---------|:--------|:----------------|:----------------|:---------|:--------------|:---------------|:-------------|:----------|:--------------------|:---------------|:----------|:----------------|:------------------|:----------|:--------------------------|:-----------|:---------------------|:--------------|:--------|:-----------------|:---------------|:-----------------|:-----------------|:------------|:--------------|:-------|:--------------|:-------------| | 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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 13 | ![](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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 10 | ![](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 | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 15 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | | | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 17 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | | | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | X | | X | | X | | | X | | | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 5 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | X | X | | | | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | 10 | 12 | ![](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 | | | | | | | | | | | | | | | 11 | 11 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-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 |

CyberHarem/otokura_yuuki_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-13T21:50:42+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:11:58+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of otokura\_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls) ========================================================================= This is the dataset of otokura\_yuuki/乙倉悠貴/오토쿠라유우키 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'short\_hair, grey\_hair, bangs, black\_eyes, grey\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

3706a3a7af88cff4c68bee8baa78ed62c67daf9f

# Dataset of oikawa_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls) This is the dataset of oikawa_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, brown_hair, breasts, brown_eyes, large_breasts, huge_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 | 494.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 334.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1161 | 670.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 460.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1161 | 859.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oikawa_shizuku_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/oikawa_shizuku_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, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, headset, neck_bell, open_mouth, pink_thighhighs, solo, blush, cleavage, cow_tail, pink_gloves, skirt, smile, navel | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, headset, neck_bell, nipples, open_mouth, pink_thighhighs, solo, skirt, cow_tail, lactation, smile | | 2 | 15 | ![](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, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, neck_bell, solo, headset, cow_tail, blush, open_mouth, smile, cleavage, navel, looking_at_viewer, simple_background, skirt, white_background | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, cowbell, elbow_gloves, neck_bell, headset, open_mouth, solo, lactation, smile, nipples | | 4 | 5 | ![](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) | 1boy, 1girl, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, cow_tail, cowbell, elbow_gloves, hetero, neck_bell, paizuri, penis, solo_focus, breasts_squeezed_together, headset, mosaic_censoring, nipples, open_mouth, ejaculation, :d, cum_on_breasts, pink_gloves, tongue | | 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, blush, collar, cow_ears, cow_girl, cow_horns, cow_print, elbow_gloves, hetero, multiple_penises, neck_bell, nipples, solo_focus, cowbell, double_handjob, gloved_handjob, lactation, open_mouth, bar_censor, cum_on_breasts, facial, gangbang, headset, paizuri, pink_gloves, 2boys, 3boys, 4boys, 5boys, bukkake, cum_on_hair, cum_on_tongue, ejaculation, fellatio, gigantic_breasts, mmf_threesome, pointless_censoring, veins | | 6 | 11 | ![](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) | looking_at_viewer, 1girl, blush, cleavage, collarbone, navel, open_mouth, solo, bangs, simple_background, white_background, bare_shoulders, :d, side-tie_bikini_bottom, striped_bikini, white_bikini | | 7 | 7 | ![](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, looking_at_viewer, simple_background, solo, white_background, blush, open_mouth, :d | | 8 | 13 | ![](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) | 1boy, 1girl, hetero, solo_focus, blush, nipples, paizuri, cum_on_breasts, penis, smile, open_mouth, breasts_squeezed_together, censored, ejaculation, gigantic_breasts | | 9 | 9 | ![](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, bare_shoulders, elbow_gloves, open_mouth, solo, black_gloves, blush, looking_at_viewer, bangs, hairband, smile, belt, bodysuit, simple_background, zipper | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | collar | cow_ears | cow_girl | cow_horns | cow_print | cowbell | elbow_gloves | headset | neck_bell | open_mouth | pink_thighhighs | solo | blush | cleavage | cow_tail | pink_gloves | skirt | smile | navel | nipples | lactation | looking_at_viewer | simple_background | white_background | 1boy | hetero | paizuri | penis | solo_focus | breasts_squeezed_together | mosaic_censoring | ejaculation | :d | cum_on_breasts | tongue | multiple_penises | double_handjob | gloved_handjob | bar_censor | facial | gangbang | 2boys | 3boys | 4boys | 5boys | bukkake | cum_on_hair | cum_on_tongue | fellatio | gigantic_breasts | mmf_threesome | pointless_censoring | veins | collarbone | bangs | bare_shoulders | side-tie_bikini_bottom | striped_bikini | white_bikini | censored | black_gloves | hairband | belt | bodysuit | zipper | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-----------|:-----------|:------------|:------------|:----------|:---------------|:----------|:------------|:-------------|:------------------|:-------|:--------|:-----------|:-----------|:--------------|:--------|:--------|:--------|:----------|:------------|:--------------------|:--------------------|:-------------------|:-------|:---------|:----------|:--------|:-------------|:----------------------------|:-------------------|:--------------|:-----|:-----------------|:---------|:-------------------|:-----------------|:-----------------|:-------------|:---------|:-----------|:--------|:--------|:--------|:--------|:----------|:--------------|:----------------|:-----------|:-------------------|:----------------|:----------------------|:--------|:-------------|:--------|:-----------------|:-------------------------|:-----------------|:---------------|:-----------|:---------------|:-----------|:-------|:-----------|:---------| | 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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | X | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 15 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | X | X | X | X | | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | X | X | | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | | | X | | | X | | | | X | X | | | | | X | X | | X | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 6 | 11 | ![](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 | | | | | | | | 7 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 13 | ![](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 | | | | | | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | | | | | X | | | X | | X | X | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | X | X | X | X |

CyberHarem/oikawa_shizuku_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-13T22:27:46+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T16:10:50+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of oikawa\_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls) ========================================================================= This is the dataset of oikawa\_shizuku/及川雫/오이카와시즈쿠 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'short\_hair, brown\_hair, breasts, brown\_eyes, large\_breasts, huge\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

7520dffa87bd6f3d41f5b5ed1f74f63743c8eda3

# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-13B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble) 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_oh-yeontaek__llama-2-13B-LoRA-assemble", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T12:38:31.031518](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble/blob/main/results_2023-10-28T12-38-31.031518.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": 0.018246644295302015, "em_stderr": 0.0013706682452812897, "f1": 0.12087667785234917, "f1_stderr": 0.002262552570535497, "acc": 0.4228981679335413, "acc_stderr": 0.009810986357152753 }, "harness|drop|3": { "em": 0.018246644295302015, "em_stderr": 0.0013706682452812897, "f1": 0.12087667785234917, "f1_stderr": 0.002262552570535497 }, "harness|gsm8k|5": { "acc": 0.0841546626231994, "acc_stderr": 0.0076470240466032045 }, "harness|winogrande|5": { "acc": 0.7616416732438832, "acc_stderr": 0.011974948667702302 } } ``` ### 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]

open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble

[ "region:us" ]

2023-09-13T22:30:23+00:00

{"pretty_name": "Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble", "dataset_summary": "Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-13B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T12:38:31.031518](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-13B-LoRA-assemble/blob/main/results_2023-10-28T12-38-31.031518.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\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812897,\n \"f1\": 0.12087667785234917,\n \"f1_stderr\": 0.002262552570535497,\n \"acc\": 0.4228981679335413,\n \"acc_stderr\": 0.009810986357152753\n },\n \"harness|drop|3\": {\n \"em\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812897,\n \"f1\": 0.12087667785234917,\n \"f1_stderr\": 0.002262552570535497\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0841546626231994,\n \"acc_stderr\": 0.0076470240466032045\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702302\n }\n}\n```", "repo_url": "https://huggingface.co/oh-yeontaek/llama-2-13B-LoRA-assemble", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-28T11:38:43+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-28T12:38:31.031518(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T12:38:31.031518(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T12:38:31.031518(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 25, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of oh-yeontaek/llama-2-13B-LoRA-assemble## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-13B-LoRA-assemble on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T12:38:31.031518(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

c6d757d4e47e2bfd6f0f195a4ab1287134818204

An Ongoing SVG Collection of Many Multiples of Brand Logos Object looks like this: Shape: ``` ├── brandName "" ├── brandWebsite "" ├── brandPresence[{ │ └── platform │ └── url │ └── username}] ├── brandLogo[{ │ └── fileName │ └── svgPath │ └── svgData │ ├── meta │ ├── width │ ├── height │ ├── viewbox │ └── fill │ └── svgRaw}] └── brandColors[{ └── meta ├── primary ├── secondary ├── tertiary ├── quaternary ├── priority └── setting ├── colorName ├── colorHex ├── colorRGB ├── colorCMYK └── colorPantone}] ```

mattrichmo/brand-logos

[ "size_categories:1K<n<10K", "region:us" ]

2023-09-13T22:36:51+00:00

{"size_categories": ["1K<n<10K"]}

2023-09-14T19:21:42+00:00

[]

TAGS #size_categories-1K<n<10K #region-us

An Ongoing SVG Collection of Many Multiples of Brand Logos Object looks like this: Shape:

[]

[ "TAGS\n#size_categories-1K<n<10K #region-us \n" ]

[ 18 ]

[ "passage: TAGS\n#size_categories-1K<n<10K #region-us \n" ]

dd2311b1ca951d687d181437372f501333f4481a

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

jsonfin17/financial_conversation_summary

[ "region:us" ]

2023-09-13T23:05:49+00:00

{}

2023-09-13T23:07:35+00:00

[]

TAGS #region-us

# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

9aaf53bb4c22bc9d29d990d85da9a035aceb98ef

# Dataset of mifune_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls) This is the dataset of mifune_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls), containing 487 images and their tags. The core tags of this character are `brown_hair, long_hair, breasts, brown_eyes, ponytail, bangs, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 487 | 522.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 487 | 342.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1117 | 684.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 487 | 476.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1117 | 899.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mifune_miyu_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/mifune_miyu_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, blush, cleavage, looking_at_viewer, smile, solo, bikini, navel, collarbone, jewelry | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, necklace, smile, solo, blush, simple_background, white_background, bare_shoulders, collarbone, bracelet, sleeveless_dress | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, blush, simple_background, sleeveless_dress, solo, white_background, white_dress, cleavage, collarbone, sidelocks, sun_hat, white_choker, white_headwear, white_panties, closed_mouth, dress_lift, looking_at_viewer, side-tie_panties, ass_visible_through_thighs, cowboy_shot, hair_ribbon, lifted_by_self, sundress | | 3 | 16 | ![](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, blush, cleavage, looking_at_viewer, navel, black_bikini, white_shirt, collarbone, dress_shirt, open_shirt, cowboy_shot, side-tie_bikini_bottom, smile, long_sleeves, open_mouth, bikini_under_clothes, day, front-tie_bikini_top, straw_hat, sun_hat, medium_breasts, outdoors, sidelocks, simple_background, sky, standing | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, hair_flower, solo, bare_shoulders, blush, open_mouth, smile, blue_dress, bracelet, choker, looking_at_viewer, blue_flower, cleavage, collarbone, detached_sleeves, medium_breasts, petals | | 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_skirt, cowboy_shot, looking_at_viewer, office_lady, pencil_skirt, sidelocks, solo, black_jacket, blush, collarbone, long_sleeves, miniskirt, simple_background, standing, white_background, white_shirt, cleavage, dress_shirt, medium_breasts, red-framed_eyewear, semi-rimless_eyewear, skirt_suit, swept_bangs, bespectacled, collared_shirt, open_mouth, parted_lips, smile | | 6 | 11 | ![](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) | looking_at_viewer, obi, 1girl, blush, smile, solo, floral_print, hair_flower, sidelocks, yukata, cleavage, wide_sleeves | | 7 | 20 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | rabbit_ears, 1girl, detached_collar, playboy_bunny, fake_animal_ears, bowtie, solo, blush, wrist_cuffs, cleavage, looking_at_viewer, simple_background, strapless_leotard, white_background, bare_shoulders, medium_breasts, black_leotard, black_pantyhose, fishnet_pantyhose, rabbit_tail, sidelocks, smile | | 8 | 16 | ![](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) | blush, tiger_print, 1girl, fingerless_gloves, solo, fake_animal_ears, tiger_ears, bare_shoulders, cleavage, looking_at_viewer, midriff, tiger_tail, navel, fur_trim, headset, belt, skirt, open_mouth, tube_top, black_choker, collarbone, crop_top, fishnet_pantyhose, side_ponytail, simple_background | | 9 | 5 | ![](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, gym_uniform, kneehighs, looking_at_viewer, open_mouth, short_sleeves, solo, white_shirt, white_socks, red_buruma, simple_background, white_background, buruma_pull, collarbone, lifted_by_self, medium_breasts, sidelocks, sitting, thighs, uwabaki, white_footwear, full_body, gym_shirt, navel, partially_visible_vulva, shirt_lift, spread_legs, wedgie, white_bra | | 10 | 5 | ![](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) | 1boy, 1girl, blush, hetero, nipples, navel, open_mouth, solo_focus, breast_grab, collarbone, grabbing, pussy, sex, sweat, vaginal, completely_nude, looking_at_viewer, mosaic_censoring, on_back, penis, pubic_hair | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, blush, long_sleeves, pleated_skirt, black_pantyhose, solo, black_serafuku, black_shirt, black_skirt, collarbone, looking_at_viewer, sitting, white_neckerchief, bag, black_sailor_collar, blue_serafuku, blue_shirt, blue_skirt, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | cleavage | looking_at_viewer | smile | solo | bikini | navel | collarbone | jewelry | necklace | simple_background | white_background | bare_shoulders | bracelet | sleeveless_dress | white_dress | sidelocks | sun_hat | white_choker | white_headwear | white_panties | closed_mouth | dress_lift | side-tie_panties | ass_visible_through_thighs | cowboy_shot | hair_ribbon | lifted_by_self | sundress | black_bikini | white_shirt | dress_shirt | open_shirt | side-tie_bikini_bottom | long_sleeves | open_mouth | bikini_under_clothes | day | front-tie_bikini_top | straw_hat | medium_breasts | outdoors | sky | standing | hair_flower | blue_dress | choker | blue_flower | detached_sleeves | petals | black_skirt | office_lady | pencil_skirt | black_jacket | miniskirt | red-framed_eyewear | semi-rimless_eyewear | skirt_suit | swept_bangs | bespectacled | collared_shirt | parted_lips | obi | floral_print | yukata | wide_sleeves | rabbit_ears | detached_collar | playboy_bunny | fake_animal_ears | bowtie | wrist_cuffs | strapless_leotard | black_leotard | black_pantyhose | fishnet_pantyhose | rabbit_tail | tiger_print | fingerless_gloves | tiger_ears | midriff | tiger_tail | fur_trim | headset | belt | skirt | tube_top | black_choker | crop_top | side_ponytail | gym_uniform | kneehighs | short_sleeves | white_socks | red_buruma | buruma_pull | sitting | thighs | uwabaki | white_footwear | full_body | gym_shirt | partially_visible_vulva | shirt_lift | spread_legs | wedgie | white_bra | 1boy | hetero | nipples | solo_focus | breast_grab | grabbing | pussy | sex | sweat | vaginal | completely_nude | mosaic_censoring | on_back | penis | pubic_hair | pleated_skirt | black_serafuku | black_shirt | white_neckerchief | bag | black_sailor_collar | blue_serafuku | blue_shirt | blue_skirt | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:-----------|:--------------------|:--------|:-------|:---------|:--------|:-------------|:----------|:-----------|:--------------------|:-------------------|:-----------------|:-----------|:-------------------|:--------------|:------------|:----------|:---------------|:-----------------|:----------------|:---------------|:-------------|:-------------------|:-----------------------------|:--------------|:--------------|:-----------------|:-----------|:---------------|:--------------|:--------------|:-------------|:-------------------------|:---------------|:-------------|:-----------------------|:------|:-----------------------|:------------|:-----------------|:-----------|:------|:-----------|:--------------|:-------------|:---------|:--------------|:-------------------|:---------|:--------------|:--------------|:---------------|:---------------|:------------|:---------------------|:-----------------------|:-------------|:--------------|:---------------|:-----------------|:--------------|:------|:---------------|:---------|:---------------|:--------------|:------------------|:----------------|:-------------------|:---------|:--------------|:--------------------|:----------------|:------------------|:--------------------|:--------------|:--------------|:--------------------|:-------------|:----------|:-------------|:-----------|:----------|:-------|:--------|:-----------|:---------------|:-----------|:----------------|:--------------|:------------|:----------------|:--------------|:-------------|:--------------|:----------|:---------|:----------|:-----------------|:------------|:------------|:--------------------------|:-------------|:--------------|:---------|:------------|:-------|:---------|:----------|:-------------|:--------------|:-----------|:--------|:------|:--------|:----------|:------------------|:-------------------|:----------|:--------|:-------------|:----------------|:-----------------|:--------------|:--------------------|:------|:----------------------|:----------------|:-------------|:-------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | X | X | X | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | X | | X | | | X | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 16 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | | X | X | | | X | | | | | | X | X | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | X | X | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | X | X | | | X | | | X | X | | | | | X | | | | | | | | | X | | | | | X | X | | | X | X | | | | | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 20 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | X | X | | | | | | X | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 16 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | X | X | | X | | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | | X | | X | | X | X | | | X | X | | | | | X | | | | | | | | | | | X | | | X | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 5 | ![](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 | | | | | | | | | | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | X | | X | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |

CyberHarem/mifune_miyu_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-13T23:14:53+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T16:21:22+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of mifune\_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls) =============================================================== This is the dataset of mifune\_miyu/三船美優 (THE iDOLM@STER: Cinderella Girls), containing 487 images and their tags. The core tags of this character are 'brown\_hair, long\_hair, breasts, brown\_eyes, ponytail, bangs, large\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

f26b5261b15ffd608903645e93a0e203577c86ef

# Dataset Card for "deleted-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HydraLM/filter-delete-1

[ "region:us" ]

2023-09-13T23:15:21+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1403707526, "num_examples": 230443}], "download_size": 1340424028, "dataset_size": 1403707526}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-13T23:20:06+00:00

[]

TAGS #region-us

# Dataset Card for "deleted-2" More Information needed

[ "# Dataset Card for \"deleted-2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"deleted-2\"\n\nMore Information needed" ]

[ 6, 13 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"deleted-2\"\n\nMore Information needed" ]

6da6baa4078f3d2da4efa3cbb74b561d13d09921

# Dataset Card for "truth-tagged-oasst-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

atmallen/truth-tagged-oasst-alpaca

[ "region:us" ]

2023-09-13T23:19:53+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "s_idx", "dtype": "int64"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "False", "1": "True"}}}}], "splits": [{"name": "validation", "num_bytes": 232102, "num_examples": 197}], "download_size": 61886, "dataset_size": 232102}}

2023-09-13T23:52:39+00:00

[]

TAGS #region-us

# Dataset Card for "truth-tagged-oasst-alpaca" More Information needed

[ "# Dataset Card for \"truth-tagged-oasst-alpaca\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"truth-tagged-oasst-alpaca\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"truth-tagged-oasst-alpaca\"\n\nMore Information needed" ]

3ba59498674cd2f1ed0fcac42c7efdff4a392745

# Dataset of hino_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls) This is the dataset of hino_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls), containing 424 images and their tags. The core tags of this character are `long_hair, brown_hair, ponytail, green_eyes, breasts, high_ponytail, bow, hair_bow, medium_breasts, aqua_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 | 424 | 350.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 424 | 271.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 916 | 530.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 424 | 336.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 916 | 642.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hino_akane_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/hino_akane_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 | 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, looking_at_viewer, open_mouth, solo, :d, navel, side-tie_bikini_bottom, striped_bikini, blush, cleavage, day, front-tie_top, large_breasts | | 1 | 29 | ![](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, cleavage, open_mouth, smile, solo, looking_at_viewer, navel, midriff, skirt, blush, star_(symbol), wristband, orange_hair | | 2 | 8 | ![](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, cleavage, navel, open_mouth, smile, solo, striped, blue_shorts, bracelet, midriff, large_breasts, armpits | | 3 | 49 | ![](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, plaid_skirt, smile, solo, school_uniform, pleated_skirt, open_mouth, blush, looking_at_viewer, scrunchie, bracelet | | 4 | 7 | ![](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, midriff, navel, skirt, solo, cleavage, highleg_panties, jacket, looking_at_viewer, smile, race_queen, :3, blush, jewelry, umbrella | | 5 | 6 | ![](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) | elbow_gloves, wedding_dress, 1girl, bare_shoulders, hair_flower, open_mouth, smile, solo, blush, cleavage, veil, sunflower, white_gloves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | open_mouth | solo | :d | navel | side-tie_bikini_bottom | striped_bikini | blush | cleavage | day | front-tie_top | large_breasts | smile | midriff | skirt | star_(symbol) | wristband | orange_hair | striped | blue_shorts | bracelet | armpits | plaid_skirt | school_uniform | pleated_skirt | scrunchie | highleg_panties | jacket | race_queen | :3 | jewelry | umbrella | elbow_gloves | wedding_dress | bare_shoulders | hair_flower | veil | sunflower | white_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:-------|:-----|:--------|:-------------------------|:-----------------|:--------|:-----------|:------|:----------------|:----------------|:--------|:----------|:--------|:----------------|:------------|:--------------|:----------|:--------------|:-----------|:----------|:--------------|:-----------------|:----------------|:------------|:------------------|:---------|:-------------|:-----|:----------|:-----------|:---------------|:----------------|:-----------------|:--------------|:-------|:------------|:---------------| | 0 | 5 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 29 | ![](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 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | X | | X | | | | X | | | X | X | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | 3 | 49 | ![](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 | | | | | | | | | | | | | | | 4 | 7 | ![](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 | | | | | | | | | 5 | 6 | ![](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 |

CyberHarem/hino_akane_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T00:01:10+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T16:25:43+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of hino\_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls) =================================================================== This is the dataset of hino\_akane/日野茜/히노아카네 (THE iDOLM@STER: Cinderella Girls), containing 424 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, ponytail, green\_eyes, breasts, high\_ponytail, bow, hair\_bow, medium\_breasts, aqua\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

1c77675649aca64435d44b664b648c16eefef79f

# Dataset of ichihara_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls) This is the dataset of ichihara_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are `long_hair, brown_hair, brown_eyes, bangs, blunt_bangs, bow`, 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 | 489 | 430.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 489 | 301.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1004 | 591.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 489 | 399.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1004 | 750.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ichihara_nina_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/ichihara_nina_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 | 8 | ![](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, :d, blush, kigurumi, open_mouth, solo, rabbit_costume, sitting | | 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, animal_costume, bell, kigurumi, sheep_ears, solo, open_mouth, horns, striped_pantyhose, :d | | 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, animal_costume, blush, kigurumi, open_mouth, pantyhose, polka_dot_legwear, solo, :d, tail, animal_ears, animal_hat, looking_at_viewer, paw_gloves | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | :d, blush, open_mouth, white_background, white_shirt, 1girl, arm_up, black_shorts, looking_at_viewer, short_sleeves, simple_background, solo, striped, hair_bow, short_shorts, animal_hood, black_bow, blue_bow, collared_shirt, very_long_hair | | 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, hood_up, rabbit_hood, solo, blush, hooded_jacket, long_sleeves, white_background, looking_at_viewer, pink_skirt, simple_background, :d, open_mouth, full_body, pom_pom_(clothes), rabbit_ears, shoes, blue_shirt, brown_jacket, fake_animal_ears, open_jacket, pink_footwear, striped_pantyhose, striped_thighhighs | | 5 | 19 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, solo, blush, looking_at_viewer, open_mouth, two_side_up, demon_tail, :d, demon_wings, frilled_dress, detached_sleeves, striped, fangs, bat_hair_ornament, demon_horns, long_sleeves, simple_background, asymmetrical_legwear, black_gloves, white_background, fake_horns, single_thighhigh, frilled_hairband, full_body, orange_hair, puffy_sleeves, single_sock | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :d | blush | kigurumi | open_mouth | solo | rabbit_costume | sitting | animal_costume | bell | sheep_ears | horns | striped_pantyhose | pantyhose | polka_dot_legwear | tail | animal_ears | animal_hat | looking_at_viewer | paw_gloves | white_background | white_shirt | arm_up | black_shorts | short_sleeves | simple_background | striped | hair_bow | short_shorts | animal_hood | black_bow | blue_bow | collared_shirt | very_long_hair | hood_up | rabbit_hood | hooded_jacket | long_sleeves | pink_skirt | full_body | pom_pom_(clothes) | rabbit_ears | shoes | blue_shirt | brown_jacket | fake_animal_ears | open_jacket | pink_footwear | striped_thighhighs | two_side_up | demon_tail | demon_wings | frilled_dress | detached_sleeves | fangs | bat_hair_ornament | demon_horns | asymmetrical_legwear | black_gloves | fake_horns | single_thighhigh | frilled_hairband | orange_hair | puffy_sleeves | single_sock | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----|:--------|:-----------|:-------------|:-------|:-----------------|:----------|:-----------------|:-------|:-------------|:--------|:--------------------|:------------|:--------------------|:-------|:--------------|:-------------|:--------------------|:-------------|:-------------------|:--------------|:---------|:---------------|:----------------|:--------------------|:----------|:-----------|:---------------|:--------------|:------------|:-----------|:-----------------|:-----------------|:----------|:--------------|:----------------|:---------------|:-------------|:------------|:--------------------|:--------------|:--------|:-------------|:---------------|:-------------------|:--------------|:----------------|:---------------------|:--------------|:-------------|:--------------|:----------------|:-------------------|:--------|:--------------------|:--------------|:-----------------------|:---------------|:-------------|:-------------------|:-------------------|:--------------|:----------------|:--------------| | 0 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | X | X | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | X | | | | | | | | | | | | | | | | | | 5 | 19 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | | X | X | | | | | | | | | | | | | X | | X | | | | | X | X | | | | | | | | | | | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/ichihara_nina_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T00:36:52+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T13:31:26+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of ichihara\_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls) ================================================================= This is the dataset of ichihara\_nina/市原仁奈 (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, brown\_eyes, bangs, blunt\_bangs, bow', 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

5863214ab1aa7be2e0294e653a7a4731ec65c4ad

# Dataset of takamori_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls) This is the dataset of takamori_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `brown_hair, brown_eyes, long_hair, single_hair_bun, hair_ornament, hair_bun, sidelocks`, 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 | 572.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 381.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1126 | 785.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 525.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1126 | 1016.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takamori_aiko_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/takamori_aiko_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 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | blush, looking_at_viewer, 1girl, bowtie, school_uniform, white_shirt, cardigan, pleated_skirt, ponytail, solo, bangs, miniskirt, open_mouth, plaid_skirt, :d, closed_mouth, microphone, sweater, wristband | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, hair_flower, smile, blush, dress, solo, looking_at_viewer, ponytail, open_mouth | | 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, looking_at_viewer, solo, white_background, navel, simple_background, white_bikini, small_breasts, micro_bikini, open_mouth, smile | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, frilled_bikini, looking_at_viewer, solo, :d, collarbone, navel, open_mouth, outdoors, barefoot, beach, hair_flower, ocean, ponytail, sitting, water, wet, bangs, cloud, day, sky, small_breasts, sparkle | | 4 | 5 | ![](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, looking_at_viewer, naked_towel, solo, water, collarbone, onsen, sitting, bangs, cleavage, open_mouth, partially_submerged, small_breasts, steam, wet, :d, bare_shoulders, bathing, medium_breasts | | 5 | 10 | ![](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, navel, blush, nipples, small_breasts, solo, female_pubic_hair, looking_at_viewer, smile, open_mouth, nude, sweat, pussy, on_back, panties | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1girl, solo, looking_at_viewer, torn_pantyhose, epaulettes, naval_uniform, ponytail, peaked_cap, blush, military_hat, skirt, black_pantyhose, breasts, navel | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | looking_at_viewer | 1girl | bowtie | school_uniform | white_shirt | cardigan | pleated_skirt | ponytail | solo | bangs | miniskirt | open_mouth | plaid_skirt | :d | closed_mouth | microphone | sweater | wristband | hair_flower | smile | dress | white_background | navel | simple_background | white_bikini | small_breasts | micro_bikini | frilled_bikini | collarbone | outdoors | barefoot | beach | ocean | sitting | water | wet | cloud | day | sky | sparkle | naked_towel | onsen | cleavage | partially_submerged | steam | bare_shoulders | bathing | medium_breasts | nipples | female_pubic_hair | nude | sweat | pussy | on_back | panties | torn_pantyhose | epaulettes | naval_uniform | peaked_cap | military_hat | skirt | black_pantyhose | breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:---------|:-----------------|:--------------|:-----------|:----------------|:-----------|:-------|:--------|:------------|:-------------|:--------------|:-----|:---------------|:-------------|:----------|:------------|:--------------|:--------|:--------|:-------------------|:--------|:--------------------|:---------------|:----------------|:---------------|:-----------------|:-------------|:-----------|:-----------|:--------|:--------|:----------|:--------|:------|:--------|:------|:------|:----------|:--------------|:--------|:-----------|:----------------------|:--------|:-----------------|:----------|:-----------------|:----------|:--------------------|:-------|:--------|:--------|:----------|:----------|:-----------------|:-------------|:----------------|:-------------|:---------------|:--------|:------------------|:----------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | | | | | X | X | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | | | | | X | X | X | | X | | X | | | | | X | | | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | 10 | ![](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 | | | | | | | | | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | | | | | | X | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |

CyberHarem/takamori_aiko_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T01:40:51+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T14:31:36+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of takamori\_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls) ================================================================= This is the dataset of takamori\_aiko/高森藍子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'brown\_hair, brown\_eyes, long\_hair, single\_hair\_bun, hair\_ornament, hair\_bun, sidelocks', 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

cbcd46dd1ddcdf9903936119be71951c91915800

# Summary `hindi-headline-article-generation` is an open source dataset of instruct-style records generated from the [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset. This was created as part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) License. Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 # Dataset Overview `hindi-headline-article-generation` is a corpus of records generated by conversion of [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset into Instruct-Style format. This Dataset can be used for the following task: - Given a headline, generate an article. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. # Dataset ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('ganeshjcs/hindi-headline-article-generation') ``` ## Purpose of Collection This was created as a part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **[Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus)**: Converted this dataset into Instruct-style prompts and completions. ## Data Fields - `inputs` : Prompt or input to the language model. - `targets` : Completion or output of the language model. - `template_id` : Id of the template used in `inputs` and `targets`. - `template_lang`: ISO code of the language used in the `inputs` and `targets` where *hin* refers to Hindi. ## Templates For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. | template_id | inputs | targets | |-------------|--------|---------| | 0 | ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` | ```यह एक लेख है: {{Article}}``` | | 1 | ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {Title}}``` | ```लेख: {{Article}}``` | | 2 | ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {{Title}}``` | ```{{Article}}``` | | 3 | ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {{Title}}``` | ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` | | 4 | ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` | ```{{Article}}``` | | 5 | ```इसके लिए एक लेख लिखें: {{Title}}``` | ```लेख: {{Article}}``` | | 6 | ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` | ```यह एक लेख है: {{Article}}``` | | 7 | ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` | ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` | | 8 | ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` | ```लेख: {{Article}}``` | | 9 | ```इसके लिए एक लेख लिखें: {{Title}}``` | ```{{Article}}``` | | 10 | ```इसके लिए एक लेख लिखें: {{Title}}``` | ```यह एक लेख है: {{Article}}``` | | 11 | ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` | ```लेख: {{Article}}``` | | 12 | ```इसके लिए एक लेख लिखें: {{Title}}``` | ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` | | 13 | ```यह शीर्षक है, इसके लिए एक लेख लिखें: {{Title}}``` | ```दिए गए शीर्षक के अनुरूप एक पाठ यह हो सकता है: {{Article}}``` | | 14 | ```एक लेख लिखें जिसका शीर्षक इस प्रकार है: {{Title}}``` | ```यह एक लेख है: {{Article}}``` | | 15 | ```इस शीर्षक के साथ एक लेख लिखें: {{Title}}``` | ```{{Article}}``` | ## Personal or Sensitive Data This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language Hindi # Known Limitations - The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. - Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. # Contributors [Ganesh Jagadeesan](https://github.com/ElefHead)

ganeshjcs/hindi-headline-article-generation

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus", "language:hi", "license:cc-by-sa-4.0", "generation", "region:us" ]

2023-09-14T01:53:23+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["hi"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Hindi Article Generation", "tags": ["generation"]}

2024-01-28T19:19:13+00:00

[]

[ "hi" ]

TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us

Summary ======= 'hindi-headline-article-generation' is an open source dataset of instruct-style records generated from the Hindi Text Short and Large Summarization dataset. This was created as part of Aya Open Science Initiative from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY-SA 4.0 License. Supported Tasks: * Training LLMs * Synthetic Data Generation * Data Augmentation Languages: Hindi Version: 1.0 Dataset Overview ================ 'hindi-headline-article-generation' is a corpus of records generated by conversion of Hindi Text Short and Large Summarization dataset into Instruct-Style format. This Dataset can be used for the following task: * Given a headline, generate an article. Intended Uses ============= While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. Dataset ======= Load with Datasets ------------------ To load this dataset with Datasets, you'll just need to install Datasets as 'pip install datasets --upgrade' and then use the following code: Purpose of Collection --------------------- This was created as a part of Aya Open Science Initiative from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. Sources ------- * Hindi Text Short and Large Summarization Corpus: Converted this dataset into Instruct-style prompts and completions. Data Fields ----------- * 'inputs' : Prompt or input to the language model. * 'targets' : Completion or output of the language model. * 'template\_id' : Id of the template used in 'inputs' and 'targets'. * 'template\_lang': ISO code of the language used in the 'inputs' and 'targets' where *hin* refers to Hindi. Templates --------- For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. template\_id: 0, inputs: , targets: template\_id: 1, inputs: , targets: template\_id: 2, inputs: , targets: template\_id: 3, inputs: , targets: template\_id: 4, inputs: , targets: template\_id: 5, inputs: , targets: template\_id: 6, inputs: , targets: template\_id: 7, inputs: , targets: template\_id: 8, inputs: , targets: template\_id: 9, inputs: , targets: template\_id: 10, inputs: , targets: template\_id: 11, inputs: , targets: template\_id: 12, inputs: , targets: template\_id: 13, inputs: , targets: template\_id: 14, inputs: , targets: template\_id: 15, inputs: , targets: Personal or Sensitive Data -------------------------- This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. Language -------- Hindi Known Limitations ================= * The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. * Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. Contributors ============ Ganesh Jagadeesan

[]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]

[ 126 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]

98d8e08f3eb1daf53d7cdd7bf505ea52b72695b5

# Dataset of satou_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls) This is the dataset of satou_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls), containing 429 images and their tags. The core tags of this character are `green_eyes, ahoge, long_hair, breasts, twintails, bangs, hair_ornament, blonde_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 | 429 | 566.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 429 | 334.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1052 | 735.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 429 | 500.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1052 | 1.00 GiB | [Download](https://huggingface.co/datasets/CyberHarem/satou_shin_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/satou_shin_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 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, hair_bow, looking_at_viewer, smile, solo, choker, frills, ribbon, open_mouth, cleavage, dress, medium_breasts, one_eye_closed, skirt, hairclip, heart_ahoge, heart_hair_ornament, navel, pink_bow, ;d, simple_background, white_background | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, looking_at_viewer, solo, hair_ribbon, cleavage, medium_breasts, smile, bare_shoulders, pink_dress, simple_background, white_background, hair_bow, upper_body, frills, heart_ahoge, wrist_cuffs | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, one_eye_closed, smile, solo, blush, tongue_out, wrist_cuffs, ;q, heart_hands, bare_shoulders, dress, necktie, polka_dot, skirt, wings | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, blush, looking_at_viewer, red_dress, solo, bow, cleavage, hair_ribbon, sleeveless_dress, smile, wrist_cuffs, heart_hair_ornament, medium_breasts, nail_polish, side_ponytail, valentine, box, holding_gift | | 4 | 5 | ![](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, ;d, blush, cleavage, looking_at_viewer, navel, one_eye_closed, open_mouth, smile, solo, collarbone, heart, large_breasts, bare_shoulders, pink_bikini, simple_background, cowboy_shot, frilled_bikini, holding, medium_breasts, thigh_gap, thighs, water_gun, 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, beach, blue_sky, blush, cleavage, cloud, day, looking_at_viewer, medium_breasts, navel, ocean, outdoors, smile, solo, collarbone, heart_ahoge, jewelry, side-tie_bikini_bottom, yellow_bikini, :q, ;q, barefoot, beer, front-tie_top, holding, one_eye_closed, wariza | | 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) | looking_at_viewer, school_uniform, short_sleeves, 1girl, blush, bowtie, cardigan_around_waist, collared_shirt, double_bun, hair_bow, one_eye_closed, pleated_skirt, smile, solo, white_shirt, blue_bow, nail_polish, open_mouth, pink_nails, ring, simple_background, ;d, bracelet, brown_hair, cellphone, dress_shirt, heart, holding_phone, kogal, plaid_skirt, sweater_around_waist, white_background | | 7 | 7 | ![](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) | blush, hetero, large_breasts, navel, nipples, penis, solo_focus, spread_legs, 1boy, 1girl, nude, sex, vaginal, looking_at_viewer, sweat, mosaic_censoring, open_mouth, pov, smile, swept_bangs, bar_censor, cum_in_pussy, hair_bobbles, heart_ahoge, missionary, on_back, pillow, pubic_hair, pussy_juice, thighs | | 8 | 9 | ![](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, blush, hair_flower, solo, looking_at_viewer, smile, floral_print, umbrella, upper_body, white_kimono | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | hair_bow | looking_at_viewer | smile | solo | choker | frills | ribbon | open_mouth | cleavage | dress | medium_breasts | one_eye_closed | skirt | hairclip | heart_ahoge | heart_hair_ornament | navel | pink_bow | ;d | simple_background | white_background | hair_ribbon | bare_shoulders | pink_dress | upper_body | wrist_cuffs | tongue_out | ;q | heart_hands | necktie | polka_dot | wings | red_dress | bow | sleeveless_dress | nail_polish | side_ponytail | valentine | box | holding_gift | collarbone | heart | large_breasts | pink_bikini | cowboy_shot | frilled_bikini | holding | thigh_gap | thighs | water_gun | beach | blue_sky | cloud | day | ocean | outdoors | jewelry | side-tie_bikini_bottom | yellow_bikini | :q | barefoot | beer | front-tie_top | wariza | school_uniform | short_sleeves | bowtie | cardigan_around_waist | collared_shirt | double_bun | pleated_skirt | white_shirt | blue_bow | pink_nails | ring | bracelet | brown_hair | cellphone | dress_shirt | holding_phone | kogal | plaid_skirt | sweater_around_waist | hetero | nipples | penis | solo_focus | spread_legs | 1boy | nude | sex | vaginal | sweat | mosaic_censoring | pov | swept_bangs | bar_censor | cum_in_pussy | hair_bobbles | missionary | on_back | pillow | pubic_hair | pussy_juice | hair_flower | floral_print | umbrella | white_kimono | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------|:--------------------|:--------|:-------|:---------|:---------|:---------|:-------------|:-----------|:--------|:-----------------|:-----------------|:--------|:-----------|:--------------|:----------------------|:--------|:-----------|:-----|:--------------------|:-------------------|:--------------|:-----------------|:-------------|:-------------|:--------------|:-------------|:-----|:--------------|:----------|:------------|:--------|:------------|:------|:-------------------|:--------------|:----------------|:------------|:------|:---------------|:-------------|:--------|:----------------|:--------------|:--------------|:-----------------|:----------|:------------|:---------|:------------|:--------|:-----------|:--------|:------|:--------|:-----------|:----------|:-------------------------|:----------------|:-----|:-----------|:-------|:----------------|:---------|:-----------------|:----------------|:---------|:------------------------|:-----------------|:-------------|:----------------|:--------------|:-----------|:-------------|:-------|:-----------|:-------------|:------------|:--------------|:----------------|:--------|:--------------|:-----------------------|:---------|:----------|:--------|:-------------|:--------------|:-------|:-------|:------|:----------|:--------|:-------------------|:------|:--------------|:-------------|:---------------|:---------------|:-------------|:----------|:---------|:-------------|:--------------|:--------------|:---------------|:-----------|:---------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 13 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | | X | | | X | | X | | | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | | X | X | X | | | | | | X | | X | X | | | | | | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | X | X | | | | | X | | X | | | | | X | | | | | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | X | X | X | | | | | X | | X | X | | | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | X | X | X | | | | X | | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 7 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | | X | X | | | | | X | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | 8 | 9 | ![](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 |

CyberHarem/satou_shin_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T02:06:20+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T15:24:16+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of satou\_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls) ============================================================= This is the dataset of satou\_shin/佐藤心 (THE iDOLM@STER: Cinderella Girls), containing 429 images and their tags. The core tags of this character are 'green\_eyes, ahoge, long\_hair, breasts, twintails, bangs, hair\_ornament, blonde\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

37990f7731a2d02b85e7024d37b62706b83f05cc

# Dataset Card for "shiba-inu-fenda-dreambooth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

zxx-silence/shiba-inu-fenda-dreambooth

[ "region:us" ]

2023-09-14T02:08:27+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2920788.0, "num_examples": 10}], "download_size": 2921904, "dataset_size": 2920788.0}}

2023-11-02T03:06:27+00:00

[]

TAGS #region-us

# Dataset Card for "shiba-inu-fenda-dreambooth" More Information needed

[ "# Dataset Card for \"shiba-inu-fenda-dreambooth\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"shiba-inu-fenda-dreambooth\"\n\nMore Information needed" ]

[ 6, 21 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"shiba-inu-fenda-dreambooth\"\n\nMore Information needed" ]

2e0f5a7db4ea29537a136be102452826f096d647

# Summary `hindi-article-summarization` is an open source dataset of instruct-style records generated from the [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset. This was created as part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) License. Supported Tasks: - Training LLMs - Synthetic Data Generation - Data Augmentation Languages: Hindi Version: 1.0 # Dataset Overview `hindi-article-summarization` is a corpus of records generated by conversion of [Hindi Text Short and Large Summarization](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus) dataset into Instruct-Style format. This Dataset can be used for the following task: - Given an article, generate its summary. # Intended Uses While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. # Dataset ## Load with Datasets To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code: ```python from datasets import load_dataset ds = load_dataset('ganeshjcs/hindi-article-summarization') ``` ## Purpose of Collection This was created as a part of [Aya Open Science Initiative](https://sites.google.com/cohere.com/aya-en/home) from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. ## Sources - **[Hindi Text Short and Large Summarization Corpus](https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus)**: Converted this dataset into Instruct-style prompts and completions. ## Data Fields - `inputs` : Prompt or input to the language model. - `targets` : Completion or output of the language model. - `template_id` : Id of the template used in `inputs` and `targets`. - `template_lang`: ISO code of the language used in the `inputs` and `targets` where *hin* refers to Hindi. ## Templates For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. | template_id | inputs | targets | |-------------|--------|---------| | 0 | ```इस पाठ का सारांश बनाओ: {{Article}}``` | ```संक्षिप्त सारांश: {{Summary}}``` | | 1 | ```इस पाठ का सारांश बनाओ: {Article}}``` | ``` {{Summary}}``` | | 2 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```यह एक सारांश है: {{Summary}}``` | | 3 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` | | 4 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```यहाँ एक सारांश है: {{Summary}}``` | | 5 | ```एक सारांश बनाओ: {{Article}}``` | ```सारांश: {{Summary}}``` | | 6 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```{{Summary}}``` | | 7 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```सारांश: {{Summary}}``` | | 8 | ```एक सारांश बनाओ: {{Article}}``` | ```संक्षिप्त सारांश: {{Summary}}``` | | 9 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```यह एक सारांश है: {{Summary}}``` | | 10 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```संक्षिप्त सारांश: {{Summary}}``` | | 11 | ```इस पाठ का सारांश बनाओ: {{Article}}``` | ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` | | 12 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```यहाँ एक सारांश है: {{Summary}}``` | | 13 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```संक्षिप्त पाठ: {{Summary}}``` | | 14 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```संक्षिप्त पाठ: {{Summary}}``` | | 15 | ```एक सारांश बनाओ: {{Article}}``` | ```यहाँ एक सारांश है: {{Summary}}``` | | 16 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```यह एक सारांश है: {{Summary}}``` | | 17 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```यहाँ एक सारांश है: {{Summary}}``` | | 18 | ```इस पाठ का सारांश बनाओ: {{Article}}``` | ```यहाँ एक सारांश है: {{Summary}}``` | | 19 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` | | 20 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```सारांश: {{Summary}}``` | | 21 | ```एक सारांश बनाओ: {{Article}}``` | ```यह एक सारांश है: {{Summary}}``` | | 22 | ```दिए गए पाठ के लिए एक सारांश बनाएं: {{Article}}``` | ```संक्षिप्त पाठ: {{Summary}}``` | | 23 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```संक्षिप्त सारांश: {{Summary}}``` | | 24 | ```इस पाठ का सारांश बनाओ: {{Article}}``` | ```यह एक सारांश है: {{Summary}}``` | | 25 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` | | 26 | ```एक सारांश बनाओ: {{Article}}``` | ```{{Summary}}``` | | 27 | ```इस पाठ का सारांश बनाओ: {{Article}}``` | ```संक्षिप्त पाठ: {{Summary}}``` | | 28 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```{{Summary}}``` | | 29 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```संक्षिप्त सारांश: {{Summary}}``` | | 30 | ```एक सारांश बनाओ: {{Article}}``` | ```संक्षिप्त पाठ: {{Summary}}``` | | 31 | ```इस के लिए एक सारांश बनाएं: {{Article}}``` | ```सारांश: {{Summary}}``` | | 32 | ```एक सारांश बनाओ: {{Article}}``` | ```दिए गए पाठ का सारांश यह हो सकता है: {{Summary}}``` | | 33 | ```इस पाठ का सारांश बनाओ: {{Article}}``` | ```सारांश: {{Summary}}``` | | 34 | ```इस पाठ का सारांश बनाएं: {{Article}}``` | ```{{Summary}}``` | ## Personal or Sensitive Data This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. ## Language Hindi # Known Limitations - The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. - Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. # Contributors [Ganesh Jagadeesan](https://github.com/ElefHead)

ganeshjcs/hindi-article-summarization

[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus", "language:hi", "license:cc-by-sa-4.0", "generation", "region:us" ]

2023-09-14T02:15:29+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["hi"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["https://www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "Hindi Article Summarization", "tags": ["generation"]}

2024-01-29T21:29:39+00:00

[]

[ "hi" ]

TAGS #task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us

Summary ======= 'hindi-article-summarization' is an open source dataset of instruct-style records generated from the Hindi Text Short and Large Summarization dataset. This was created as part of Aya Open Science Initiative from Cohere For AI. This dataset can be used for any purpose, whether academic or commercial, under the terms of the CC BY-SA 4.0 License. Supported Tasks: * Training LLMs * Synthetic Data Generation * Data Augmentation Languages: Hindi Version: 1.0 Dataset Overview ================ 'hindi-article-summarization' is a corpus of records generated by conversion of Hindi Text Short and Large Summarization dataset into Instruct-Style format. This Dataset can be used for the following task: * Given an article, generate its summary. Intended Uses ============= While immediately valuable for instruction fine tuning large language models, as a corpus of instruction prompts, this dataset also presents a valuable opportunity for synthetic data generation in the methods. For example, prompt-completions could be submitted as few-shot examples to a large open language model to generate sentence and corresponding paraphrased sentence. Dataset ======= Load with Datasets ------------------ To load this dataset with Datasets, you'll just need to install Datasets as 'pip install datasets --upgrade' and then use the following code: Purpose of Collection --------------------- This was created as a part of Aya Open Science Initiative from Cohere For AI to make sure Hindi is well represented in the space of AI/ML. This dataset can be used, modified, and extended for any purpose, including academic or commercial applications. Sources ------- * Hindi Text Short and Large Summarization Corpus: Converted this dataset into Instruct-style prompts and completions. Data Fields ----------- * 'inputs' : Prompt or input to the language model. * 'targets' : Completion or output of the language model. * 'template\_id' : Id of the template used in 'inputs' and 'targets'. * 'template\_lang': ISO code of the language used in the 'inputs' and 'targets' where *hin* refers to Hindi. Templates --------- For the creation of instruct-style prompts and completions from the original dataset, the following one template category with 6 different variations were used: 1. Given a sentence, generate a sentence with similar meaning. template\_id: 0, inputs: , targets: template\_id: 1, inputs: , targets: template\_id: 2, inputs: , targets: template\_id: 3, inputs: , targets: template\_id: 4, inputs: , targets: template\_id: 5, inputs: , targets: template\_id: 6, inputs: , targets: template\_id: 7, inputs: , targets: template\_id: 8, inputs: , targets: template\_id: 9, inputs: , targets: template\_id: 10, inputs: , targets: template\_id: 11, inputs: , targets: template\_id: 12, inputs: , targets: template\_id: 13, inputs: , targets: template\_id: 14, inputs: , targets: template\_id: 15, inputs: , targets: template\_id: 16, inputs: , targets: template\_id: 17, inputs: , targets: template\_id: 18, inputs: , targets: template\_id: 19, inputs: , targets: template\_id: 20, inputs: , targets: template\_id: 21, inputs: , targets: template\_id: 22, inputs: , targets: template\_id: 23, inputs: , targets: template\_id: 24, inputs: , targets: template\_id: 25, inputs: , targets: template\_id: 26, inputs: , targets: template\_id: 27, inputs: , targets: template\_id: 28, inputs: , targets: template\_id: 29, inputs: , targets: template\_id: 30, inputs: , targets: template\_id: 31, inputs: , targets: template\_id: 32, inputs: , targets: template\_id: 33, inputs: , targets: template\_id: 34, inputs: , targets: Personal or Sensitive Data -------------------------- This dataset contains public information. To our knowledge, there are no private person’s personal identifiers or sensitive information. Language -------- Hindi Known Limitations ================= * The Dataset is converted from the existing dataset and the contents of this dataset may reflect the bias, factual errors and sensitive matters. * Although there is utmost care taken to keep the dataset as monolingual, there might be some records that may contain English Language along with Hindi. Contributors ============ Ganesh Jagadeesan

[]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]

[ 126 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//www.kaggle.com/datasets/disisbig/hindi-text-short-summarization-corpus #language-Hindi #license-cc-by-sa-4.0 #generation #region-us \n" ]

ead98be61d862d271b09ab423a39b73d3d7d04f4

# AutoTrain Dataset for project: big ## Dataset Description This dataset has been automatically processed by AutoTrain for project big. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "feat_Unnamed: 0.1": 4865, "feat_Unnamed: 0": 754, "target": "\"Names\": \"Aidan\", \"Physical Appearance\": \"Tall and lanky build\", \"Location\": \"A lively street filled with buskers\", \"Romantic Relationships\": \"Happily committed to Taylor\", \"Sexuality\": \"Heterosexual\", \"Personality Traits\": \"Meticulous\", \"Secrets\": \"Holds the key to a valuable treasure\", \"Physical Appearance\": \"Tall and lanky build\", \"Skills & Talents\": \"Talent for creating memorable experiences\", \"Hobbies or Interests\": \"Playing board games\", \"Motivations\": \"Search for personal identity\", \"Flaws\": \"Excessive pride leading to underestimating others\", \"Personality Traits\": \"Innovative\", \"Familial Relationships\": \"Eleanor (grandmother)\", \"Personal Beliefs and Values\": \"Commitment to promoting equality and inclusion for all\", \"Tone & Mood\": \"Playful teasing among friends\", \"Political Views\": \"Total apathy toward politics\", \"Hobbies or Interests\": \"Playing board games\", \"Fear or Desire for Change\": \"Desire for stability and routine\", \"Motivations\": \"Quest for personal identity\", \"Personality Traits\": \"Introverted\"", "feat_Names": "Aidan", "feat_Personality Traits": "Introverted", "feat_Motivations": "Quest for personal identity", "feat_Action": null, "feat_Feelings": null, "feat_Tone & Mood": "Playful teasing among friends", "feat_Friendships and Alliances": null, "feat_Pets": null, "feat_Romantic Relationships": "Happily committed to Taylor", "feat_Familial Relationships": "Eleanor (grandmother)", "feat_Location": "A lively street filled with buskers", "feat_Action Taken": null, "feat_Personal Beliefs and Values": "Commitment to promoting equality and inclusion for all", "feat_Socioeconomic Status": null, "feat_Hobbies or Interests": "Playing board games", "feat_Physical Appearance": "Tall and lanky build", "feat_Skills & Talents": "Talent for creating memorable experiences", "feat_Fear or Desire for Change": "Desire for stability and routine", "feat_Family": null, "feat_Background and History": null, "feat_Goals and Aspirations": null, "feat_Thoughts and Inner Monologues": null, "feat_External Pressures or Influences": null, "feat_Flaws": "Excessive pride leading to underestimating others", "feat_hairstyle": null, "feat_Thoughts": null, "feat_Status": null, "feat_Past Experiences Influencing Actions": null, "feat_Phrase": null, "feat_, \"Weight": null, "feat_Rivalries or Conflicts": null, "feat_Marital Status": null, "feat_Weather": null, "feat_Reasons for Feelings": null, "feat_Cultural Practices": null, "feat_Moral or Ethical Dilemmas": null, "feat_Political Views": "Total apathy toward politics", "feat_Mental or Emotional States": null, "feat_Age": null, "feat_Medical Conditions": null, "feat_Contentment": null, "feat_Personal Interests": null, "feat_Clothing": null, "feat_Secrets": "Holds the key to a valuable treasure", "feat_Background": null, "feat_Cultural Practices or Traditions": null, "feat_Personal Feelings": null, "feat_Parenting": null, "feat_Goals": null, "feat_Reaction": null, "feat_Fears": null, "feat_Physical Characteristics": null, "feat_Gender": null, "feat_Mental Health": null, "feat_Knowledge & Education": null, "feat_Fears & Anxieties": null, "feat_Political Affiliation": null, "feat_Romantic Status": null, "feat_Friendships": null, "feat_Gratitude Expressed": null, "feat_Morals": null, "feat_Personal Possessions": null, "feat_Wishes or Aspirations": null, "feat_Academic and Career Goals": null, "feat_Financial Status": null, "feat_Reasons for Action": null, "feat_Knowledge or Skills": null, "feat_Nationality": null, "feat_Action Tendencies": null, "feat_Feelings Toward School": null, "feat_Occupation": null, "feat_tone_attitude": null, "feat_emotional_state": null, "feat_pastimes_hobbies": null, "feat_Quotes": null, "feat_tone": null, "feat_question": null, "feat_Content_Tone": null, "feat_Past Medical History": null, "feat_Clothing or Fashion": null, "feat_Clothing & Accessories": null, "feat_Personal Challenges": null, "feat_Eye Color": null, "feat_Physical Sensations": null, "feat_Siblings": null, "feat_Speech & Dialogue": null, "feat_Relationships": null, "feat_Regrets": null, "feat_Time Period": null, "feat_Factual Information": null, "feat_Moral Dilemmas": null, "feat_Personal Struggles": null, "feat_Mental and Emotional Health": null, "feat_Phobias": null, "feat_FearOrWorry": null, "feat_Personal Motivations": null, "feat_Religious Beliefs and Practices": null, "feat_Thoughts and Opinions": null, "feat_Romantic Tensions": null, "feat_Current Location": null, "feat_Fears & Worries": null, "feat_Career": null, "feat_External Pressures": null, "feat_Sexual Orientation": null, "feat_Role or Occupation": null, "feat_Friendships_and_Relationships": null, "feat_Opinions": null, "feat_Appearance": null, "feat_Likes": null, "feat_Resolutions": null, "feat_Tone & Attitude": null, "feat_Instructions or Directions": null, "feat_Position or Job Title": null, "feat_assumptions_made": null, "feat_Animal": null, "feat_Manner of Speaking": null, "feat_Self-Perception": null, "feat_Subjective Opinions": null, "feat_Statements of Belief": null, "feat_Responsibilities": null, "feat_Personal Sacrifices": null, "feat_Academic and Professional Background": null, "feat_Cultural Background": null, "feat_past_experiences_impacting_actions": null, "feat_ExternalFacts": null, "feat_Statements": null, "feat_No\", \"Names": null, "feat_Self-Reflection": null, "feat_Fact": null, "feat_Empathy Statements": null, "feat_Hobbies or Pastimes": null, "feat_Moral and Ethical Considerations": null, "feat_Personal Tastes or Preferences": null, "feat_Subject": null, "feat_Lifestyle Choices": null, "feat_Sensory Experiences": null, "feat_Personal History": null, "feat_Physical Health": null, "feat_Temptations": null, "feat_Home Life": null, "feat_What They're Avoiding": null, "feat_Hobbies": null, "feat_Speech Characteristics": null, "feat_What They Do": null, "feat_Personal Relationships": null, "feat_What They Fear Most": null, "feat_Psychological Disorders": null, "feat_Ethnicity": null, "feat_Romantic Ideals": null, "feat_Ailments & Health Conditions": null, "feat_Education": null, "feat_Sentence Structure": null, "feat_Likes and Dislikes": null, "feat_Profession": null, "feat_Advice": null, "feat_Habits or Tendencies": null, "feat_Bad Habits or Flaws": null, "feat_Personal Motto or Philosophy": null, "feat_Quirks": null, "feat_Prejudices or Biases": null, "feat_Grief or Emotional Pain": null, "feat_past_tense_verbs": null, "feat_sentiments_expressed": null, "feat_Lifestyle": null, "feat_Stereotypes & Bias": null, "feat_Cultural Norms": null, "feat_Childhood Memories": null, "feat_Cause of Death": null, "feat_fear_or_phobia": null, "feat_thoughts_and_feelings": null, "feat_Stance on Controversial Issues": null, "feat_Job": null, "text": "`` i never said i did n't want you two to get to know each other-i said i did n't want you using him for a fling . ''", "feat_context": null, "feat_hair_color": null, "feat_fashion_style": null, "feat_Requests or Directions": null, "feat_Religious Views": null, "feat_Conflicts": null, "feat_Guilt": null, "feat_Person": null, "feat_Positive Experiences": null, "feat_Personal Accomplishments": null, "feat_Backstory": null, "feat_Circumstances of Death": null, "feat_Children": null, "feat_Virtues": null, "feat_Sentiment Towards the Topic": null, "feat_Time": null, "feat_External Factors Impacting the User": null, "feat_Hair Color": null, "feat_Financial": null, "feat_Societal Norms or Pressures": null, "feat_Hopes and Dreams": null, "feat_Insecurities": null, "feat_Emotions Expressed": null, "feat_Biological Sex": null, "feat_Body": null, "feat_Lifestyle and Social Status": null, "feat_Affiliations": null, "feat_Negative Traits": null, "feat_Positive Traits": null, "feat_Physical Traits": null, "feat_Reaction to Conflict": null, "feat_Racial or Ethnic Identity": null, "feat_Emotions": null, "feat_Behavior": null, "feat_External Conflicts": null, "feat_familial_roles": null, "feat_Apology Languages": null, "feat_Worries or Anxieties": null, "feat_Direct Address": null, "feat_Enneagram Type": null, "feat_Employment": null, "feat_Rumors or Gossip": null, "feat_Romantic Tension or Conflict": null, "feat_Personal Philosophies and Weltanschauungen": null, "feat_Things That Make Them Happy": null, "feat_Love Interests": null, "feat_Negative Emotions": null, "feat_Physical Symptoms": null, "feat_skills": null, "feat_Question or Prompt": null, "feat_Setting": null, "feat_External Challenges or Obstacles": null, "feat_Obligations and Responsibilities": null, "feat_Attributes": null, "feat_Decision-Making Process": null, "feat_Thoughts and Prayers": null, "feat_Feelings of the Speaker": null, "feat_familial_status": null, "feat_voice": null, "feat_Events": null, "feat_Familial Duties and Conflicts": null, "feat_Origin": null, "feat_Sexuality": "Heterosexual", "feat_Fears & Phobias": null, "feat_past_experience": null, "feat_Transportation": null, "feat_Job Titles": null, "feat_Personal Philosophies": null, "feat_Contexts and Backgrounds": null, "feat_Senses": null, "feat_Sentiment Towards Technology": null, "feat_Content Preferences": null, "feat_Mannerisms": null, "feat_Gratitude Journals": null, "feat_Psychological Traits": null, "feat_Advice Taken": null, "feat_Answer": null, "feat_Sentiment": null, "feat_Personal Names": null, "feat_Time Phrases": null, "feat_Nickname": null, "feat_Role": null, "feat_Request": null, "feat_Response": null, "feat_Physical_Pain_or_Illness": null, "feat_Body_Parts": null, "feat_Skills_&_Abilities": null, "feat_Major Life Events": null, "feat_Opinions About Controversial Topics": null, "feat_home_town": null, "feat_Afraid of": null, "feat_Food Preferences": null, "feat_Relationships and Family": null, "feat_Body Language": null, "feat_No\", \"Mental or Physical Disabilities": null, "feat_Other Feelings": null, "feat_Question or Hypothesis": null, "feat_PastTenseVerbs": null, "feat_Dreams": null, "feat_Dietary Restrictions or Choices": null, "feat_Plans and Intentions": null, "feat_Nicknames or Aliases": null, "feat_Symbols or Motifs": null, "feat_Passions or Interests": null, "feat_Description": null, "feat_Moral Values": null, "feat_Question or query": null, "feat_external_appearances": null, "feat_Themes": null, "feat_Chapter": null, "feat_General Sentiment": null, "feat_Traits": null, "feat_Tone&Style": null, "feat_Revenge or Retribution": null, "feat_Strengths": null, "feat_What they want": null, "feat_Priorities": null, "feat_Compliments Received": null, "feat_Daily Routine": null, "feat_Timeframe": null, "feat_tactics_or_strategies": null, "feat_Societal Norms or Expectations": null, "feat_Outfit": null, "feat_sentiment_score": 0.1596837944664031 }, { "feat_Unnamed: 0.1": 3631, "feat_Unnamed: 0": 338, "target": "\"Names\": \"Becky\", \"Familial Relationships\": \"John (nephew)\", \"External Pressures or Influences\": \"Influence of a persuasive mentor in professional decisions\", \"Personality Traits\": \"Friendly\", \"Hobbies or Interests\": \"Playing the piano\", \"Location\": \"A small cottage in the countryside\", \"Feelings\": \"Confusion\", \"Socioeconomic Status\": \"Impoverished children in a developing nation\"", "feat_Names": "Becky", "feat_Personality Traits": "Friendly", "feat_Motivations": null, "feat_Action": null, "feat_Feelings": "Confusion", "feat_Tone & Mood": null, "feat_Friendships and Alliances": null, "feat_Pets": null, "feat_Romantic Relationships": null, "feat_Familial Relationships": "John (nephew)", "feat_Location": "A small cottage in the countryside", "feat_Action Taken": null, "feat_Personal Beliefs and Values": null, "feat_Socioeconomic Status": "Impoverished children in a developing nation", "feat_Hobbies or Interests": "Playing the piano", "feat_Physical Appearance": null, "feat_Skills & Talents": null, "feat_Fear or Desire for Change": null, 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"feat_fashion_style": null, "feat_Requests or Directions": null, "feat_Religious Views": null, "feat_Conflicts": null, "feat_Guilt": null, "feat_Person": null, "feat_Positive Experiences": null, "feat_Personal Accomplishments": null, "feat_Backstory": null, "feat_Circumstances of Death": null, "feat_Children": null, "feat_Virtues": null, "feat_Sentiment Towards the Topic": null, "feat_Time": null, "feat_External Factors Impacting the User": null, "feat_Hair Color": null, "feat_Financial": null, "feat_Societal Norms or Pressures": null, "feat_Hopes and Dreams": null, "feat_Insecurities": null, "feat_Emotions Expressed": null, "feat_Biological Sex": null, "feat_Body": null, "feat_Lifestyle and Social Status": null, "feat_Affiliations": null, "feat_Negative Traits": null, "feat_Positive Traits": null, "feat_Physical Traits": null, "feat_Reaction to Conflict": null, "feat_Racial or Ethnic Identity": null, "feat_Emotions": null, "feat_Behavior": null, "feat_External Conflicts": null, "feat_familial_roles": null, "feat_Apology Languages": null, "feat_Worries or Anxieties": null, "feat_Direct Address": null, "feat_Enneagram Type": null, "feat_Employment": null, "feat_Rumors or Gossip": null, "feat_Romantic Tension or Conflict": null, "feat_Personal Philosophies and Weltanschauungen": null, "feat_Things That Make Them Happy": null, "feat_Love Interests": null, "feat_Negative Emotions": null, "feat_Physical Symptoms": null, "feat_skills": null, "feat_Question or Prompt": null, "feat_Setting": null, "feat_External Challenges or Obstacles": null, "feat_Obligations and Responsibilities": null, "feat_Attributes": null, "feat_Decision-Making Process": null, "feat_Thoughts and Prayers": null, "feat_Feelings of the Speaker": null, "feat_familial_status": null, "feat_voice": null, "feat_Events": null, "feat_Familial Duties and Conflicts": null, "feat_Origin": null, "feat_Sexuality": null, "feat_Fears & Phobias": null, "feat_past_experience": null, "feat_Transportation": null, "feat_Job Titles": null, "feat_Personal Philosophies": null, "feat_Contexts and Backgrounds": null, "feat_Senses": null, "feat_Sentiment Towards Technology": null, "feat_Content Preferences": null, "feat_Mannerisms": null, "feat_Gratitude Journals": null, "feat_Psychological Traits": null, "feat_Advice Taken": null, "feat_Answer": null, "feat_Sentiment": null, "feat_Personal Names": null, "feat_Time Phrases": null, "feat_Nickname": null, "feat_Role": null, "feat_Request": null, "feat_Response": null, "feat_Physical_Pain_or_Illness": null, "feat_Body_Parts": null, "feat_Skills_&_Abilities": null, "feat_Major Life Events": null, "feat_Opinions About Controversial Topics": null, "feat_home_town": null, "feat_Afraid of": null, "feat_Food Preferences": null, "feat_Relationships and Family": null, "feat_Body Language": null, "feat_No\", \"Mental or Physical Disabilities": null, "feat_Other Feelings": null, "feat_Question or Hypothesis": null, "feat_PastTenseVerbs": null, "feat_Dreams": null, "feat_Dietary Restrictions or Choices": null, "feat_Plans and Intentions": null, "feat_Nicknames or Aliases": null, "feat_Symbols or Motifs": null, "feat_Passions or Interests": null, "feat_Description": null, "feat_Moral Values": null, "feat_Question or query": null, "feat_external_appearances": null, "feat_Themes": null, "feat_Chapter": null, "feat_General Sentiment": null, "feat_Traits": null, "feat_Tone&Style": null, "feat_Revenge or Retribution": null, "feat_Strengths": null, "feat_What they want": null, "feat_Priorities": null, "feat_Compliments Received": null, "feat_Daily Routine": null, "feat_Timeframe": null, "feat_tactics_or_strategies": null, "feat_Societal Norms or Expectations": null, "feat_Outfit": null, "feat_sentiment_score": 0.0642857142857142 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "feat_Unnamed: 0.1": "Value(dtype='int64', id=None)", "feat_Unnamed: 0": "Value(dtype='int64', id=None)", "target": 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id=None)", "feat_Reaction to Conflict": "Value(dtype='string', id=None)", "feat_Racial or Ethnic Identity": "Value(dtype='string', id=None)", "feat_Emotions": "Value(dtype='string', id=None)", "feat_Behavior": "Value(dtype='string', id=None)", "feat_External Conflicts": "Value(dtype='string', id=None)", "feat_familial_roles": "Value(dtype='string', id=None)", "feat_Apology Languages": "Value(dtype='string', id=None)", "feat_Worries or Anxieties": "Value(dtype='string', id=None)", "feat_Direct Address": "Value(dtype='string', id=None)", "feat_Enneagram Type": "Value(dtype='string', id=None)", "feat_Employment": "Value(dtype='string', id=None)", "feat_Rumors or Gossip": "Value(dtype='string', id=None)", "feat_Romantic Tension or Conflict": "Value(dtype='string', id=None)", "feat_Personal Philosophies and Weltanschauungen": "Value(dtype='string', id=None)", "feat_Things That Make Them Happy": "Value(dtype='string', id=None)", "feat_Love Interests": "Value(dtype='string', id=None)", "feat_Negative Emotions": "Value(dtype='string', id=None)", "feat_Physical Symptoms": "Value(dtype='string', id=None)", "feat_skills": "Value(dtype='string', id=None)", "feat_Question or Prompt": "Value(dtype='string', id=None)", "feat_Setting": "Value(dtype='string', id=None)", "feat_External Challenges or Obstacles": "Value(dtype='string', id=None)", "feat_Obligations and Responsibilities": "Value(dtype='string', id=None)", "feat_Attributes": "Value(dtype='string', id=None)", "feat_Decision-Making Process": "Value(dtype='string', id=None)", "feat_Thoughts and Prayers": "Value(dtype='string', id=None)", "feat_Feelings of the Speaker": "Value(dtype='string', id=None)", "feat_familial_status": "Value(dtype='string', id=None)", "feat_voice": "Value(dtype='string', id=None)", "feat_Events": "Value(dtype='string', id=None)", "feat_Familial Duties and Conflicts": "Value(dtype='string', id=None)", "feat_Origin": "Value(dtype='string', id=None)", "feat_Sexuality": "Value(dtype='string', id=None)", "feat_Fears & Phobias": "Value(dtype='string', id=None)", "feat_past_experience": "Value(dtype='string', id=None)", "feat_Transportation": "Value(dtype='string', id=None)", "feat_Job Titles": "Value(dtype='string', id=None)", "feat_Personal Philosophies": "Value(dtype='string', id=None)", "feat_Contexts and Backgrounds": "Value(dtype='string', id=None)", "feat_Senses": "Value(dtype='string', id=None)", "feat_Sentiment Towards Technology": "Value(dtype='string', id=None)", "feat_Content Preferences": "Value(dtype='string', id=None)", "feat_Mannerisms": "Value(dtype='string', id=None)", "feat_Gratitude Journals": "Value(dtype='string', id=None)", "feat_Psychological Traits": "Value(dtype='string', id=None)", "feat_Advice Taken": "Value(dtype='string', id=None)", "feat_Answer": "Value(dtype='string', id=None)", "feat_Sentiment": "Value(dtype='string', id=None)", "feat_Personal Names": "Value(dtype='string', id=None)", "feat_Time Phrases": "Value(dtype='string', id=None)", "feat_Nickname": "Value(dtype='string', id=None)", "feat_Role": "Value(dtype='string', id=None)", "feat_Request": "Value(dtype='string', id=None)", "feat_Response": "Value(dtype='string', id=None)", "feat_Physical_Pain_or_Illness": "Value(dtype='string', id=None)", "feat_Body_Parts": "Value(dtype='string', id=None)", "feat_Skills_&_Abilities": "Value(dtype='string', id=None)", "feat_Major Life Events": "Value(dtype='string', id=None)", "feat_Opinions About Controversial Topics": "Value(dtype='string', id=None)", "feat_home_town": "Value(dtype='string', id=None)", "feat_Afraid of": "Value(dtype='string', id=None)", "feat_Food Preferences": "Value(dtype='string', id=None)", "feat_Relationships and Family": "Value(dtype='string', id=None)", "feat_Body Language": "Value(dtype='string', id=None)", "feat_No\", \"Mental or Physical Disabilities": "Value(dtype='string', id=None)", "feat_Other Feelings": "Value(dtype='string', id=None)", "feat_Question or Hypothesis": "Value(dtype='string', id=None)", "feat_PastTenseVerbs": "Value(dtype='string', id=None)", "feat_Dreams": "Value(dtype='string', id=None)", "feat_Dietary Restrictions or Choices": "Value(dtype='string', id=None)", "feat_Plans and Intentions": "Value(dtype='string', id=None)", "feat_Nicknames or Aliases": "Value(dtype='string', id=None)", "feat_Symbols or Motifs": "Value(dtype='string', id=None)", "feat_Passions or Interests": "Value(dtype='string', id=None)", "feat_Description": "Value(dtype='string', id=None)", "feat_Moral Values": "Value(dtype='string', id=None)", "feat_Question or query": "Value(dtype='string', id=None)", "feat_external_appearances": "Value(dtype='string', id=None)", "feat_Themes": "Value(dtype='string', id=None)", "feat_Chapter": "Value(dtype='string', id=None)", "feat_General Sentiment": "Value(dtype='string', id=None)", "feat_Traits": "Value(dtype='string', id=None)", "feat_Tone&Style": "Value(dtype='string', id=None)", "feat_Revenge or Retribution": "Value(dtype='string', id=None)", "feat_Strengths": "Value(dtype='string', id=None)", "feat_What they want": "Value(dtype='string', id=None)", "feat_Priorities": "Value(dtype='string', id=None)", "feat_Compliments Received": "Value(dtype='string', id=None)", "feat_Daily Routine": "Value(dtype='string', id=None)", "feat_Timeframe": "Value(dtype='string', id=None)", "feat_tactics_or_strategies": "Value(dtype='string', id=None)", "feat_Societal Norms or Expectations": "Value(dtype='string', id=None)", "feat_Outfit": "Value(dtype='string', id=None)", "feat_sentiment_score": "Value(dtype='float64', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 3958 | | valid | 990 |

Sambosis/autotrain-data-big

[ "task_categories:summarization", "language:en", "region:us" ]

2023-09-14T02:43:39+00:00

{"language": ["en"], "task_categories": ["summarization"]}

2023-09-14T02:48:01+00:00

[]

[ "en" ]

TAGS #task_categories-summarization #language-English #region-us

AutoTrain Dataset for project: big ================================== Dataset Description ------------------- This dataset has been automatically processed by AutoTrain for project big. ### Languages The BCP-47 code for the dataset's language is en. Dataset Structure ----------------- ### Data Instances A sample from this dataset looks as follows: ### Dataset Fields The dataset has the following fields (also called "features"): ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow:

[ "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ "TAGS\n#task_categories-summarization #language-English #region-us \n", "### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nA sample from this dataset looks as follows:", "### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):", "### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

[ 20, 26, 17, 23, 27 ]

[ "passage: TAGS\n#task_categories-summarization #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:" ]

0dc8394d1b32b9f3864aa5642a12fcc3c171795a

# Dataset Card for "dataset-multiple-myeloma-study-dictionary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nzindoc/dataset-multiple-myeloma-study-dictionary

[ "region:us" ]

2023-09-14T02:58:55+00:00

{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1285724160, "num_examples": 1975680}], "download_size": 30419669, "dataset_size": 1285724160}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T03:00:10+00:00

[]

TAGS #region-us

# Dataset Card for "dataset-multiple-myeloma-study-dictionary" More Information needed

[ "# Dataset Card for \"dataset-multiple-myeloma-study-dictionary\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"dataset-multiple-myeloma-study-dictionary\"\n\nMore Information needed" ]

[ 6, 26 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"dataset-multiple-myeloma-study-dictionary\"\n\nMore Information needed" ]

9fe6e146a49427b1e035f89643065fc358a273c0

# Dataset of tada_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls) This is the dataset of tada_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `short_hair, brown_hair, green_eyes, headphones, 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 | 419.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 298.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1009 | 552.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 387.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1009 | 697.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tada_riina_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/tada_riina_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 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, serafuku, solo, blush, looking_at_viewer, skirt, sitting, smile | | 1 | 8 | ![](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, necklace, plaid_skirt, solo, blush, clothes_writing, looking_at_viewer, smile, belt, open_mouth, black_thighhighs | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, belt, clothes_writing, necklace, plaid_skirt, solo, tank_top, open_mouth | | 3 | 5 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, open_mouth, solo, looking_at_viewer, :d, dress, hair_ornament, fingerless_gloves | | 4 | 12 | ![](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, smile, solo, fingerless_gloves, skirt, microphone, open_mouth, belt, detached_sleeves, thighhighs, choker, looking_at_viewer, chain, garter_straps, cuffs | | 5 | 11 | ![](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) | cat_ear_headphones, cat_ears, midriff, navel, looking_at_viewer, open_mouth, smile, 2girls, necklace, blush, 1girl, belt, black_skirt, boots, miniskirt, solo_focus, thighhighs | | 6 | 6 | ![](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, bangs, hair_between_eyes, looking_at_viewer, shirt, solo, white_background, blush, shiny_hair, simple_background, :d, miniskirt, open_mouth, pleated_skirt, sleeveless, black_skirt, collarbone, jacket, long_sleeves, open_clothes | | 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, cleavage, medium_breasts, microphone_stand, smile, solo, thighhighs, choker, looking_at_viewer, skirt, belt, jewelry, open_mouth, boots, gloves, midriff, navel | | 8 | 13 | ![](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, cleavage, looking_at_viewer, medium_breasts, smile, solo, navel, open_mouth, blue_bikini, blush, jewelry | | 9 | 5 | ![](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, pleated_skirt, serafuku, short_sleeves, solo, white_shirt, bangs, blue_sailor_collar, blue_skirt, hair_between_eyes, kneehighs, looking_at_viewer, yellow_neckerchief, black_socks, brown_footwear, full_body, miniskirt, grin, headphones_around_neck, loafers, navel, one_eye_closed, open_mouth, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | serafuku | solo | blush | looking_at_viewer | skirt | sitting | smile | necklace | plaid_skirt | clothes_writing | belt | open_mouth | black_thighhighs | tank_top | :d | dress | hair_ornament | fingerless_gloves | microphone | detached_sleeves | thighhighs | choker | chain | garter_straps | cuffs | cat_ear_headphones | cat_ears | midriff | navel | 2girls | black_skirt | boots | miniskirt | solo_focus | bangs | hair_between_eyes | shirt | white_background | shiny_hair | simple_background | pleated_skirt | sleeveless | collarbone | jacket | long_sleeves | open_clothes | cleavage | medium_breasts | microphone_stand | jewelry | gloves | blue_bikini | short_sleeves | white_shirt | blue_sailor_collar | blue_skirt | kneehighs | yellow_neckerchief | black_socks | brown_footwear | full_body | grin | headphones_around_neck | loafers | one_eye_closed | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-------|:--------|:--------------------|:--------|:----------|:--------|:-----------|:--------------|:------------------|:-------|:-------------|:-------------------|:-----------|:-----|:--------|:----------------|:--------------------|:-------------|:-------------------|:-------------|:---------|:--------|:----------------|:--------|:---------------------|:-----------|:----------|:--------|:---------|:--------------|:--------|:------------|:-------------|:--------|:--------------------|:--------|:-------------------|:-------------|:--------------------|:----------------|:-------------|:-------------|:---------|:---------------|:---------------|:-----------|:-----------------|:-------------------|:----------|:---------|:--------------|:----------------|:--------------|:---------------------|:-------------|:------------|:---------------------|:--------------|:-----------------|:------------|:-------|:-------------------------|:----------|:-----------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 | ![](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 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | | | | | X | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | X | X | X | | | | | | | | X | | | X | | | | | | | | | | | | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 7 | 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 | | | | | | | | | | | | | | | | 8 | 13 | ![](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 | | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | X | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | X | | X | X | | | | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/tada_riina_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T03:03:05+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T15:18:08+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of tada\_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls) =============================================================== This is the dataset of tada\_riina/多田李衣菜 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'short\_hair, brown\_hair, green\_eyes, headphones, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

c802b66008e6cb0495afe70202b662c1db7128da

# Dataset of hisakawa_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls) This is the dataset of hisakawa_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `long_hair, bangs, grey_hair, braid, blue_eyes, breasts, braided_bangs, earrings, very_long_hair, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 860.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 453.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1294 | 995.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 743.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1294 | 1.47 GiB | [Download](https://huggingface.co/datasets/CyberHarem/hisakawa_hayate_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/hisakawa_hayate_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) | blush, collared_shirt, fingerless_gloves, looking_at_viewer, open_jacket, plaid_necktie, white_gloves, white_jacket, white_shirt, 1girl, jewelry, open_mouth, pleated_skirt, solo, white_skirt, :d, white_background, bare_shoulders, off_shoulder, short_sleeves, sleeveless_shirt, hands_up, star_(symbol), starry_background | | 1 | 23 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | looking_at_viewer, white_shirt, blush, jewelry, collared_shirt, white_background, 1girl, dress_shirt, juliet_sleeves, black_jacket, simple_background, solo, black_skirt, open_mouth, collarbone, :d | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | collarbone, jacket, looking_at_viewer, necklace, off_shoulder, open_clothes, :d, bare_shoulders, blush, green_skirt, open_mouth, 1girl, aqua_skirt, heart_hands, long_sleeves, solo, white_shirt, simple_background, white_background | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, collarbone, jewelry, simple_background, solo, white_background, cleavage, looking_at_viewer, navel, large_breasts, micro_bikini, sweat, gold_bikini, skindentation, upper_body, white_bikini, yellow_bikini | | 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, cleavage, collarbone, looking_at_viewer, solo, open_mouth, outdoors, :d, day, frilled_bikini, navel, ocean, water, white_bikini, bikini_skirt, blue_sky, blush, bare_shoulders, cloud, bracelet, wet | | 5 | 6 | ![](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) | blue_sky, collarbone, day, looking_at_viewer, navel, ocean, outdoors, smile, 1girl, solo, string_bikini, beach, blush, cleavage, cloud, cowboy_shot, halterneck, open_mouth, armpits, horizon, side-tie_bikini_bottom, standing | | 6 | 17 | ![](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, looking_at_viewer, hair_flower, hair_ribbon, open_mouth, solo, bare_shoulders, blush, jewelry, choker, collarbone, white_dress, simple_background, white_background, :d, off-shoulder_dress, short_sleeves, green_ribbon, hand_up, upper_body, white_flower | | 7 | 17 | ![](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, looking_at_viewer, solo, necktie, wrist_cuffs, white_sailor_collar, blush, blue_dress, puffy_short_sleeves, open_mouth, hair_ornament, sailor_dress, white_background, :d, grey_eyes, pleated_dress, pleated_skirt, shirt | | 8 | 9 | ![](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) | 1boy, blush, hetero, open_mouth, solo_focus, completely_nude, jewelry, nipples, penis, 1girl, sweat, collarbone, large_breasts, looking_at_viewer, mosaic_censoring, cum, navel, pov, sex | | 9 | 10 | ![](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) | black_dress, blush, looking_at_viewer, enmaided, maid_apron, white_apron, 1girl, maid_headdress, solo, frilled_apron, open_mouth, simple_background, puffy_short_sleeves, white_background, :d, jewelry, long_sleeves, neck_ribbon | | 10 | 7 | ![](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) | 1girl, blue_headwear, bow, detached_sleeves, long_sleeves, :d, bare_shoulders, beret, blush, jewelry, looking_at_viewer, open_mouth, solo, white_background, belt_buckle, blue_shirt, collared_shirt, pink_flower, simple_background, sweater, blue_sleeves, pleated_skirt, sleeveless_shirt, sleeves_past_wrists, hand_up, red_flower, tilted_headwear, yellow_skirt | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, blush, collarbone, cow_ears, cow_horns, cow_print, fake_animal_ears, fake_horns, looking_at_viewer, navel, neck_bell, solo, cleavage, cowbell, hairband, heart, jewelry, large_breasts, open_mouth, print_bikini, smile, white_background, 2021, ;d, holding, milk_bottle, one_eye_closed, print_legwear, side-tie_bikini_bottom, simple_background, two-tone_background, white_bikini, white_thighhighs, year_of_the_ox | | 12 | 11 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | obi, blush, floral_print, long_sleeves, print_kimono, wide_sleeves, 1girl, blue_kimono, hair_flower, holding, jewelry, solo, looking_at_viewer, :d, open_mouth, blue_flower, upper_body, white_background | | 13 | 11 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | looking_at_viewer, playboy_bunny, 1girl, bowtie, cleavage, detached_collar, fake_animal_ears, rabbit_ears, solo, strapless_leotard, blush, wrist_cuffs, bare_shoulders, collarbone, black_leotard, black_pantyhose, simple_background, smile, white_background, hairband, open_mouth, covered_navel, high_heels, large_breasts, wing_collar | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | collared_shirt | fingerless_gloves | looking_at_viewer | open_jacket | plaid_necktie | white_gloves | white_jacket | white_shirt | 1girl | jewelry | open_mouth | pleated_skirt | solo | white_skirt | :d | white_background | bare_shoulders | off_shoulder | short_sleeves | sleeveless_shirt | hands_up | star_(symbol) | starry_background | dress_shirt | juliet_sleeves | black_jacket | simple_background | black_skirt | collarbone | jacket | necklace | open_clothes | green_skirt | aqua_skirt | heart_hands | long_sleeves | cleavage | navel | large_breasts | micro_bikini | sweat | gold_bikini | skindentation | upper_body | white_bikini | yellow_bikini | outdoors | day | frilled_bikini | ocean | water | bikini_skirt | blue_sky | cloud | bracelet | wet | smile | string_bikini | beach | cowboy_shot | halterneck | armpits | horizon | side-tie_bikini_bottom | standing | hair_flower | hair_ribbon | choker | white_dress | off-shoulder_dress | green_ribbon | hand_up | white_flower | necktie | wrist_cuffs | white_sailor_collar | blue_dress | puffy_short_sleeves | hair_ornament | sailor_dress | grey_eyes | pleated_dress | shirt | 1boy | hetero | solo_focus | completely_nude | nipples | penis | mosaic_censoring | cum | pov | sex | black_dress | enmaided | maid_apron | white_apron | maid_headdress | frilled_apron | neck_ribbon | blue_headwear | bow | detached_sleeves | beret | belt_buckle | blue_shirt | pink_flower | sweater | blue_sleeves | sleeves_past_wrists | red_flower | tilted_headwear | yellow_skirt | cow_ears | cow_horns | cow_print | fake_animal_ears | fake_horns | neck_bell | cowbell | hairband | heart | print_bikini | 2021 | ;d | holding | milk_bottle | one_eye_closed | print_legwear | two-tone_background | white_thighhighs | year_of_the_ox | obi | floral_print | print_kimono | wide_sleeves | blue_kimono | blue_flower | playboy_bunny | bowtie | detached_collar | rabbit_ears | strapless_leotard | black_leotard | black_pantyhose | covered_navel | high_heels | wing_collar | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------|:--------------------|:--------------------|:--------------|:----------------|:---------------|:---------------|:--------------|:--------|:----------|:-------------|:----------------|:-------|:--------------|:-----|:-------------------|:-----------------|:---------------|:----------------|:-------------------|:-----------|:----------------|:--------------------|:--------------|:-----------------|:---------------|:--------------------|:--------------|:-------------|:---------|:-----------|:---------------|:--------------|:-------------|:--------------|:---------------|:-----------|:--------|:----------------|:---------------|:--------|:--------------|:----------------|:-------------|:---------------|:----------------|:-----------|:------|:-----------------|:--------|:--------|:---------------|:-----------|:--------|:-----------|:------|:--------|:----------------|:--------|:--------------|:-------------|:----------|:----------|:-------------------------|:-----------|:--------------|:--------------|:---------|:--------------|:---------------------|:---------------|:----------|:---------------|:----------|:--------------|:----------------------|:-------------|:----------------------|:----------------|:---------------|:------------|:----------------|:--------|:-------|:---------|:-------------|:------------------|:----------|:--------|:-------------------|:------|:------|:------|:--------------|:-----------|:-------------|:--------------|:-----------------|:----------------|:--------------|:----------------|:------|:-------------------|:--------|:--------------|:-------------|:--------------|:----------|:---------------|:----------------------|:-------------|:------------------|:---------------|:-----------|:------------|:------------|:-------------------|:-------------|:------------|:----------|:-----------|:--------|:---------------|:-------|:-----|:----------|:--------------|:-----------------|:----------------|:----------------------|:-------------------|:-----------------|:------|:---------------|:---------------|:---------------|:--------------|:--------------|:----------------|:---------|:------------------|:--------------|:--------------------|:----------------|:------------------|:----------------|:-------------|:--------------| | 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 23 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | X | | | | | X | X | | X | | X | | X | X | X | X | | | | | | | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | X | | | | | | X | X | | | X | | | X | | | | | | | | | | | X | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | | | | | | X | | X | | X | | | | | | | | | | | | | | | | X | | | | | | | | X | X | | | | | | | | | X | X | | X | | | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 17 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 17 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 10 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | | | | | | X | X | X | | X | | X | X | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 7 | ![](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 | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 5 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-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 | | | | | | | | | | | | | | | | | | 12 | 11 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | | | X | | | | | | X | X | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | 13 | 11 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | X | | 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CyberHarem/hisakawa_hayate_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T03:34:11+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T15:22:45+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of hisakawa\_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of hisakawa\_hayate/久川颯 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'long\_hair, bangs, grey\_hair, braid, blue\_eyes, breasts, braided\_bangs, earrings, very\_long\_hair, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

81df905a36243c3bd2e24bde182bad0454905dcb

# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/NoIdeaLand/test-2048-1500ck - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [NoIdeaLand/test-2048-1500ck](https://huggingface.co/NoIdeaLand/test-2048-1500ck) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_NoIdeaLand__test-2048-1500ck", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-14T04:39:40.489809](https://huggingface.co/datasets/open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck/blob/main/results_2023-09-14T04-39-40.489809.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": 0.26196111221791213, "acc_stderr": 0.03173586961427775, "acc_norm": 0.2653334325357461, "acc_norm_stderr": 0.03173833592722594, "mc1": 0.23990208078335373, "mc1_stderr": 0.014948812679062137, "mc2": 0.4095943166947606, "mc2_stderr": 0.014642509125225842 }, "harness|arc:challenge|25": { "acc": 0.33532423208191126, "acc_stderr": 0.013796182947785564, "acc_norm": 0.36689419795221845, "acc_norm_stderr": 0.014084133118104294 }, "harness|hellaswag|10": { "acc": 0.45817566221868156, "acc_stderr": 0.004972293764978723, "acc_norm": 0.6255725951005776, "acc_norm_stderr": 0.004829856058603573 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.24444444444444444, "acc_stderr": 0.037125378336148665, "acc_norm": 0.24444444444444444, "acc_norm_stderr": 0.037125378336148665 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.19078947368421054, "acc_stderr": 0.031975658210325, "acc_norm": 0.19078947368421054, "acc_norm_stderr": 0.031975658210325 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.24150943396226415, "acc_stderr": 0.026341480371118366, "acc_norm": 0.24150943396226415, "acc_norm_stderr": 0.026341480371118366 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2986111111111111, "acc_stderr": 0.03827052357950756, "acc_norm": 0.2986111111111111, "acc_norm_stderr": 0.03827052357950756 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.24, "acc_stderr": 0.04292346959909283, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909283 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.18, "acc_stderr": 0.038612291966536955, "acc_norm": 0.18, "acc_norm_stderr": 0.038612291966536955 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.24, "acc_stderr": 0.04292346959909281, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909281 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.2023121387283237, "acc_stderr": 0.03063114553919882, "acc_norm": 0.2023121387283237, "acc_norm_stderr": 0.03063114553919882 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.28, "acc_stderr": 0.045126085985421276, "acc_norm": 0.28, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3191489361702128, "acc_stderr": 0.030472973363380042, "acc_norm": 0.3191489361702128, "acc_norm_stderr": 0.030472973363380042 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2894736842105263, "acc_stderr": 0.04266339443159394, "acc_norm": 0.2894736842105263, "acc_norm_stderr": 0.04266339443159394 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.2896551724137931, "acc_stderr": 0.03780019230438015, "acc_norm": 0.2896551724137931, "acc_norm_stderr": 0.03780019230438015 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2328042328042328, "acc_stderr": 0.021765961672154523, "acc_norm": 0.2328042328042328, "acc_norm_stderr": 0.021765961672154523 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.23015873015873015, "acc_stderr": 0.03764950879790606, "acc_norm": 0.23015873015873015, "acc_norm_stderr": 0.03764950879790606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.19, "acc_stderr": 0.03942772444036625, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036625 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.22258064516129034, "acc_stderr": 0.023664216671642518, "acc_norm": 0.22258064516129034, "acc_norm_stderr": 0.023664216671642518 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.23645320197044334, "acc_stderr": 0.029896114291733552, "acc_norm": 0.23645320197044334, "acc_norm_stderr": 0.029896114291733552 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.19, "acc_stderr": 0.03942772444036623, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036623 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.3151515151515151, "acc_stderr": 0.0362773057502241, "acc_norm": 0.3151515151515151, "acc_norm_stderr": 0.0362773057502241 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.19696969696969696, "acc_stderr": 0.02833560973246335, "acc_norm": 0.19696969696969696, "acc_norm_stderr": 0.02833560973246335 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.30569948186528495, "acc_stderr": 0.03324837939758159, "acc_norm": 0.30569948186528495, "acc_norm_stderr": 0.03324837939758159 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.2692307692307692, "acc_stderr": 0.022489389793654824, "acc_norm": 0.2692307692307692, "acc_norm_stderr": 0.022489389793654824 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25555555555555554, "acc_stderr": 0.026593939101844086, "acc_norm": 0.25555555555555554, "acc_norm_stderr": 0.026593939101844086 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.22268907563025211, "acc_stderr": 0.02702543349888239, "acc_norm": 0.22268907563025211, "acc_norm_stderr": 0.02702543349888239 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2119205298013245, "acc_stderr": 0.03336767086567977, "acc_norm": 0.2119205298013245, "acc_norm_stderr": 0.03336767086567977 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.22018348623853212, "acc_stderr": 0.01776597865232757, "acc_norm": 0.22018348623853212, "acc_norm_stderr": 0.01776597865232757 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2361111111111111, "acc_stderr": 0.028963702570791033, "acc_norm": 0.2361111111111111, "acc_norm_stderr": 0.028963702570791033 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.28431372549019607, "acc_stderr": 0.03166009679399812, "acc_norm": 0.28431372549019607, "acc_norm_stderr": 0.03166009679399812 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.2869198312236287, "acc_stderr": 0.02944377302259469, "acc_norm": 0.2869198312236287, "acc_norm_stderr": 0.02944377302259469 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.39461883408071746, "acc_stderr": 0.03280400504755291, "acc_norm": 0.39461883408071746, "acc_norm_stderr": 0.03280400504755291 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.2595419847328244, "acc_stderr": 0.03844876139785271, "acc_norm": 0.2595419847328244, "acc_norm_stderr": 0.03844876139785271 }, "harness|hendrycksTest-international_law|5": { "acc": 0.24793388429752067, "acc_stderr": 0.03941897526516302, "acc_norm": 0.24793388429752067, "acc_norm_stderr": 0.03941897526516302 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.2777777777777778, "acc_stderr": 0.043300437496507437, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.043300437496507437 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.25153374233128833, "acc_stderr": 0.03408997886857529, "acc_norm": 0.25153374233128833, "acc_norm_stderr": 0.03408997886857529 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.29464285714285715, "acc_stderr": 0.04327040932578728, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.04327040932578728 }, "harness|hendrycksTest-management|5": { "acc": 0.17475728155339806, "acc_stderr": 0.037601780060266224, "acc_norm": 0.17475728155339806, "acc_norm_stderr": 0.037601780060266224 }, "harness|hendrycksTest-marketing|5": { "acc": 0.3076923076923077, "acc_stderr": 0.030236389942173095, "acc_norm": 0.3076923076923077, "acc_norm_stderr": 0.030236389942173095 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.2835249042145594, "acc_stderr": 0.016117318166832265, "acc_norm": 0.2835249042145594, "acc_norm_stderr": 0.016117318166832265 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.2745664739884393, "acc_stderr": 0.02402774515526501, "acc_norm": 0.2745664739884393, "acc_norm_stderr": 0.02402774515526501 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.2424581005586592, "acc_stderr": 0.014333522059217889, "acc_norm": 0.2424581005586592, "acc_norm_stderr": 0.014333522059217889 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.27450980392156865, "acc_stderr": 0.025553169991826507, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.025553169991826507 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2347266881028939, "acc_stderr": 0.024071805887677045, "acc_norm": 0.2347266881028939, "acc_norm_stderr": 0.024071805887677045 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.21604938271604937, "acc_stderr": 0.022899162918445806, "acc_norm": 0.21604938271604937, "acc_norm_stderr": 0.022899162918445806 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2553191489361702, "acc_stderr": 0.02601199293090202, "acc_norm": 0.2553191489361702, "acc_norm_stderr": 0.02601199293090202 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.2627118644067797, "acc_stderr": 0.01124054551499566, "acc_norm": 0.2627118644067797, "acc_norm_stderr": 0.01124054551499566 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.21691176470588236, "acc_stderr": 0.025035845227711254, "acc_norm": 0.21691176470588236, "acc_norm_stderr": 0.025035845227711254 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.27450980392156865, "acc_stderr": 0.0180540274588152, "acc_norm": 0.27450980392156865, "acc_norm_stderr": 0.0180540274588152 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.2818181818181818, "acc_stderr": 0.04309118709946458, "acc_norm": 0.2818181818181818, "acc_norm_stderr": 0.04309118709946458 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.1836734693877551, "acc_stderr": 0.024789071332007653, "acc_norm": 0.1836734693877551, "acc_norm_stderr": 0.024789071332007653 }, "harness|hendrycksTest-sociology|5": { "acc": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-virology|5": { "acc": 0.29518072289156627, "acc_stderr": 0.035509201856896294, "acc_norm": 0.29518072289156627, "acc_norm_stderr": 0.035509201856896294 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.3157894736842105, "acc_stderr": 0.03565079670708311, "acc_norm": 0.3157894736842105, "acc_norm_stderr": 0.03565079670708311 }, "harness|truthfulqa:mc|0": { "mc1": 0.23990208078335373, "mc1_stderr": 0.014948812679062137, "mc2": 0.4095943166947606, "mc2_stderr": 0.014642509125225842 } } ``` ### 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]

open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck

[ "region:us" ]

2023-09-14T03:39:54+00:00

{"pretty_name": "Evaluation run of NoIdeaLand/test-2048-1500ck", "dataset_summary": "Dataset automatically created during the evaluation run of model [NoIdeaLand/test-2048-1500ck](https://huggingface.co/NoIdeaLand/test-2048-1500ck) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-14T04:39:40.489809](https://huggingface.co/datasets/open-llm-leaderboard/details_NoIdeaLand__test-2048-1500ck/blob/main/results_2023-09-14T04-39-40.489809.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\": 0.26196111221791213,\n \"acc_stderr\": 0.03173586961427775,\n \"acc_norm\": 0.2653334325357461,\n \"acc_norm_stderr\": 0.03173833592722594,\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062137,\n \"mc2\": 0.4095943166947606,\n \"mc2_stderr\": 0.014642509125225842\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.33532423208191126,\n \"acc_stderr\": 0.013796182947785564,\n \"acc_norm\": 0.36689419795221845,\n \"acc_norm_stderr\": 0.014084133118104294\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.45817566221868156,\n \"acc_stderr\": 0.004972293764978723,\n \"acc_norm\": 0.6255725951005776,\n \"acc_norm_stderr\": 0.004829856058603573\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.24444444444444444,\n \"acc_stderr\": 0.037125378336148665,\n \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.037125378336148665\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.19078947368421054,\n \"acc_stderr\": 0.031975658210325,\n \"acc_norm\": 0.19078947368421054,\n \"acc_norm_stderr\": 0.031975658210325\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.24150943396226415,\n \"acc_stderr\": 0.026341480371118366,\n \"acc_norm\": 0.24150943396226415,\n \"acc_norm_stderr\": 0.026341480371118366\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2986111111111111,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.2986111111111111,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2023121387283237,\n \"acc_stderr\": 0.03063114553919882,\n \"acc_norm\": 0.2023121387283237,\n \"acc_norm_stderr\": 0.03063114553919882\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.3191489361702128,\n \"acc_stderr\": 0.030472973363380042,\n \"acc_norm\": 0.3191489361702128,\n \"acc_norm_stderr\": 0.030472973363380042\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438015,\n \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438015\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2328042328042328,\n \"acc_stderr\": 0.021765961672154523,\n \"acc_norm\": 0.2328042328042328,\n \"acc_norm_stderr\": 0.021765961672154523\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23015873015873015,\n \"acc_stderr\": 0.03764950879790606,\n \"acc_norm\": 0.23015873015873015,\n \"acc_norm_stderr\": 0.03764950879790606\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036625,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036625\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.22258064516129034,\n \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.22258064516129034,\n \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.23645320197044334,\n \"acc_stderr\": 0.029896114291733552,\n \"acc_norm\": 0.23645320197044334,\n \"acc_norm_stderr\": 0.029896114291733552\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.3151515151515151,\n \"acc_stderr\": 0.0362773057502241,\n \"acc_norm\": 0.3151515151515151,\n \"acc_norm_stderr\": 0.0362773057502241\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.19696969696969696,\n \"acc_stderr\": 0.02833560973246335,\n \"acc_norm\": 0.19696969696969696,\n \"acc_norm_stderr\": 0.02833560973246335\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.30569948186528495,\n \"acc_stderr\": 0.03324837939758159,\n \"acc_norm\": 0.30569948186528495,\n \"acc_norm_stderr\": 0.03324837939758159\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2692307692307692,\n \"acc_stderr\": 0.022489389793654824,\n \"acc_norm\": 0.2692307692307692,\n \"acc_norm_stderr\": 0.022489389793654824\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844086,\n \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844086\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.22268907563025211,\n \"acc_stderr\": 0.02702543349888239,\n \"acc_norm\": 0.22268907563025211,\n \"acc_norm_stderr\": 0.02702543349888239\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2119205298013245,\n \"acc_stderr\": 0.03336767086567977,\n \"acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.03336767086567977\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22018348623853212,\n \"acc_stderr\": 0.01776597865232757,\n \"acc_norm\": 0.22018348623853212,\n \"acc_norm_stderr\": 0.01776597865232757\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.2361111111111111,\n \"acc_stderr\": 0.028963702570791033,\n \"acc_norm\": 0.2361111111111111,\n \"acc_norm_stderr\": 0.028963702570791033\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.03166009679399812,\n \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.03166009679399812\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2869198312236287,\n \"acc_stderr\": 0.02944377302259469,\n \"acc_norm\": 0.2869198312236287,\n \"acc_norm_stderr\": 0.02944377302259469\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.39461883408071746,\n \"acc_stderr\": 0.03280400504755291,\n \"acc_norm\": 0.39461883408071746,\n \"acc_norm_stderr\": 0.03280400504755291\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.24793388429752067,\n \"acc_stderr\": 0.03941897526516302,\n \"acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.03941897526516302\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.25153374233128833,\n \"acc_stderr\": 0.03408997886857529,\n \"acc_norm\": 0.25153374233128833,\n \"acc_norm_stderr\": 0.03408997886857529\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.04327040932578728,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.04327040932578728\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3076923076923077,\n \"acc_stderr\": 0.030236389942173095,\n \"acc_norm\": 0.3076923076923077,\n \"acc_norm_stderr\": 0.030236389942173095\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.2835249042145594,\n \"acc_stderr\": 0.016117318166832265,\n \"acc_norm\": 0.2835249042145594,\n \"acc_norm_stderr\": 0.016117318166832265\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2745664739884393,\n \"acc_stderr\": 0.02402774515526501,\n \"acc_norm\": 0.2745664739884393,\n \"acc_norm_stderr\": 0.02402774515526501\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.025553169991826507,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.025553169991826507\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2347266881028939,\n \"acc_stderr\": 0.024071805887677045,\n \"acc_norm\": 0.2347266881028939,\n \"acc_norm_stderr\": 0.024071805887677045\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2553191489361702,\n \"acc_stderr\": 0.02601199293090202,\n \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.02601199293090202\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2627118644067797,\n \"acc_stderr\": 0.01124054551499566,\n \"acc_norm\": 0.2627118644067797,\n \"acc_norm_stderr\": 0.01124054551499566\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.21691176470588236,\n \"acc_stderr\": 0.025035845227711254,\n \"acc_norm\": 0.21691176470588236,\n \"acc_norm_stderr\": 0.025035845227711254\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.0180540274588152,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.0180540274588152\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2818181818181818,\n \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.2818181818181818,\n \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.1836734693877551,\n \"acc_stderr\": 0.024789071332007653,\n \"acc_norm\": 0.1836734693877551,\n \"acc_norm_stderr\": 0.024789071332007653\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.29518072289156627,\n \"acc_stderr\": 0.035509201856896294,\n \"acc_norm\": 0.29518072289156627,\n \"acc_norm_stderr\": 0.035509201856896294\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.03565079670708311,\n \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.03565079670708311\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062137,\n \"mc2\": 0.4095943166947606,\n \"mc2_stderr\": 0.014642509125225842\n }\n}\n```", "repo_url": "https://huggingface.co/NoIdeaLand/test-2048-1500ck", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T04_39_40.489809", "path": ["**/details_harness|arc:challenge|25_2023-09-14T04-39-40.489809.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-14T04-39-40.489809.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T04_39_40.489809", "path": ["**/details_harness|hellaswag|10_2023-09-14T04-39-40.489809.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-14T04-39-40.489809.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_14T04_39_40.489809", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-09-14T04-39-40.489809.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-14T04-39-40.489809.parquet", 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2023-09-14T03:40:54+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard. The dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-09-14T04:39:40.489809(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T04:39:40.489809(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T04:39:40.489809(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 21, 31, 169, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NoIdeaLand/test-2048-1500ck## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model NoIdeaLand/test-2048-1500ck on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-14T04:39:40.489809(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

48714734801b2ef26c7baa1293577f43c748d4bc

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

hanho/test2

[ "license:openrail", "region:us" ]

2023-09-14T03:44:12+00:00

{"license": "openrail", "dataset_info": {"features": [{"name": "pokemon", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43, "num_examples": 2}], "download_size": 1215, "dataset_size": 43}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T03:51:35+00:00

[]

TAGS #license-openrail #region-us

# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#license-openrail #region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 12, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#license-openrail #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

7b1544cda95be6c5da6d97c41f4f76b09e4b3930

# Dataset Card for "mmlu_binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

atmallen/mmlu_binary

[ "region:us" ]

2023-09-14T03:47:27+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int32"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "validation", "num_bytes": 653717, "num_examples": 1218}, {"name": "test", "num_bytes": 5979564, "num_examples": 11526}], "download_size": 3456524, "dataset_size": 6633281}}

2023-09-19T04:12:16+00:00

[]

TAGS #region-us

# Dataset Card for "mmlu_binary" More Information needed

[ "# Dataset Card for \"mmlu_binary\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu_binary\"\n\nMore Information needed" ]

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"mmlu_binary\"\n\nMore Information needed" ]

f4dd3d7cf529f409aaf1d1cc3b170e6ac59e6615

# Dataset Card for "mmlu_chat_binary" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

atmallen/mmlu_chat_binary

[ "region:us" ]

2023-09-14T03:55:59+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "answer", "dtype": "int32"}, {"name": "statement", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "false", "1": "true"}}}}], "splits": [{"name": "validation", "num_bytes": 877546, "num_examples": 1218}, {"name": "test", "num_bytes": 8026608, "num_examples": 11526}], "download_size": 3732071, "dataset_size": 8904154}}

2023-09-19T04:12:20+00:00

[]

TAGS #region-us

# Dataset Card for "mmlu_chat_binary" More Information needed

[ "# Dataset Card for \"mmlu_chat_binary\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu_chat_binary\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"mmlu_chat_binary\"\n\nMore Information needed" ]

5637eb112199d89afe3d4d088bc7537908e6c1d2

# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Faradaylab/ARIA-70B-V2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Faradaylab/ARIA-70B-V2](https://huggingface.co/Faradaylab/ARIA-70B-V2) 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_Faradaylab__ARIA-70B-V2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T19:48:19.078343](https://huggingface.co/datasets/open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2/blob/main/results_2023-10-25T19-48-19.078343.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": 0.12174916107382551, "em_stderr": 0.0033487438315364985, "f1": 0.18035549496644265, "f1_stderr": 0.0034191831504093964, "acc": 0.552493667621485, "acc_stderr": 0.011672124185183144 }, "harness|drop|3": { "em": 0.12174916107382551, "em_stderr": 0.0033487438315364985, "f1": 0.18035549496644265, "f1_stderr": 0.0034191831504093964 }, "harness|gsm8k|5": { "acc": 0.2880970432145565, "acc_stderr": 0.012474469737197917 }, "harness|winogrande|5": { "acc": 0.8168902920284136, "acc_stderr": 0.01086977863316837 } } ``` ### 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]

open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2

[ "region:us" ]

2023-09-14T04:14:20+00:00

{"pretty_name": "Evaluation run of Faradaylab/ARIA-70B-V2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Faradaylab/ARIA-70B-V2](https://huggingface.co/Faradaylab/ARIA-70B-V2) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T19:48:19.078343](https://huggingface.co/datasets/open-llm-leaderboard/details_Faradaylab__ARIA-70B-V2/blob/main/results_2023-10-25T19-48-19.078343.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\": 0.12174916107382551,\n \"em_stderr\": 0.0033487438315364985,\n \"f1\": 0.18035549496644265,\n \"f1_stderr\": 0.0034191831504093964,\n \"acc\": 0.552493667621485,\n \"acc_stderr\": 0.011672124185183144\n },\n \"harness|drop|3\": {\n \"em\": 0.12174916107382551,\n \"em_stderr\": 0.0033487438315364985,\n \"f1\": 0.18035549496644265,\n \"f1_stderr\": 0.0034191831504093964\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2880970432145565,\n \"acc_stderr\": 0.012474469737197917\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8168902920284136,\n \"acc_stderr\": 0.01086977863316837\n }\n}\n```", "repo_url": "https://huggingface.co/Faradaylab/ARIA-70B-V2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T05_14_04.383698", "path": ["**/details_harness|arc:challenge|25_2023-09-14T05-14-04.383698.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-14T05-14-04.383698.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T19_48_19.078343", "path": ["**/details_harness|drop|3_2023-10-25T19-48-19.078343.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T19-48-19.078343.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T19_48_19.078343", "path": ["**/details_harness|gsm8k|5_2023-10-25T19-48-19.078343.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T19-48-19.078343.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T05_14_04.383698", "path": ["**/details_harness|hellaswag|10_2023-09-14T05-14-04.383698.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-14T05-14-04.383698.parquet"]}]}, 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2023-10-25T18:48:32+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-25T19:48:19.078343(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T19:48:19.078343(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T19:48:19.078343(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 19, 31, 167, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Faradaylab/ARIA-70B-V2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Faradaylab/ARIA-70B-V2 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T19:48:19.078343(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

6d7ff385cc25fa66aa76870d3eec3ff23686b9a4

# Bangumi Image Base of Watashi No Yuri Wa Oshigoto Desu! This is the image base of bangumi Watashi no Yuri wa Oshigoto Desu!, we detected 31 characters, 3255 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 221 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 10 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 15 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 12 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 12 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 10 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 23 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 14 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 26 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 22 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 416 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 142 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 31 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 5 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | N/A | N/A | N/A | | 14 | 420 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 63 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 23 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 970 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 87 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 364 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 60 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 21 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 36 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | 23 | 11 | [Download](23/dataset.zip) | ![preview 1](23/preview_1.png) | ![preview 2](23/preview_2.png) | ![preview 3](23/preview_3.png) | ![preview 4](23/preview_4.png) | ![preview 5](23/preview_5.png) | ![preview 6](23/preview_6.png) | ![preview 7](23/preview_7.png) | ![preview 8](23/preview_8.png) | | 24 | 12 | [Download](24/dataset.zip) | ![preview 1](24/preview_1.png) | ![preview 2](24/preview_2.png) | ![preview 3](24/preview_3.png) | ![preview 4](24/preview_4.png) | ![preview 5](24/preview_5.png) | ![preview 6](24/preview_6.png) | ![preview 7](24/preview_7.png) | ![preview 8](24/preview_8.png) | | 25 | 13 | [Download](25/dataset.zip) | ![preview 1](25/preview_1.png) | ![preview 2](25/preview_2.png) | ![preview 3](25/preview_3.png) | ![preview 4](25/preview_4.png) | ![preview 5](25/preview_5.png) | ![preview 6](25/preview_6.png) | ![preview 7](25/preview_7.png) | ![preview 8](25/preview_8.png) | | 26 | 10 | [Download](26/dataset.zip) | ![preview 1](26/preview_1.png) | ![preview 2](26/preview_2.png) | ![preview 3](26/preview_3.png) | ![preview 4](26/preview_4.png) | ![preview 5](26/preview_5.png) | ![preview 6](26/preview_6.png) | ![preview 7](26/preview_7.png) | ![preview 8](26/preview_8.png) | | 27 | 24 | [Download](27/dataset.zip) | ![preview 1](27/preview_1.png) | ![preview 2](27/preview_2.png) | ![preview 3](27/preview_3.png) | ![preview 4](27/preview_4.png) | ![preview 5](27/preview_5.png) | ![preview 6](27/preview_6.png) | ![preview 7](27/preview_7.png) | ![preview 8](27/preview_8.png) | | 28 | 29 | [Download](28/dataset.zip) | ![preview 1](28/preview_1.png) | ![preview 2](28/preview_2.png) | ![preview 3](28/preview_3.png) | ![preview 4](28/preview_4.png) | ![preview 5](28/preview_5.png) | ![preview 6](28/preview_6.png) | ![preview 7](28/preview_7.png) | ![preview 8](28/preview_8.png) | | 29 | 13 | [Download](29/dataset.zip) | ![preview 1](29/preview_1.png) | ![preview 2](29/preview_2.png) | ![preview 3](29/preview_3.png) | ![preview 4](29/preview_4.png) | ![preview 5](29/preview_5.png) | ![preview 6](29/preview_6.png) | ![preview 7](29/preview_7.png) | ![preview 8](29/preview_8.png) | | noise | 140 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

BangumiBase/watashinoyuriwaoshigotodesu

[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]

2023-09-14T04:16:54+00:00

{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}

2023-09-29T06:26:06+00:00

[]

TAGS #size_categories-1K<n<10K #license-mit #art #region-us

Bangumi Image Base of Watashi No Yuri Wa Oshigoto Desu! ======================================================= This is the image base of bangumi Watashi no Yuri wa Oshigoto Desu!, we detected 31 characters, 3255 images in total. The full dataset is here. Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview:

[]

[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

[ 25 ]

[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

af4ab2bf94b96e527aefc82fcacfd4d2817878dd

# Dataset Card for "fonts_ds" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuanmei424/fonts_ds

[ "region:us" ]

2023-09-14T04:18:19+00:00

{"dataset_info": {"features": [{"name": "edit_prompt", "dtype": "string"}, {"name": "input_image", "dtype": "image"}, {"name": "edited_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 83621453418.25, "num_examples": 19837823}], "download_size": 0, "dataset_size": 83621453418.25}}

2023-09-27T21:50:32+00:00

[]

TAGS #region-us

# Dataset Card for "fonts_ds" More Information needed

[ "# Dataset Card for \"fonts_ds\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fonts_ds\"\n\nMore Information needed" ]

[ 6, 14 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"fonts_ds\"\n\nMore Information needed" ]

56ad86b8331b5b138948994eaa2d5c2001612af2

# Dataset of aiba_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls) This is the dataset of aiba_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls), containing 379 images and their tags. The core tags of this character are `short_hair, brown_eyes, breasts, blonde_hair, bangs, medium_breasts, brown_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 | 379 | 427.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 379 | 266.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 856 | 537.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 379 | 385.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 856 | 725.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aiba_yumi_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/aiba_yumi_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 | 23 | ![](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, blush, open_mouth, dress, looking_at_viewer, cleavage, necklace, :d, flower | | 1 | 7 | ![](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, collarbone, hair_flower, necklace, smile, solo, wrist_cuffs, bare_shoulders, blush, looking_at_viewer, red_flower, strapless_dress, earrings, petals, white_background, cleavage, standing, white_dress, bow, butterfly_hair_ornament, closed_mouth | | 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, bracelet, earrings, hair_flower, looking_at_viewer, open_mouth, smile, blush, collarbone, floral_print, navel, necklace, solo, belt, cleavage, one_eye_closed, water, bikini_skirt, day, frills, midriff, nail_polish, outdoors, petals, sandals, sky | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, midriff, navel, open_mouth, solo, hair_flower, rabbit_ears, rabbit_tail, :d, bare_shoulders, bow, earrings, microphone, skirt, star_(symbol), wrist_cuffs | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | blush, cleavage, collarbone, frilled_bikini, looking_at_viewer, 1girl, :d, navel, open_mouth, outdoors, blue_sky, day, floral_print, ocean, side-tie_bikini_bottom, solo, cloud, large_breasts, pink_bikini, water | | 5 | 11 | ![](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, playboy_bunny, rabbit_ears, solo, detached_collar, rabbit_tail, wrist_cuffs, black_leotard, looking_at_viewer, fake_animal_ears, strapless_leotard, black_pantyhose, cleavage, simple_background, ass, bare_shoulders, black_bowtie, blush, cowboy_shot, white_background | | 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, nurse_cap, short_sleeves, solo, :d, angel_wings, blush, feathered_wings, looking_at_viewer, open_mouth, white_wings, heart, white_dress, holding_syringe, star_(symbol), white_gloves, wrist_cuffs | | 7 | 12 | ![](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) | 1boy, 1girl, blush, hetero, penis, nipples, solo_focus, vaginal, open_mouth, bar_censor, spread_legs, sweat, girl_on_top, navel, sex_from_behind, smile, straddling, breast_grab, cum_in_pussy, grabbing_from_behind, large_breasts, nude, clothed_sex, collarbone, jewelry, open_shirt, skirt | | 8 | 5 | ![](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, blush, brown_skirt, collared_shirt, plaid_skirt, pleated_skirt, solo, white_shirt, long_sleeves, looking_at_viewer, school_uniform, black_bowtie, dress_shirt, open_mouth, smile, ;d, black_socks, blurry, bra_visible_through_clothes, closed_mouth, day, hose, indoors, kneehighs, one_eye_closed, outdoors, see-through, standing, water, wet_shirt | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | open_mouth | dress | looking_at_viewer | cleavage | necklace | :d | flower | collarbone | hair_flower | smile | wrist_cuffs | bare_shoulders | red_flower | strapless_dress | earrings | petals | white_background | standing | white_dress | bow | butterfly_hair_ornament | closed_mouth | bracelet | floral_print | navel | belt | one_eye_closed | water | bikini_skirt | day | frills | midriff | nail_polish | outdoors | sandals | sky | rabbit_ears | rabbit_tail | microphone | skirt | star_(symbol) | frilled_bikini | blue_sky | ocean | side-tie_bikini_bottom | cloud | large_breasts | pink_bikini | playboy_bunny | detached_collar | black_leotard | fake_animal_ears | strapless_leotard | black_pantyhose | simple_background | ass | black_bowtie | cowboy_shot | nurse_cap | short_sleeves | angel_wings | feathered_wings | white_wings | heart | holding_syringe | white_gloves | 1boy | hetero | penis | nipples | solo_focus | vaginal | bar_censor | spread_legs | sweat | girl_on_top | sex_from_behind | straddling | breast_grab | cum_in_pussy | grabbing_from_behind | nude | clothed_sex | jewelry | open_shirt | brown_skirt | collared_shirt | plaid_skirt | pleated_skirt | white_shirt | long_sleeves | school_uniform | dress_shirt | ;d | black_socks | blurry | bra_visible_through_clothes | hose | indoors | kneehighs | see-through | wet_shirt | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-------------|:--------|:--------------------|:-----------|:-----------|:-----|:---------|:-------------|:--------------|:--------|:--------------|:-----------------|:-------------|:------------------|:-----------|:---------|:-------------------|:-----------|:--------------|:------|:--------------------------|:---------------|:-----------|:---------------|:--------|:-------|:-----------------|:--------|:---------------|:------|:---------|:----------|:--------------|:-----------|:----------|:------|:--------------|:--------------|:-------------|:--------|:----------------|:-----------------|:-----------|:--------|:-------------------------|:--------|:----------------|:--------------|:----------------|:------------------|:----------------|:-------------------|:--------------------|:------------------|:--------------------|:------|:---------------|:--------------|:------------|:----------------|:--------------|:------------------|:--------------|:--------|:------------------|:---------------|:-------|:---------|:--------|:----------|:-------------|:----------|:-------------|:--------------|:--------|:--------------|:------------------|:-------------|:--------------|:---------------|:-----------------------|:-------|:--------------|:----------|:-------------|:--------------|:-----------------|:--------------|:----------------|:--------------|:---------------|:-----------------|:--------------|:-----|:--------------|:---------|:------------------------------|:-------|:----------|:------------|:--------------|:------------| | 0 | 23 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | | | | | X | | | X | | X | X | | | X | | | | | X | | | | | X | | | | | | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | | X | X | | X | | X | | | | | | | | | | | | | | | | X | X | | | X | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 11 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | | | X | X | | | | | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 12 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | X | X | | | | | | | X | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 8 | 5 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | X | X | | X | | | | | | | X | | | | | | | | X | | | | X | | | | | X | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/aiba_yumi_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T04:25:18+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:33:51+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of aiba\_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls) ============================================================= This is the dataset of aiba\_yumi/相葉夕美 (THE iDOLM@STER: Cinderella Girls), containing 379 images and their tags. The core tags of this character are 'short\_hair, brown\_eyes, breasts, blonde\_hair, bangs, medium\_breasts, brown\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

bedb4cba9abd6b3eb84d3d3d1e906c439ee49f97

# Dataset of senkawa_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls) This is the dataset of senkawa_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls), containing 291 images and their tags. The core tags of this character are `brown_hair, braid, long_hair, single_braid, hair_over_shoulder, breasts, scrunchie, brown_eyes, hair_scrunchie, hair_ornament, 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 | 291 | 278.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 291 | 182.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 641 | 363.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 291 | 252.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 641 | 479.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/senkawa_chihiro_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/senkawa_chihiro_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 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, long_sleeves, solo, black_skirt, blush, collared_shirt, green_jacket, looking_at_viewer, pencil_skirt, red_scrunchie, white_shirt, yellow_necktie, black_pantyhose, office_lady, simple_background, white_background, :d, miniskirt, open_mouth, closed_mouth, dress_shirt, holding, name_tag | | 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, necktie, smile, solo, blush, open_mouth, looking_at_viewer | | 2 | 14 | ![](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, navel, solo, smile, green_bikini, looking_at_viewer, open_mouth, large_breasts, cleavage, frilled_bikini, side-tie_bikini_bottom | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, cleavage, medium_breasts, solo, looking_at_viewer, green_bra, navel, smile, black_thighhighs, green_panties, open_shirt, sitting | | 4 | 5 | ![](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, black_pantyhose, black_skirt, bralines, office_lady, pencil_skirt, solo, white_shirt, ass, bra_visible_through_clothes, from_behind, long_sleeves, closed_eyes, high-waist_skirt, indoors, pantylines, see-through, facing_away, handbag, lanyard, sleeping | | 5 | 8 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | 1girl, detached_collar, playboy_bunny, rabbit_ears, medium_breasts, solo, wrist_cuffs, blush, bowtie, cleavage, fishnet_pantyhose, looking_at_viewer, black_pantyhose, rabbit_tail, strapless_leotard, bare_shoulders, black_leotard, fake_animal_ears, open_mouth, white_background, simple_background, smile | | 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) | bare_shoulders, collarbone, green_dress, strapless_dress, 1girl, bare_arms, blush, looking_at_viewer, pearl_necklace, solo, bow, cleavage, hands_up, medium_breasts, open_mouth, orange_eyes, red_scrunchie, white_background, :d, bead_necklace, closed_mouth, cowboy_shot, gradient_background, hair_between_eyes, large_breasts, own_hands_together, purple_rose, sash, sparkle, standing, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | solo | black_skirt | blush | collared_shirt | green_jacket | looking_at_viewer | pencil_skirt | red_scrunchie | white_shirt | yellow_necktie | black_pantyhose | office_lady | simple_background | white_background | :d | miniskirt | open_mouth | closed_mouth | dress_shirt | holding | name_tag | necktie | smile | navel | green_bikini | large_breasts | cleavage | frilled_bikini | side-tie_bikini_bottom | medium_breasts | green_bra | black_thighhighs | green_panties | open_shirt | sitting | bralines | ass | bra_visible_through_clothes | from_behind | closed_eyes | high-waist_skirt | indoors | pantylines | see-through | facing_away | handbag | lanyard | sleeping | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | bowtie | fishnet_pantyhose | rabbit_tail | strapless_leotard | bare_shoulders | black_leotard | fake_animal_ears | collarbone | green_dress | strapless_dress | bare_arms | pearl_necklace | bow | hands_up | orange_eyes | bead_necklace | cowboy_shot | gradient_background | hair_between_eyes | own_hands_together | purple_rose | sash | sparkle | standing | upper_body | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------|:--------------|:--------|:-----------------|:---------------|:--------------------|:---------------|:----------------|:--------------|:-----------------|:------------------|:--------------|:--------------------|:-------------------|:-----|:------------|:-------------|:---------------|:--------------|:----------|:-----------|:----------|:--------|:--------|:---------------|:----------------|:-----------|:-----------------|:-------------------------|:-----------------|:------------|:-------------------|:----------------|:-------------|:----------|:-----------|:------|:------------------------------|:--------------|:--------------|:-------------------|:----------|:-------------|:--------------|:--------------|:----------|:----------|:-----------|:------------------|:----------------|:--------------|:--------------|:---------|:--------------------|:--------------|:--------------------|:-----------------|:----------------|:-------------------|:-------------|:--------------|:------------------|:------------|:-----------------|:------|:-----------|:--------------|:----------------|:--------------|:----------------------|:--------------------|:---------------------|:--------------|:-------|:----------|:-----------|:-------------| | 0 | 10 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 14 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | X | | X | | | X | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | X | | | X | | | | | | | | | | | | | | | | | X | X | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | 8 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | X | | X | | | X | | | | | X | | X | X | | | X | | | | | | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 6 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | X | | X | | | X | | X | | | | | | X | X | | X | X | | | | | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/senkawa_chihiro_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T04:53:53+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:37:55+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of senkawa\_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls) =========================================================================== This is the dataset of senkawa\_chihiro/千川ちひろ/센카와치히로 (THE iDOLM@STER: Cinderella Girls), containing 291 images and their tags. The core tags of this character are 'brown\_hair, braid, long\_hair, single\_braid, hair\_over\_shoulder, breasts, scrunchie, brown\_eyes, hair\_scrunchie, hair\_ornament, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

40bf935773035a84787afebbfae4834dc52fd9ae

# Dataset Card for "art_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Falah/art_prompts

[ "region:us" ]

2023-09-14T05:31:13+00:00

{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 205606, "num_examples": 1000}], "download_size": 32002, "dataset_size": 205606}}

2023-09-14T05:31:14+00:00

[]

TAGS #region-us

# Dataset Card for "art_prompts" More Information needed

[ "# Dataset Card for \"art_prompts\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"art_prompts\"\n\nMore Information needed" ]

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"art_prompts\"\n\nMore Information needed" ]

ba5d3daf526ea0847f3a737744aeb7e9107b5912

# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/AIDC-ai-business/Marcoroni-70B - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B](https://huggingface.co/AIDC-ai-business/Marcoroni-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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_AIDC-ai-business__Marcoroni-70B", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-19T02:16:50.789886](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B/blob/main/results_2023-09-19T02-16-50.789886.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": 0.23992448312110085, "acc_stderr": 0.031078389352549952, "acc_norm": 0.24054395860756556, "acc_norm_stderr": 0.03108725267744147, "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN }, "harness|arc:challenge|25": { "acc": 0.24744027303754265, "acc_stderr": 0.012610352663292673, "acc_norm": 0.2790102389078498, "acc_norm_stderr": 0.013106784883601346 }, "harness|hellaswag|10": { "acc": 0.2621987651862179, "acc_stderr": 0.004389312748012152, "acc_norm": 0.2671778530173272, "acc_norm_stderr": 0.004415816696303084 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.19, "acc_stderr": 0.03942772444036624, "acc_norm": 0.19, "acc_norm_stderr": 0.03942772444036624 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.18518518518518517, "acc_stderr": 0.0335567721631314, "acc_norm": 0.18518518518518517, "acc_norm_stderr": 0.0335567721631314 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.21710526315789475, "acc_stderr": 0.033550453048829226, "acc_norm": 0.21710526315789475, "acc_norm_stderr": 0.033550453048829226 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.29, "acc_stderr": 0.04560480215720684, "acc_norm": 0.29, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.20754716981132076, "acc_stderr": 0.02495991802891127, "acc_norm": 0.20754716981132076, "acc_norm_stderr": 0.02495991802891127 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.2638888888888889, "acc_stderr": 0.03685651095897532, "acc_norm": 0.2638888888888889, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.19, "acc_stderr": 0.039427724440366234, "acc_norm": 0.19, "acc_norm_stderr": 0.039427724440366234 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.29, "acc_stderr": 0.045604802157206845, "acc_norm": 0.29, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.22, "acc_stderr": 0.04163331998932269, "acc_norm": 0.22, "acc_norm_stderr": 0.04163331998932269 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.18497109826589594, "acc_stderr": 0.029605623981771204, "acc_norm": 0.18497109826589594, "acc_norm_stderr": 0.029605623981771204 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.22549019607843138, "acc_stderr": 0.041583075330832865, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.041583075330832865 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.25, "acc_stderr": 0.04351941398892446, "acc_norm": 0.25, "acc_norm_stderr": 0.04351941398892446 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.2723404255319149, "acc_stderr": 0.029101290698386705, "acc_norm": 0.2723404255319149, "acc_norm_stderr": 0.029101290698386705 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2807017543859649, "acc_stderr": 0.042270544512322, "acc_norm": 0.2807017543859649, "acc_norm_stderr": 0.042270544512322 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.25517241379310346, "acc_stderr": 0.03632984052707842, "acc_norm": 0.25517241379310346, "acc_norm_stderr": 0.03632984052707842 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.24603174603174602, "acc_stderr": 0.022182037202948365, "acc_norm": 0.24603174603174602, "acc_norm_stderr": 0.022182037202948365 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2777777777777778, "acc_stderr": 0.04006168083848876, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.04006168083848876 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.21, "acc_stderr": 0.040936018074033256, "acc_norm": 0.21, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.2, "acc_stderr": 0.022755204959542932, "acc_norm": 0.2, "acc_norm_stderr": 0.022755204959542932 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 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"acc_norm_stderr": 0.025695341643824685 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.25, "acc_stderr": 0.03039153369274154, "acc_norm": 0.25, "acc_norm_stderr": 0.03039153369274154 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.25738396624472576, "acc_stderr": 0.028458820991460302, "acc_norm": 0.25738396624472576, "acc_norm_stderr": 0.028458820991460302 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.2914798206278027, "acc_stderr": 0.030500283176545902, "acc_norm": 0.2914798206278027, "acc_norm_stderr": 0.030500283176545902 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.24427480916030533, "acc_stderr": 0.037683359597287434, "acc_norm": 0.24427480916030533, "acc_norm_stderr": 0.037683359597287434 }, "harness|hendrycksTest-international_law|5": { "acc": 0.2809917355371901, "acc_stderr": 0.04103203830514511, "acc_norm": 0.2809917355371901, "acc_norm_stderr": 0.04103203830514511 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 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0.0446196043338474 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.26181353767560667, "acc_stderr": 0.015720838678445266, "acc_norm": 0.26181353767560667, "acc_norm_stderr": 0.015720838678445266 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24566473988439305, "acc_stderr": 0.02317629820399201, "acc_norm": 0.24566473988439305, "acc_norm_stderr": 0.02317629820399201 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.25251396648044694, "acc_stderr": 0.014530330201468645, "acc_norm": 0.25251396648044694, "acc_norm_stderr": 0.014530330201468645 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.2647058823529412, "acc_stderr": 0.025261691219729487, "acc_norm": 0.2647058823529412, "acc_norm_stderr": 0.025261691219729487 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.2508038585209003, "acc_stderr": 0.024619771956697165, "acc_norm": 0.2508038585209003, "acc_norm_stderr": 0.024619771956697165 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.22530864197530864, "acc_stderr": 0.02324620264781975, "acc_norm": 0.22530864197530864, "acc_norm_stderr": 0.02324620264781975 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.2765957446808511, "acc_stderr": 0.026684564340461004, "acc_norm": 0.2765957446808511, "acc_norm_stderr": 0.026684564340461004 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.25358539765319427, "acc_stderr": 0.011111715336101136, "acc_norm": 0.25358539765319427, "acc_norm_stderr": 0.011111715336101136 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.18382352941176472, "acc_stderr": 0.02352924218519311, "acc_norm": 0.18382352941176472, "acc_norm_stderr": 0.02352924218519311 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.24673202614379086, "acc_stderr": 0.017440820367402493, "acc_norm": 0.24673202614379086, "acc_norm_stderr": 0.017440820367402493 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.19090909090909092, "acc_stderr": 0.03764425585984927, "acc_norm": 0.19090909090909092, "acc_norm_stderr": 0.03764425585984927 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.18775510204081633, "acc_stderr": 0.02500025603954621, "acc_norm": 0.18775510204081633, "acc_norm_stderr": 0.02500025603954621 }, "harness|hendrycksTest-sociology|5": { "acc": 0.23383084577114427, "acc_stderr": 0.029929415408348384, "acc_norm": 0.23383084577114427, "acc_norm_stderr": 0.029929415408348384 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-virology|5": { "acc": 0.25301204819277107, "acc_stderr": 0.03384429155233134, "acc_norm": 0.25301204819277107, "acc_norm_stderr": 0.03384429155233134 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.26900584795321636, "acc_stderr": 0.0340105262010409, "acc_norm": 0.26900584795321636, "acc_norm_stderr": 0.0340105262010409 }, "harness|truthfulqa:mc|0": { "mc1": 1.0, "mc1_stderr": 0.0, "mc2": NaN, "mc2_stderr": NaN } } ``` ### 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]

open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B

[ "region:us" ]

2023-09-14T05:34:49+00:00

{"pretty_name": "Evaluation run of AIDC-ai-business/Marcoroni-70B", "dataset_summary": "Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B](https://huggingface.co/AIDC-ai-business/Marcoroni-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-19T02:16:50.789886](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B/blob/main/results_2023-09-19T02-16-50.789886.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\": 0.23992448312110085,\n \"acc_stderr\": 0.031078389352549952,\n \"acc_norm\": 0.24054395860756556,\n \"acc_norm_stderr\": 0.03108725267744147,\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.24744027303754265,\n \"acc_stderr\": 0.012610352663292673,\n \"acc_norm\": 0.2790102389078498,\n \"acc_norm_stderr\": 0.013106784883601346\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2621987651862179,\n \"acc_stderr\": 0.004389312748012152,\n \"acc_norm\": 0.2671778530173272,\n \"acc_norm_stderr\": 0.004415816696303084\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.0335567721631314,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.0335567721631314\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.21710526315789475,\n \"acc_stderr\": 0.033550453048829226,\n \"acc_norm\": 0.21710526315789475,\n \"acc_norm_stderr\": 0.033550453048829226\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.20754716981132076,\n \"acc_stderr\": 0.02495991802891127,\n \"acc_norm\": 0.20754716981132076,\n \"acc_norm_stderr\": 0.02495991802891127\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2638888888888889,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.029605623981771204,\n \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.029605623981771204\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.041583075330832865,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.041583075330832865\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2723404255319149,\n \"acc_stderr\": 0.029101290698386705,\n \"acc_norm\": 0.2723404255319149,\n \"acc_norm_stderr\": 0.029101290698386705\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2807017543859649,\n \"acc_stderr\": 0.042270544512322,\n \"acc_norm\": 0.2807017543859649,\n \"acc_norm_stderr\": 0.042270544512322\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.25517241379310346,\n \"acc_stderr\": 0.03632984052707842,\n \"acc_norm\": 0.25517241379310346,\n \"acc_norm_stderr\": 0.03632984052707842\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.24603174603174602,\n \"acc_stderr\": 0.022182037202948365,\n \"acc_norm\": 0.24603174603174602,\n \"acc_norm_stderr\": 0.022182037202948365\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.04006168083848876,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.04006168083848876\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.022755204959542932,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.022755204959542932\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.22167487684729065,\n \"acc_stderr\": 0.029225575892489607,\n \"acc_norm\": 0.22167487684729065,\n \"acc_norm_stderr\": 0.029225575892489607\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.0340150671524904,\n \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.0340150671524904\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.20707070707070707,\n \"acc_stderr\": 0.02886977846026705,\n \"acc_norm\": 0.20707070707070707,\n \"acc_norm_stderr\": 0.02886977846026705\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.2849740932642487,\n \"acc_stderr\": 0.03257714077709661,\n \"acc_norm\": 0.2849740932642487,\n \"acc_norm_stderr\": 0.03257714077709661\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.23333333333333334,\n \"acc_stderr\": 0.021444547301560486,\n \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.021444547301560486\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.22592592592592592,\n \"acc_stderr\": 0.025497532639609542,\n \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.025497532639609542\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.19747899159663865,\n \"acc_stderr\": 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2023-09-19T01:18:15+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard. The dataset is composed of 61 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-09-19T02:16:50.789886(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-19T02:16:50.789886(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-19T02:16:50.789886(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 21, 31, 169, 66, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AIDC-ai-business/Marcoroni-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-19T02:16:50.789886(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

191b42bf54afb83a552cb0fcf9e7deed68c9645a

# Dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls) This is the dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are `dark-skinned_female, dark_skin, short_hair, purple_eyes, breasts, black_hair, green_hair, large_breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 489 | 487.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 489 | 328.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1086 | 654.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 489 | 447.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natalia_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1086 | 855.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/natalia_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/natalia_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 | 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, earrings, midriff, open_mouth, solo, bracelet, card_parody, character_name, star_(symbol), sun_symbol, black_thighhighs, cleavage, dancer, navel, :d, bow, panties | | 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) | hair_flower, smile, 1girl, solo, cleavage, elbow_gloves, blush, earrings, open_mouth, wedding_dress, bare_shoulders | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, necklace, solo, white_dress, bare_shoulders, blush, looking_at_viewer, open_mouth, cleavage, :d, bracelet | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, elbow_gloves, looking_at_viewer, solo, blush, bridal_veil, rose, wedding_dress, white_gloves, bouquet, open_mouth, :d, cleavage, earrings, strapless | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, collarbone, looking_at_viewer, solo, white_shirt, short_sleeves, upper_body, wet_shirt, simple_background, white_background, :d, bangs, black_skirt, bra, cleavage, neckerchief, open_mouth, pleated_skirt, sailor_collar, see-through, serafuku | | 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, looking_at_viewer, nipples, nude, smile, solo, collarbone, doujin_cover, content_rating, open_mouth, simple_background, upper_body | | 6 | 9 | ![](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) | blush, looking_at_viewer, 1girl, solo, underwear_only, white_background, cleavage, smile, white_panties, navel, simple_background, white_bra, open_mouth | | 7 | 17 | ![](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) | 1boy, 1girl, blush, hetero, nipples, solo_focus, open_mouth, sex, sweat, censored, girl_on_top, nude, vaginal, penis, navel, cowgirl_position, smile, pussy | | 8 | 12 | ![](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) | playboy_bunny, rabbit_ears, smile, 1girl, detached_collar, fake_animal_ears, looking_at_viewer, rabbit_tail, solo, fake_tail, strapless_leotard, wrist_cuffs, black_leotard, blush, simple_background, white_background, bare_shoulders, bowtie, cleavage, cowboy_shot, armpits, arms_up, brown_pantyhose, fishnet_pantyhose, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | earrings | midriff | open_mouth | solo | bracelet | card_parody | character_name | star_(symbol) | sun_symbol | black_thighhighs | cleavage | dancer | navel | :d | bow | panties | hair_flower | smile | elbow_gloves | blush | wedding_dress | bare_shoulders | necklace | white_dress | looking_at_viewer | bridal_veil | rose | white_gloves | bouquet | strapless | collarbone | white_shirt | short_sleeves | upper_body | wet_shirt | simple_background | white_background | bangs | black_skirt | bra | neckerchief | pleated_skirt | sailor_collar | see-through | serafuku | nipples | nude | doujin_cover | content_rating | underwear_only | white_panties | white_bra | 1boy | hetero | solo_focus | sex | sweat | censored | girl_on_top | vaginal | penis | cowgirl_position | pussy | playboy_bunny | rabbit_ears | detached_collar | fake_animal_ears | rabbit_tail | fake_tail | strapless_leotard | wrist_cuffs | black_leotard | bowtie | cowboy_shot | armpits | arms_up | brown_pantyhose | fishnet_pantyhose | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:----------|:-------------|:-------|:-----------|:--------------|:-----------------|:----------------|:-------------|:-------------------|:-----------|:---------|:--------|:-----|:------|:----------|:--------------|:--------|:---------------|:--------|:----------------|:-----------------|:-----------|:--------------|:--------------------|:--------------|:-------|:---------------|:----------|:------------|:-------------|:--------------|:----------------|:-------------|:------------|:--------------------|:-------------------|:--------|:--------------|:------|:--------------|:----------------|:----------------|:--------------|:-----------|:----------|:-------|:---------------|:-----------------|:-----------------|:----------------|:------------|:-------|:---------|:-------------|:------|:--------|:-----------|:--------------|:----------|:--------|:-------------------|:--------|:----------------|:--------------|:------------------|:-------------------|:--------------|:------------|:--------------------|:--------------|:----------------|:---------|:--------------|:----------|:----------|:------------------|:--------------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | | | X | X | X | | | | | | X | | | X | | | | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | X | | | | | | | X | | | X | | | | | X | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | | | X | X | | | | | | | X | | | X | | | | | | X | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 9 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 17 | ![](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 | | | | | | | | | | | | | | | | | 8 | 12 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | X | X | | | | | | | X | | | | | | | X | | X | | X | | | X | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/natalia_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T05:45:25+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T19:16:31+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls) =========================================================== This is the dataset of natalia/ナターリア (THE iDOLM@STER: Cinderella Girls), containing 489 images and their tags. The core tags of this character are 'dark-skinned\_female, dark\_skin, short\_hair, purple\_eyes, breasts, black\_hair, green\_hair, large\_breasts, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

6927da00136947b440a657c2773e1a706c1e1ca9

# Bangumi Image Base of Sakurasou No Pet Na Kanojo This is the image base of bangumi Sakurasou no Pet na Kanojo, we detected 24 characters, 4107 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 1328 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 405 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 313 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 33 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 18 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 46 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | ![preview 7](5/preview_7.png) | ![preview 8](5/preview_8.png) | | 6 | 47 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 74 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 580 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 105 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 43 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 523 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 43 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 71 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 11 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | 15 | 21 | [Download](15/dataset.zip) | ![preview 1](15/preview_1.png) | ![preview 2](15/preview_2.png) | ![preview 3](15/preview_3.png) | ![preview 4](15/preview_4.png) | ![preview 5](15/preview_5.png) | ![preview 6](15/preview_6.png) | ![preview 7](15/preview_7.png) | ![preview 8](15/preview_8.png) | | 16 | 139 | [Download](16/dataset.zip) | ![preview 1](16/preview_1.png) | ![preview 2](16/preview_2.png) | ![preview 3](16/preview_3.png) | ![preview 4](16/preview_4.png) | ![preview 5](16/preview_5.png) | ![preview 6](16/preview_6.png) | ![preview 7](16/preview_7.png) | ![preview 8](16/preview_8.png) | | 17 | 13 | [Download](17/dataset.zip) | ![preview 1](17/preview_1.png) | ![preview 2](17/preview_2.png) | ![preview 3](17/preview_3.png) | ![preview 4](17/preview_4.png) | ![preview 5](17/preview_5.png) | ![preview 6](17/preview_6.png) | ![preview 7](17/preview_7.png) | ![preview 8](17/preview_8.png) | | 18 | 9 | [Download](18/dataset.zip) | ![preview 1](18/preview_1.png) | ![preview 2](18/preview_2.png) | ![preview 3](18/preview_3.png) | ![preview 4](18/preview_4.png) | ![preview 5](18/preview_5.png) | ![preview 6](18/preview_6.png) | ![preview 7](18/preview_7.png) | ![preview 8](18/preview_8.png) | | 19 | 28 | [Download](19/dataset.zip) | ![preview 1](19/preview_1.png) | ![preview 2](19/preview_2.png) | ![preview 3](19/preview_3.png) | ![preview 4](19/preview_4.png) | ![preview 5](19/preview_5.png) | ![preview 6](19/preview_6.png) | ![preview 7](19/preview_7.png) | ![preview 8](19/preview_8.png) | | 20 | 9 | [Download](20/dataset.zip) | ![preview 1](20/preview_1.png) | ![preview 2](20/preview_2.png) | ![preview 3](20/preview_3.png) | ![preview 4](20/preview_4.png) | ![preview 5](20/preview_5.png) | ![preview 6](20/preview_6.png) | ![preview 7](20/preview_7.png) | ![preview 8](20/preview_8.png) | | 21 | 20 | [Download](21/dataset.zip) | ![preview 1](21/preview_1.png) | ![preview 2](21/preview_2.png) | ![preview 3](21/preview_3.png) | ![preview 4](21/preview_4.png) | ![preview 5](21/preview_5.png) | ![preview 6](21/preview_6.png) | ![preview 7](21/preview_7.png) | ![preview 8](21/preview_8.png) | | 22 | 9 | [Download](22/dataset.zip) | ![preview 1](22/preview_1.png) | ![preview 2](22/preview_2.png) | ![preview 3](22/preview_3.png) | ![preview 4](22/preview_4.png) | ![preview 5](22/preview_5.png) | ![preview 6](22/preview_6.png) | ![preview 7](22/preview_7.png) | ![preview 8](22/preview_8.png) | | noise | 219 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

BangumiBase/sakurasounopetnakanojo

[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]

2023-09-14T05:45:43+00:00

{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}

2023-09-29T06:39:39+00:00

[]

TAGS #size_categories-1K<n<10K #license-mit #art #region-us

Bangumi Image Base of Sakurasou No Pet Na Kanojo ================================================ This is the image base of bangumi Sakurasou no Pet na Kanojo, we detected 24 characters, 4107 images in total. The full dataset is here. Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview:

[]

[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

[ 25 ]

[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

f5c39d4691446dcc605d0ddf4a198a3286d4e0f5

# Dataset of shirayuki_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls) This is the dataset of shirayuki_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls), containing 323 images and their tags. The core tags of this character are `black_hair, short_hair, bangs, purple_eyes, 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 | 323 | 392.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 323 | 225.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 741 | 469.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 323 | 349.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 741 | 680.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shirayuki_chiyo_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/shirayuki_chiyo_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 | 15 | ![](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_gloves, black_serafuku, black_skirt, long_sleeves, pleated_skirt, red_neckerchief, solo, looking_at_viewer, black_shirt, simple_background, white_sailor_collar, bob_cut, closed_mouth, white_background | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, red_neckerchief, solo, upper_body, long_sleeves, simple_background, white_sailor_collar, black_gloves, black_shirt, closed_mouth, white_background, black_serafuku, blush, bob_cut | | 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_serafuku, black_shirt, bob_cut, looking_at_viewer, red_neckerchief, solo, upper_body, white_sailor_collar, parted_lips, shaded_face, disgust, simple_background, white_background | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, black_bikini, collarbone, solo, bare_shoulders, blush, looking_at_viewer, navel, small_breasts, white_background | | 4 | 5 | ![](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, black_dress, black_gloves, elbow_gloves, looking_at_viewer, solo, bare_shoulders, bracelet, frills, bob_cut, bowtie, flower, hair_ornament, parted_lips, ribbon, simple_background, sleeveless_dress, upper_body | | 5 | 9 | ![](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) | looking_at_viewer, maid_headdress, 1girl, maid_apron, solo, black_gloves, bowtie, brooch, juliet_sleeves, black_dress, blush, grey_background, blue_bow, enmaided, simple_background, white_apron | | 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) | ascot, black_gloves, feather_hair_ornament, holding_sword, looking_at_viewer, 1girl, belt, closed_mouth, long_sleeves, solo, white_background, blush, brooch, sheathed, simple_background, smile, black_thighhighs, blue_flower, blue_ribbon, bob_cut, cape, cowboy_shot, petals, scabbard, skirt, white_jacket | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | 1boy, 1girl, blush, hetero, vaginal, breasts, penis, serafuku, sex, solo_focus, pussy, black_gloves, missionary, mosaic_censoring, nipples, on_back, open_mouth, pubic_hair, bar_censor, navel, pantyhose, spread_legs, torn_clothes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | black_serafuku | black_skirt | long_sleeves | pleated_skirt | red_neckerchief | solo | looking_at_viewer | black_shirt | simple_background | white_sailor_collar | bob_cut | closed_mouth | white_background | upper_body | blush | parted_lips | shaded_face | disgust | black_bikini | collarbone | bare_shoulders | navel | small_breasts | black_dress | elbow_gloves | bracelet | frills | bowtie | flower | hair_ornament | ribbon | sleeveless_dress | maid_headdress | maid_apron | brooch | juliet_sleeves | grey_background | blue_bow | enmaided | white_apron | ascot | feather_hair_ornament | holding_sword | belt | sheathed | smile | black_thighhighs | blue_flower | blue_ribbon | cape | cowboy_shot | petals | scabbard | skirt | white_jacket | 1boy | hetero | vaginal | breasts | penis | serafuku | sex | solo_focus | pussy | missionary | mosaic_censoring | nipples | on_back | open_mouth | pubic_hair | bar_censor | pantyhose | spread_legs | torn_clothes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------------|:--------------|:---------------|:----------------|:------------------|:-------|:--------------------|:--------------|:--------------------|:----------------------|:----------|:---------------|:-------------------|:-------------|:--------|:--------------|:--------------|:----------|:---------------|:-------------|:-----------------|:--------|:----------------|:--------------|:---------------|:-----------|:---------|:---------|:---------|:----------------|:---------|:-------------------|:-----------------|:-------------|:---------|:-----------------|:------------------|:-----------|:-----------|:--------------|:--------|:------------------------|:----------------|:-------|:-----------|:--------|:-------------------|:--------------|:--------------|:-------|:--------------|:---------|:-----------|:--------|:---------------|:-------|:---------|:----------|:----------|:--------|:-----------|:------|:-------------|:--------|:-------------|:-------------------|:----------|:----------|:-------------|:-------------|:-------------|:------------|:--------------|:---------------| | 0 | 15 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | | | | | | X | X | | | | | | X | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | | | | X | X | | X | | X | | | X | | X | | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 9 | ![](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 | 5 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | | | X | | | X | X | | X | | X | X | X | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 7 | 9 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/shirayuki_chiyo_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T06:12:02+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:48:10+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of shirayuki\_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls) =================================================================== This is the dataset of shirayuki\_chiyo/白雪千夜 (THE iDOLM@STER: Cinderella Girls), containing 323 images and their tags. The core tags of this character are 'black\_hair, short\_hair, bangs, purple\_eyes, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

82cbea549dcd66d035bfb866b6d806152327c00c

# Dataset Card for "election2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davidadamczyk/election2

[ "region:us" ]

2023-09-14T06:12:18+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "text_label", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 108283.95478723405, "num_examples": 526}, {"name": "test", "num_bytes": 46525.04521276596, "num_examples": 226}], "download_size": 84563, "dataset_size": 154809.0}}

2023-09-14T06:12:25+00:00

[]

TAGS #region-us

# Dataset Card for "election2" More Information needed

[ "# Dataset Card for \"election2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"election2\"\n\nMore Information needed" ]

[ 6, 13 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"election2\"\n\nMore Information needed" ]

e8558fb2527e2d1d63d55ba1f4030023e044ad8e

# Dataset Card for "corpus_1_clustered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HydraLM/corpus_1_clustered

[ "region:us" ]

2023-09-14T06:18:45+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 99791008, "num_examples": 10000}], "download_size": 75705515, "dataset_size": 99791008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T06:34:35+00:00

[]

TAGS #region-us

# Dataset Card for "corpus_1_clustered" More Information needed

[ "# Dataset Card for \"corpus_1_clustered\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"corpus_1_clustered\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered\"\n\nMore Information needed" ]

1ead7035f848c138e7091e0e103a0d8d3d87f9f1

# Dataset Card for "corpus_1_clustered_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pharaouk/corpus_1_clustered_2

[ "region:us" ]

2023-09-14T06:37:05+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 99791008, "num_examples": 10000}], "download_size": 75705515, "dataset_size": 99791008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T06:37:18+00:00

[]

TAGS #region-us

# Dataset Card for "corpus_1_clustered_2" More Information needed

[ "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]

f9257e15d84620f869fb7f33fa7ed5020c5fb59e

A collection of about 21k urls of news articles taken from RAI news sites (national and regionals). The file ("urls_train_set.csv") contains around 20k of them reffering to articles published in the period 01/01/2022 – 09/03/2023 while the file ("urls_test_set.csv") contains urls referring to articles published in the period 10/03/2023 - 04/05/2023.

raicrits/news_urls

[ "license:other", "region:us" ]

2023-09-14T06:38:55+00:00

{"license": "other"}

2023-09-14T06:43:51+00:00

[]

TAGS #license-other #region-us

A collection of about 21k urls of news articles taken from RAI news sites (national and regionals). The file ("urls_train_set.csv") contains around 20k of them reffering to articles published in the period 01/01/2022 – 09/03/2023 while the file ("urls_test_set.csv") contains urls referring to articles published in the period 10/03/2023 - 04/05/2023.

[]

[ "TAGS\n#license-other #region-us \n" ]

[ 11 ]

[ "passage: TAGS\n#license-other #region-us \n" ]

ad19f815c8d8263b39b825cf275d797b7803c49e

# Dataset Card for "corpus_1_clustered_2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HydraLM/corpus_1_clustered_2

[ "region:us" ]

2023-09-14T06:39:25+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 99791008, "num_examples": 10000}], "download_size": 0, "dataset_size": 99791008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T06:42:45+00:00

[]

TAGS #region-us

# Dataset Card for "corpus_1_clustered_2" More Information needed

[ "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered_2\"\n\nMore Information needed" ]

e774e584e6f02954246f36048ec9eeeb15df2001

# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/vihangd/smartyplats-3b-v2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [vihangd/smartyplats-3b-v2](https://huggingface.co/vihangd/smartyplats-3b-v2) 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_vihangd__smartyplats-3b-v2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T18:23:12.485611](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__smartyplats-3b-v2/blob/main/results_2023-10-28T18-23-12.485611.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": 0.0016778523489932886, "em_stderr": 0.00041913301788268375, "f1": 0.05385906040268484, "f1_stderr": 0.0013190145725969279, "acc": 0.3426093528318133, "acc_stderr": 0.008335127579695843 }, "harness|drop|3": { "em": 0.0016778523489932886, "em_stderr": 0.00041913301788268375, "f1": 0.05385906040268484, "f1_stderr": 0.0013190145725969279 }, "harness|gsm8k|5": { "acc": 0.01592115238817286, "acc_stderr": 0.0034478192723889915 }, "harness|winogrande|5": { "acc": 0.6692975532754538, "acc_stderr": 0.013222435887002695 } } ``` ### 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]

open-llm-leaderboard/details_vihangd__smartyplats-3b-v2

[ "region:us" ]

2023-09-14T06:53:24+00:00

{"pretty_name": "Evaluation run of vihangd/smartyplats-3b-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [vihangd/smartyplats-3b-v2](https://huggingface.co/vihangd/smartyplats-3b-v2) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vihangd__smartyplats-3b-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T18:23:12.485611](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__smartyplats-3b-v2/blob/main/results_2023-10-28T18-23-12.485611.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\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268375,\n \"f1\": 0.05385906040268484,\n \"f1_stderr\": 0.0013190145725969279,\n \"acc\": 0.3426093528318133,\n \"acc_stderr\": 0.008335127579695843\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268375,\n \"f1\": 0.05385906040268484,\n \"f1_stderr\": 0.0013190145725969279\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01592115238817286,\n \"acc_stderr\": 0.0034478192723889915\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6692975532754538,\n \"acc_stderr\": 0.013222435887002695\n }\n}\n```", "repo_url": "https://huggingface.co/vihangd/smartyplats-3b-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-28T17:23:25+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-28T18:23:12.485611(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T18:23:12.485611(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T18:23:12.485611(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 21, 31, 169, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of vihangd/smartyplats-3b-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model vihangd/smartyplats-3b-v2 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T18:23:12.485611(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

9cda9018e10a8dbf57a56efacb752415fc801aa0

# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/HyperbeeAI/Tulpar-7b-v1 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [HyperbeeAI/Tulpar-7b-v1](https://huggingface.co/HyperbeeAI/Tulpar-7b-v1) 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_HyperbeeAI__Tulpar-7b-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T09:02:28.162757](https://huggingface.co/datasets/open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1/blob/main/results_2023-10-23T09-02-28.162757.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": 0.2915268456375839, "em_stderr": 0.004654152691335802, "f1": 0.3658179530201351, "f1_stderr": 0.004568137923093851, "acc": 0.3656847615417434, "acc_stderr": 0.0074116135789820126 }, "harness|drop|3": { "em": 0.2915268456375839, "em_stderr": 0.004654152691335802, "f1": 0.3658179530201351, "f1_stderr": 0.004568137923093851 }, "harness|gsm8k|5": { "acc": 0.006823351023502654, "acc_stderr": 0.0022675371022544944 }, "harness|winogrande|5": { "acc": 0.7245461720599842, "acc_stderr": 0.01255569005570953 } } ``` ### 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]

open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1

[ "region:us" ]

2023-09-14T06:59:55+00:00

{"pretty_name": "Evaluation run of HyperbeeAI/Tulpar-7b-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [HyperbeeAI/Tulpar-7b-v1](https://huggingface.co/HyperbeeAI/Tulpar-7b-v1) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T09:02:28.162757](https://huggingface.co/datasets/open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v1/blob/main/results_2023-10-23T09-02-28.162757.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\": 0.2915268456375839,\n \"em_stderr\": 0.004654152691335802,\n \"f1\": 0.3658179530201351,\n \"f1_stderr\": 0.004568137923093851,\n \"acc\": 0.3656847615417434,\n \"acc_stderr\": 0.0074116135789820126\n },\n \"harness|drop|3\": {\n \"em\": 0.2915268456375839,\n \"em_stderr\": 0.004654152691335802,\n \"f1\": 0.3658179530201351,\n \"f1_stderr\": 0.004568137923093851\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \"acc_stderr\": 0.0022675371022544944\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7245461720599842,\n \"acc_stderr\": 0.01255569005570953\n }\n}\n```", "repo_url": "https://huggingface.co/HyperbeeAI/Tulpar-7b-v1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T07_59_39.326009", 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2023-10-23T08:02:42+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-23T09:02:28.162757(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T09:02:28.162757(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T09:02:28.162757(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 22, 31, 170, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HyperbeeAI/Tulpar-7b-v1## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model HyperbeeAI/Tulpar-7b-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T09:02:28.162757(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

b7aa99eccfed2b60cd47b13827c2a7738a3dfae5

# Dataset Card for "oasst1-m2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

TinyPixel/oasst1-m2

[ "region:us" ]

2023-09-14T07:02:34+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9483398, "num_examples": 8274}], "download_size": 5130538, "dataset_size": 9483398}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-15T03:13:51+00:00

[]

TAGS #region-us

# Dataset Card for "oasst1-m2" More Information needed

[ "# Dataset Card for \"oasst1-m2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"oasst1-m2\"\n\nMore Information needed" ]

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"oasst1-m2\"\n\nMore Information needed" ]

6cfaf7f49a3da66cc46700e7e4c89695a814342a

# Dataset Card for "finreport-llama2-5k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Arrivedercis/finreport-llama2-5k

[ "region:us" ]

2023-09-14T07:14:03+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2293425, "num_examples": 10000}], "download_size": 1144776, "dataset_size": 2293425}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-16T01:49:04+00:00

[]

TAGS #region-us

# Dataset Card for "finreport-llama2-5k" More Information needed

[ "# Dataset Card for \"finreport-llama2-5k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"finreport-llama2-5k\"\n\nMore Information needed" ]

[ 6, 18 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"finreport-llama2-5k\"\n\nMore Information needed" ]

de71631994c8a0926f58cc0c857d75b0cd542f3f

# Dataset Card for "clustered_1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

HydraLM/clustered_1

[ "region:us" ]

2023-09-14T07:19:05+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}, {"name": "text_processed", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "cluster", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 17476162280, "num_examples": 1472917}], "download_size": 12523176003, "dataset_size": 17476162280}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T09:21:47+00:00

[]

TAGS #region-us

# Dataset Card for "clustered_1" More Information needed

[ "# Dataset Card for \"clustered_1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"clustered_1\"\n\nMore Information needed" ]

[ 6, 14 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"clustered_1\"\n\nMore Information needed" ]

fa53b1d472f1e35069d8154133b8445c4f47703b

# Dataset of morikubo_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls) This is the dataset of morikubo_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are `brown_eyes, bangs, light_brown_hair, long_hair, drill_hair, earrings, brown_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 | 500 | 583.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 323.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1042 | 678.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 504.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1042 | 997.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/morikubo_nono_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/morikubo_nono_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 | 16 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, blue_dress, simple_background, ringlets, upper_body, white_background, puffy_short_sleeves, medium_hair, open_mouth, blush, sweat, @_@, stud_earrings, tears, hair_ornament | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blue_dress, blue_footwear, chibi, puffy_short_sleeves, ringlets, solo, collared_dress, shoes, closed_mouth, holding, standing, :3, blush, full_body, outdoors, sitting, white_background, white_socks | | 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, blonde_hair, blush, looking_at_viewer, solo, upper_body, holding, flower, jewelry, long_sleeves, ascot, medium_hair, open_mouth, simple_background | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, simple_background, solo, white_background, bare_shoulders, smile, collarbone, looking_at_viewer, medium_hair, ringlets, sleeveless_dress, upper_body, blonde_hair, choker, jewelry | | 4 | 13 | ![](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, jewelry, solo, blush, sleeveless, black_gloves, looking_at_viewer, smile, corset, green_dress, holding_microphone, open_mouth, simple_background, white_background, blonde_hair, frills, lace, @_@, hair_bow, hair_ornament, thighhighs | | 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) | 1boy, 1girl, blush, hetero, solo_focus, nipples, nude, open_mouth, simple_background, sweat, @_@, bar_censor, cum, medium_hair, penis, small_breasts, tears, white_background, blonde_hair, handjob, navel, pussy, ringlets, sex | | 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, hair_bow, ringlets, solo, blush, floral_print, looking_at_viewer, upper_body, wide_sleeves, frills, holding, jewelry, long_sleeves, open_mouth, print_kimono, ribbon, smile, blonde_hair | | 7 | 15 | ![](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, solo, blush, frilled_bikini, floral_print, hair_flower, navel, ringlets, white_bikini, collarbone, wavy_mouth, looking_at_viewer, outdoors, print_bikini, small_breasts, water, bare_shoulders, blonde_hair, open_mouth | | 8 | 9 | ![](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) | 1boy, 1girl, blush, hetero, solo_focus, open_mouth, penis, pussy, tears, sweat, vaginal, long_sleeves, mosaic_censoring, on_back, short_hair, wavy_mouth, blonde_hair, clothed_sex, dress, hair_flower, jaggy_lines, jewelry, oekaki, panties, spread_legs | | 9 | 5 | ![](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, one_side_up, pleated_skirt, school_uniform, solo, hair_scrunchie, long_sleeves, stud_earrings, white_background, cardigan, kogal, looking_at_viewer, nail_polish, necklace, simple_background, blue_skirt, cellphone, charm_(object), flying_sweatdrops, from_below, green_bow, green_scrunchie, hairclip, holding_phone, loose_bowtie, loose_socks, medium_breasts, open_mouth, pantyshot, school_bag, sitting, striped_panties, white_shirt, white_socks | | 10 | 5 | ![](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) | 1girl, cosplay, ringlets, simple_background, solo, white_background, belt_buckle, black_footwear, shoes, sweat, full_body, long_sleeves, nose_blush, open_jacket, white_shirt, >_<, @_@, black_shirt, blue_pants, boots, brown_footwear, chain, closed_eyes, collared_shirt, fingerless_gloves, holding_weapon, open_mouth, parted_lips, standing, tears, wavy_mouth, white_gloves, white_jacket | | 11 | 13 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | 1girl, solo, bow, jewelry, blush, cape, long_sleeves, star_(symbol), fur-trimmed_cloak, looking_at_viewer, mini_crown, shorts, smile, side_ponytail, white_background | | 12 | 9 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | maid_headdress, blush, enmaided, frills, 1girl, black_dress, jewelry, solo, long_sleeves, open_mouth, puffy_sleeves, ringlets, blonde_hair, bow, looking_at_viewer, maid_apron, wavy_mouth, simple_background, white_apron, white_background | | 13 | 5 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-sample4.png) | 1girl, @_@, fake_animal_ears, rabbit_ears, ringlets, solo, white_background, detached_collar, nose_blush, simple_background, sweat, bare_shoulders, black_bowtie, black_jacket, hand_up, navel, white_collar, wing_collar, wrist_cuffs, arm_behind_back, black_hairband, black_leotard, breasts, closed_mouth, hair_between_eyes, hands_up, long_sleeves, looking_away, no_pants, pantyhose, playboy_bunny, rabbit_tail, strapless_leotard, wavy_mouth, white_gloves, white_panties | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blue_dress | simple_background | ringlets | upper_body | white_background | puffy_short_sleeves | medium_hair | open_mouth | blush | sweat | @_@ | stud_earrings | tears | hair_ornament | blue_footwear | chibi | collared_dress | shoes | closed_mouth | holding | standing | :3 | full_body | outdoors | sitting | white_socks | blonde_hair | looking_at_viewer | flower | jewelry | long_sleeves | ascot | bare_shoulders | smile | collarbone | sleeveless_dress | choker | sleeveless | black_gloves | corset | green_dress | holding_microphone | frills | lace | hair_bow | thighhighs | 1boy | hetero | solo_focus | nipples | nude | bar_censor | cum | penis | small_breasts | handjob | navel | pussy | sex | floral_print | wide_sleeves | print_kimono | ribbon | frilled_bikini | hair_flower | white_bikini | wavy_mouth | print_bikini | water | vaginal | mosaic_censoring | on_back | short_hair | clothed_sex | dress | jaggy_lines | oekaki | panties | spread_legs | one_side_up | pleated_skirt | school_uniform | hair_scrunchie | cardigan | kogal | nail_polish | necklace | blue_skirt | cellphone | charm_(object) | flying_sweatdrops | from_below | green_bow | green_scrunchie | hairclip | holding_phone | loose_bowtie | loose_socks | medium_breasts | pantyshot | school_bag | striped_panties | white_shirt | cosplay | belt_buckle | black_footwear | nose_blush | open_jacket | >_< | black_shirt | blue_pants | boots | brown_footwear | chain | closed_eyes | collared_shirt | fingerless_gloves | holding_weapon | parted_lips | white_gloves | white_jacket | bow | cape | star_(symbol) | fur-trimmed_cloak | mini_crown | shorts | side_ponytail | maid_headdress | enmaided | black_dress | puffy_sleeves | maid_apron | white_apron | fake_animal_ears | rabbit_ears | detached_collar | black_bowtie | black_jacket | hand_up | white_collar | wing_collar | wrist_cuffs | arm_behind_back | black_hairband | black_leotard | breasts | hair_between_eyes | hands_up | looking_away | no_pants | pantyhose | playboy_bunny | rabbit_tail | strapless_leotard | white_panties | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:-------------|:--------------------|:-----------|:-------------|:-------------------|:----------------------|:--------------|:-------------|:--------|:--------|:------|:----------------|:--------|:----------------|:----------------|:--------|:-----------------|:--------|:---------------|:----------|:-----------|:-----|:------------|:-----------|:----------|:--------------|:--------------|:--------------------|:---------|:----------|:---------------|:--------|:-----------------|:--------|:-------------|:-------------------|:---------|:-------------|:---------------|:---------|:--------------|:---------------------|:---------|:-------|:-----------|:-------------|:-------|:---------|:-------------|:----------|:-------|:-------------|:------|:--------|:----------------|:----------|:--------|:--------|:------|:---------------|:---------------|:---------------|:---------|:-----------------|:--------------|:---------------|:-------------|:---------------|:--------|:----------|:-------------------|:----------|:-------------|:--------------|:--------|:--------------|:---------|:----------|:--------------|:--------------|:----------------|:-----------------|:-----------------|:-----------|:--------|:--------------|:-----------|:-------------|:------------|:-----------------|:--------------------|:-------------|:------------|:------------------|:-----------|:----------------|:---------------|:--------------|:-----------------|:------------|:-------------|:------------------|:--------------|:----------|:--------------|:-----------------|:-------------|:--------------|:------|:--------------|:-------------|:--------|:-----------------|:--------|:--------------|:-----------------|:--------------------|:-----------------|:--------------|:---------------|:---------------|:------|:-------|:----------------|:--------------------|:-------------|:---------|:----------------|:-----------------|:-----------|:--------------|:----------------|:-------------|:--------------|:-------------------|:--------------|:------------------|:---------------|:---------------|:----------|:---------------|:--------------|:--------------|:------------------|:-----------------|:----------------|:----------|:--------------------|:-----------|:---------------|:-----------|:------------|:----------------|:--------------|:--------------------|:----------------| | 0 | 16 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | | X | | X | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | X | X | X | X | | X | | X | | | | | | | | | | | | | | | | | | X | X | | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 13 | ![](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 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | | X | X | | X | | X | X | X | X | X | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 15 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | | | | | | | | | X | X | X | | | X | | | | | | | | | | | | | | X | | | X | X | | | | | | | | | | | | | | | | X | X | X | | | | | X | | | | X | | | | | | | X | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | | X | | | X | | | X | X | | | X | | | | | | | | | | | | | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 5 | ![](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 | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 13 | ![](samples/11/clu11-sample0.png) | ![](samples/11/clu11-sample1.png) | ![](samples/11/clu11-sample2.png) | ![](samples/11/clu11-sample3.png) | ![](samples/11/clu11-sample4.png) | X | X | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 12 | 9 | ![](samples/12/clu12-sample0.png) | ![](samples/12/clu12-sample1.png) | ![](samples/12/clu12-sample2.png) | ![](samples/12/clu12-sample3.png) | ![](samples/12/clu12-sample4.png) | X | X | | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | X | X | | X | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 13 | 5 | ![](samples/13/clu13-sample0.png) | ![](samples/13/clu13-sample1.png) | ![](samples/13/clu13-sample2.png) | ![](samples/13/clu13-sample3.png) | ![](samples/13/clu13-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 |

CyberHarem/morikubo_nono_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T07:19:22+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T11:11:17+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of morikubo\_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls) ========================================================================= This is the dataset of morikubo\_nono/森久保乃々/모리쿠보노노 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags. The core tags of this character are 'brown\_eyes, bangs, light\_brown\_hair, long\_hair, drill\_hair, earrings, brown\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

fd3e012225362817e83b6e7ed6bf6a0e6551614a

# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w](https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w) 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_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-25T02:53:14.013945](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w/blob/main/results_2023-10-25T02-53-14.013945.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": 0.002936241610738255, "em_stderr": 0.0005541113054709996, "f1": 0.0881931627516778, "f1_stderr": 0.0017824166498732977, "acc": 0.43444724154830167, "acc_stderr": 0.01050882298494655 }, "harness|drop|3": { "em": 0.002936241610738255, "em_stderr": 0.0005541113054709996, "f1": 0.0881931627516778, "f1_stderr": 0.0017824166498732977 }, "harness|gsm8k|5": { "acc": 0.11751326762699014, "acc_stderr": 0.008870331256489993 }, "harness|winogrande|5": { "acc": 0.7513812154696132, "acc_stderr": 0.012147314713403108 } } ``` ### 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]

open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w

[ "region:us" ]

2023-09-14T07:19:31+00:00

{"pretty_name": "Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "dataset_summary": "Dataset automatically created during the evaluation run of model [lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w](https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T02:53:14.013945](https://huggingface.co/datasets/open-llm-leaderboard/details_lu-vae__llama2-13B-sharegpt4-orca-openplatypus-8w/blob/main/results_2023-10-25T02-53-14.013945.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\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709996,\n \"f1\": 0.0881931627516778,\n \"f1_stderr\": 0.0017824166498732977,\n \"acc\": 0.43444724154830167,\n \"acc_stderr\": 0.01050882298494655\n },\n \"harness|drop|3\": {\n \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709996,\n \"f1\": 0.0881931627516778,\n \"f1_stderr\": 0.0017824166498732977\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11751326762699014,\n \"acc_stderr\": 0.008870331256489993\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7513812154696132,\n \"acc_stderr\": 0.012147314713403108\n }\n}\n```", "repo_url": "https://huggingface.co/lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|arc:challenge|25_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T02_53_14.013945", "path": ["**/details_harness|drop|3_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T02-53-14.013945.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T02_53_14.013945", "path": ["**/details_harness|gsm8k|5_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T02-53-14.013945.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|hellaswag|10_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-14T08-19-15.271974.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-14T08-19-15.271974.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_25T02_53_14.013945", "path": ["**/details_harness|winogrande|5_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-25T02-53-14.013945.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_14T08_19_15.271974", "path": ["results_2023-09-14T08-19-15.271974.parquet"]}, {"split": "2023_10_25T02_53_14.013945", "path": ["results_2023-10-25T02-53-14.013945.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T02-53-14.013945.parquet"]}]}]}

2023-10-25T01:53:26+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-25T02:53:14.013945(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T02:53:14.013945(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-25T02:53:14.013945(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 33, 31, 181, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model lu-vae/llama2-13B-sharegpt4-orca-openplatypus-8w on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T02:53:14.013945(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

550931003d3619b88aef7c283db4b84eb26ce80d

# Dataset Card for "fabric_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rikdas/fabric_dataset

[ "region:us" ]

2023-09-14T07:30:02+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41259319.0, "num_examples": 20}], "download_size": 41261924, "dataset_size": 41259319.0}}

2023-09-14T07:37:27+00:00

[]

TAGS #region-us

# Dataset Card for "fabric_dataset" More Information needed

[ "# Dataset Card for \"fabric_dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fabric_dataset\"\n\nMore Information needed" ]

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"fabric_dataset\"\n\nMore Information needed" ]

358840310535836cb7df9d872a8d345d35bf12b5

# Dataset Card for "processed_bert_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pteron/processed_bert_dataset

[ "region:us" ]

2023-09-14T07:31:07+00:00

{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 173314800.0, "num_examples": 48143}], "download_size": 41856821, "dataset_size": 173314800.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2023-09-14T07:31:17+00:00

[]

TAGS #region-us

# Dataset Card for "processed_bert_dataset" More Information needed

[ "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"processed_bert_dataset\"\n\nMore Information needed" ]

1031a2d0ed7441b050b2605bb0bcb993f4285bac

# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [Weyaxi/ChatAYT-Lora-Assamble-Marcoroni](https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni) 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_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T13:33:47.797770](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni/blob/main/results_2023-10-24T13-33-47.797770.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": 0.008598993288590604, "em_stderr": 0.0009455579144542189, "f1": 0.1045532718120813, "f1_stderr": 0.0020198084132137728, "acc": 0.43109211314448, "acc_stderr": 0.009797803895878525 }, "harness|drop|3": { "em": 0.008598993288590604, "em_stderr": 0.0009455579144542189, "f1": 0.1045532718120813, "f1_stderr": 0.0020198084132137728 }, "harness|gsm8k|5": { "acc": 0.0887035633055345, "acc_stderr": 0.007831458737058703 }, "harness|winogrande|5": { "acc": 0.7734806629834254, "acc_stderr": 0.011764149054698346 } } ``` ### 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]

open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni

[ "region:us" ]

2023-09-14T07:40:07+00:00

{"pretty_name": "Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/ChatAYT-Lora-Assamble-Marcoroni](https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T13:33:47.797770](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__ChatAYT-Lora-Assamble-Marcoroni/blob/main/results_2023-10-24T13-33-47.797770.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\": 0.008598993288590604,\n \"em_stderr\": 0.0009455579144542189,\n \"f1\": 0.1045532718120813,\n \"f1_stderr\": 0.0020198084132137728,\n \"acc\": 0.43109211314448,\n \"acc_stderr\": 0.009797803895878525\n },\n \"harness|drop|3\": {\n \"em\": 0.008598993288590604,\n \"em_stderr\": 0.0009455579144542189,\n \"f1\": 0.1045532718120813,\n \"f1_stderr\": 0.0020198084132137728\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0887035633055345,\n \"acc_stderr\": 0.007831458737058703\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698346\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-24T12:34:02+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-24T13:33:47.797770(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:33:47.797770(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:33:47.797770(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 27, 31, 175, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/ChatAYT-Lora-Assamble-Marcoroni## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/ChatAYT-Lora-Assamble-Marcoroni on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T13:33:47.797770(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

d08b7f6e4fc13cc40cb2def7513d8e5932666ea2

# Dataset Card for "fabric_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rikdas/fabric_data

[ "region:us" ]

2023-09-14T07:40:30+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41259319.0, "num_examples": 20}], "download_size": 41261924, "dataset_size": 41259319.0}}

2023-09-14T07:40:34+00:00

[]

TAGS #region-us

# Dataset Card for "fabric_data" More Information needed

[ "# Dataset Card for \"fabric_data\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fabric_data\"\n\nMore Information needed" ]

[ 6, 14 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"fabric_data\"\n\nMore Information needed" ]

3fece45f78cb2b55bceca6722674c1ab46ba6ab0

# Dataset Card for "structs_token_size_4_reduced_labelled_eval" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the validation dataset. Train set at: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_train Data structures and labels are extracted from: https://zenodo.org/records/6537904 Thesis and associated scripts: https://zenodo.org/records/10053730

johannes-garstenauer/structs_token_size_4_reduced_labelled_eval

[ "digital forensics", "region:us" ]

2023-09-14T07:59:17+00:00

{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 65294030.35619895, "num_examples": 269087}], "download_size": 24102593, "dataset_size": 65294030.35619895}, "tags": ["digital forensics"]}

2023-10-30T13:26:35+00:00

[]

TAGS #digital forensics #region-us

# Dataset Card for "structs_token_size_4_reduced_labelled_eval" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the validation dataset. Train set at: URL Data structures and labels are extracted from: URL Thesis and associated scripts: URL

[ "# Dataset Card for \"structs_token_size_4_reduced_labelled_eval\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the validation dataset. Train set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]

[ "TAGS\n#digital forensics #region-us \n", "# Dataset Card for \"structs_token_size_4_reduced_labelled_eval\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the validation dataset. Train set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]

[ 11, 109 ]

[ "passage: TAGS\n#digital forensics #region-us \n# Dataset Card for \"structs_token_size_4_reduced_labelled_eval\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the validation dataset. Train set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]

23f26b80cb775bfea60decd22d8b368b15bda920

# Dataset of Shiraki Hime This is the dataset of Shiraki Hime, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 655 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 655 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 655 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 655 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/shiraki_hime_watashinoyuriwaoshigotodesu

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T08:02:22+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:36:56+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Shiraki Hime ======================= This is the dataset of Shiraki Hime, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

f5ca64b81f06f354c65a1e10fcde6f3bc69a55a8

# Dataset of kurosaki_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls) This is the dataset of kurosaki_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls), containing 328 images and their tags. The core tags of this character are `blonde_hair, long_hair, bangs, red_eyes, breasts, hair_between_eyes, hairband, very_long_hair, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 328 | 532.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 328 | 293.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 830 | 626.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 328 | 465.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 830 | 913.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_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/kurosaki_chitose_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 | 23 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, long_sleeves, looking_at_viewer, solo, open_mouth, white_shirt, black_hairband, :d, white_background, brooch, frills, simple_background, skirt, upper_body, blush, sleeves_past_wrists, thighhighs | | 1 | 18 | ![](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) | serafuku, 1girl, long_sleeves, red_neckerchief, shirt, looking_at_viewer, solo, white_sailor_collar, blush, pleated_skirt, black_skirt, black_hairband, open_cardigan, open_mouth, white_background, :d, simple_background | | 2 | 8 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, bare_shoulders, looking_at_viewer, white_dress, smile, solo, strapless_dress, wedding_dress, blush, bridal_veil, bride, cleavage, upper_body, collarbone, closed_mouth, holding_bouquet, necklace, red_rose, white_background | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, bare_shoulders, hair_flower, looking_at_viewer, medium_breasts, red_rose, solo, red_dress, smile, blush, cleavage, hair_intakes, wrist_cuffs, nail_polish, petals, red_nails, simple_background, strapless, upper_body, white_background | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, blush, looking_at_viewer, navel, solo, cleavage, collarbone, midriff, pink_shirt, groin, long_sleeves, open_mouth, pink_shorts, simple_background, sweat, white_background, :d, crop_top, heart, off_shoulder, pink_eyes, sleeves_past_wrists, stomach | | 5 | 17 | ![](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) | looking_at_viewer, smile, 1girl, blush, collarbone, navel, solo, cleavage, bikini, bare_shoulders, sitting, black_hairband, closed_mouth, thighs | | 6 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1boy, 1girl, blush, hetero, solo_focus, sweat, looking_at_viewer, nipples, penis, pov, smile, breast_grab, grabbing, mosaic_censoring, nude, open_mouth, paizuri, black_hairband, collarbone, male_pubic_hair, navel | | 7 | 8 | ![](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, fake_animal_ears, playboy_bunny, rabbit_ears, detached_collar, looking_at_viewer, solo, strapless_leotard, black_leotard, cleavage, smile, wrist_cuffs, bare_shoulders, blush, simple_background, thighhighs, white_background, black_hairband, bowtie, covered_navel, medium_breasts, nail_polish, pantyhose, rabbit_tail, red_nails | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | looking_at_viewer | solo | open_mouth | white_shirt | black_hairband | :d | white_background | brooch | frills | simple_background | skirt | upper_body | blush | sleeves_past_wrists | thighhighs | serafuku | red_neckerchief | shirt | white_sailor_collar | pleated_skirt | black_skirt | open_cardigan | bare_shoulders | white_dress | smile | strapless_dress | wedding_dress | bridal_veil | bride | cleavage | collarbone | closed_mouth | holding_bouquet | necklace | red_rose | hair_flower | medium_breasts | red_dress | hair_intakes | wrist_cuffs | nail_polish | petals | red_nails | strapless | navel | midriff | pink_shirt | groin | pink_shorts | sweat | crop_top | heart | off_shoulder | pink_eyes | stomach | bikini | sitting | thighs | 1boy | hetero | solo_focus | nipples | penis | pov | breast_grab | grabbing | mosaic_censoring | nude | paizuri | male_pubic_hair | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | strapless_leotard | black_leotard | bowtie | covered_navel | pantyhose | rabbit_tail | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:-------------|:--------------|:-----------------|:-----|:-------------------|:---------|:---------|:--------------------|:--------|:-------------|:--------|:----------------------|:-------------|:-----------|:------------------|:--------|:----------------------|:----------------|:--------------|:----------------|:-----------------|:--------------|:--------|:------------------|:----------------|:--------------|:--------|:-----------|:-------------|:---------------|:------------------|:-----------|:-----------|:--------------|:-----------------|:------------|:---------------|:--------------|:--------------|:---------|:------------|:------------|:--------|:----------|:-------------|:--------|:--------------|:--------|:-----------|:--------|:---------------|:------------|:----------|:---------|:----------|:---------|:-------|:---------|:-------------|:----------|:--------|:------|:--------------|:-----------|:-------------------|:-------|:----------|:------------------|:-------------------|:----------------|:--------------|:------------------|:--------------------|:----------------|:---------|:----------------|:------------|:--------------| | 0 | 23 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 18 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | | X | X | X | | | X | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | X | | | | | X | | | X | | X | X | | | | | | | | | | X | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | X | | | X | X | | | X | | | X | X | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 17 | ![](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 | 7 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | | X | | X | | X | | | | | | | | X | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 7 | 8 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | | X | X | | | X | | X | | | X | | | X | | X | | | | | | | | X | | X | | | | | X | | | | | | | X | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |

CyberHarem/kurosaki_chitose_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T08:02:34+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T19:14:50+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of kurosaki\_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls) ===================================================================== This is the dataset of kurosaki\_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls), containing 328 images and their tags. The core tags of this character are 'blonde\_hair, long\_hair, bangs, red\_eyes, breasts, hair\_between\_eyes, hairband, very\_long\_hair, large\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

4a499ac608ed3e37435d85f945bcf8707a3e23f7

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

strumber/objectDatasetLetsMOD

[ "region:us" ]

2023-09-14T08:09:37+00:00

{}

2023-09-14T14:33:06+00:00

[]

TAGS #region-us

# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

7e8533a9671f94a60f2ddcf3a02a5afb1e7395fe

# Dataset Card for "MedText-alpaca" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

maximegmd/MedText-alpaca

[ "region:us" ]

2023-09-14T08:22:46+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 949136, "num_examples": 1412}], "download_size": 494828, "dataset_size": 949136}}

2023-09-14T08:23:08+00:00

[]

TAGS #region-us

# Dataset Card for "MedText-alpaca" More Information needed

[ "# Dataset Card for \"MedText-alpaca\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"MedText-alpaca\"\n\nMore Information needed" ]

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"MedText-alpaca\"\n\nMore Information needed" ]

f92c8a01fc750a81cebbf8cafcbfa53a96c87a0c

# Dataset of Yano Mitsuki This is the dataset of Yano Mitsuki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 643 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 643 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 643 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 643 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/yano_mitsuki_watashinoyuriwaoshigotodesu

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T08:25:36+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:01+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Yano Mitsuki ======================= This is the dataset of Yano Mitsuki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

762eb77131a095a55f8bc163ba2e211c90ae424c

# Dataset Card for "celeba" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Diego1234/celeba

[ "region:us" ]

2023-09-14T08:46:43+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "female", "1": "male"}}}}], "splits": [{"name": "train", "num_bytes": 2768237832.0, "num_examples": 28000}, {"name": "validation", "num_bytes": 194932418.0, "num_examples": 2000}], "download_size": 2963322017, "dataset_size": 2963170250.0}}

2023-09-19T10:49:37+00:00

[]

TAGS #region-us

# Dataset Card for "celeba" More Information needed

[ "# Dataset Card for \"celeba\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"celeba\"\n\nMore Information needed" ]

[ 6, 12 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"celeba\"\n\nMore Information needed" ]

863d412c86a8b835beec63aa18d24e7e045af3d5

# Dataset of Mamiya Kanoko This is the dataset of Mamiya Kanoko, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 672 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 672 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 672 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 672 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/mamiya_kanoko_watashinoyuriwaoshigotodesu

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T08:48:48+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:03+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Mamiya Kanoko ======================== This is the dataset of Mamiya Kanoko, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

ce9f1350c8f11103a45cd50c63992fcff97d1923

# Dataset Card for "fine-tune-hebrew-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sabuhi1997/fine-tune-hebrew-dataset

[ "region:us" ]

2023-09-14T08:56:06+00:00

{"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": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train", "2": "validation"}}}}], "splits": [{"name": "train", "num_bytes": 5714802.0, "num_examples": 8}, {"name": "validation", "num_bytes": 1759819.0, "num_examples": 3}, {"name": "test", "num_bytes": 1625529.0, "num_examples": 4}], "download_size": 7719156, "dataset_size": 9100150.0}}

2023-09-14T09:40:44+00:00

[]

TAGS #region-us

# Dataset Card for "fine-tune-hebrew-dataset" More Information needed

[ "# Dataset Card for \"fine-tune-hebrew-dataset\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fine-tune-hebrew-dataset\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"fine-tune-hebrew-dataset\"\n\nMore Information needed" ]

9a557b2e65d5b3b3afd6e0524273130cbb69ecfd

# 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 |

CyberHarem/sajou_yukimi_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T08:56:59+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T15:00:58+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

8165669c49a748c91736e00cb31da3412ac7edc6

# Dataset Card for "l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

johannes-garstenauer/l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3

[ "region:us" ]

2023-09-14T08:57:29+00:00

{"dataset_info": {"features": [{"name": "last_cls", "sequence": "float32"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1542000, "num_examples": 500}], "download_size": 2136798, "dataset_size": 1542000}}

2023-09-14T08:57:36+00:00

[]

TAGS #region-us

# Dataset Card for "l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3" More Information needed

[ "# Dataset Card for \"l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3\"\n\nMore Information needed" ]

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"l_cls_labelled_from_distilbert_seqclass_pretrain_pad_3\"\n\nMore Information needed" ]

998d048d80c67c26ad0dc2c4ffc41df5d3111545

# Dataset of Chibana Sumika This is the dataset of Chibana Sumika, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 681 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 681 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 681 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 681 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/chibana_sumika_watashinoyuriwaoshigotodesu

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T09:14:14+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:07+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Chibana Sumika ========================= This is the dataset of Chibana Sumika, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

9836639743110569310f492b56bf3d68543268cb

# Dataset of Shiina Mashiro This is the dataset of Shiina Mashiro, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 688 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 688 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 688 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 688 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/shiina_mashiro_sakurasounopetnakanojo

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T09:24:17+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:09+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Shiina Mashiro ========================= This is the dataset of Shiina Mashiro, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

2ba0483f840e4a891ff0d9cf07e80b07d6a50ce6

# Dataset of Koshiba Mai This is the dataset of Koshiba Mai, containing 220 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 220 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 507 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 220 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 220 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 220 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 220 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 220 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 507 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 507 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 507 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/koshiba_mai_watashinoyuriwaoshigotodesu

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T09:29:50+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:11+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Koshiba Mai ====================== This is the dataset of Koshiba Mai, containing 220 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

2750339c06c47074a37fde4d6b585fb12349fca5

# Dataset Card for "fine-tune-hebrew-dataset-2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sabuhi1997/fine-tune-hebrew-dataset-2

[ "region:us" ]

2023-09-14T09:51:15+00:00

{"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": "audio", "dtype": "audio"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5715697.0, "num_examples": 8}, {"name": "validation", "num_bytes": 1760186.0, "num_examples": 3}, {"name": "test", "num_bytes": 1625785.0, "num_examples": 4}], "download_size": 3211475, "dataset_size": 9101668.0}}

2023-09-14T09:59:14+00:00

[]

TAGS #region-us

# Dataset Card for "fine-tune-hebrew-dataset-2" More Information needed

[ "# Dataset Card for \"fine-tune-hebrew-dataset-2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"fine-tune-hebrew-dataset-2\"\n\nMore Information needed" ]

[ 6, 20 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"fine-tune-hebrew-dataset-2\"\n\nMore Information needed" ]

7061eadbc707c7aa275a9cfb85a8c9ce6ac5318a

# Dataset of hayasaka_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls) This is the dataset of hayasaka_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls), containing 374 images and their tags. The core tags of this character are `eyepatch, purple_hair, multicolored_hair, brown_eyes, short_hair, red_hair, streaked_hair, fang, 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 | 374 | 325.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 374 | 234.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 802 | 461.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 374 | 305.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 802 | 584.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hayasaka_mirei_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/hayasaka_mirei_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 | 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, looking_at_viewer, solo, open_mouth, simple_background, upper_body, white_background, :d, blush, claw_pose, collarbone, green_jacket, heart, long_sleeves, black_shirt, choker, horns | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, cat_hood, heart, black_shirt, looking_at_viewer, red_jacket, solo, blush, hood_up, belt_buckle, belt_collar, crescent, open_mouth, simple_background, white_background, black_belt, black_collar, claws, upper_body, short_over_long_sleeves, button_badge, collarbone, skirt, smile | | 2 | 8 | ![](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, belt, cat_hood, hoodie, skirt, solo, blush, collar, striped_thighhighs, heart, claws, open_mouth, smile, white_background | | 3 | 12 | ![](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, navel, solo, bikini_top_only, midriff, belt, claws, horns, skirt, chain, collar, looking_at_viewer, cleavage, hoodie, smile | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | 1girl, fingerless_gloves, horns, looking_at_viewer, nail_polish, solo, belt, navel, open_mouth, skirt, smile, thighhighs, collar, microphone, midriff, chain, headphones | | 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) | blush, looking_at_viewer, navel, open_mouth, solo, 1girl, flat_chest, side-tie_bikini_bottom, simple_background, small_breasts, white_background, white_bikini, black_bikini, black_hair, collarbone, groin, hair_over_one_eye, horns, standing, sweat | | 6 | 14 | ![](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) | blush, white_shirt, 1girl, pleated_skirt, solo, long_sleeves, collared_shirt, grey_skirt, simple_background, striped_bow, dress_shirt, heart, looking_at_viewer, white_background, bangs, black_thighhighs, open_mouth, school_uniform, blue_jacket, hood_down, :d, black_jacket, hooded_jacket, open_jacket, backpack, blue_bowtie | | 7 | 13 | ![](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) | short_sleeves, looking_at_viewer, collared_shirt, hair_ornament, open_mouth, :d, fake_horns, plaid_skirt, solo_focus, two-tone_hair, 1girl, bangs, layered_skirt, pointing, purple_skirt, red_necktie, thighhighs, v-shaped_eyebrows, black_belt, blush, 2girls, belt_buckle, checkered_clothes | | 8 | 10 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | 1girl, solo, looking_at_viewer, blush, cat_ears, open_mouth, shoulder_cutout, wrist_cuffs, black_necktie, garter_straps, hairband, maid_headdress, short_sleeves, waist_apron, white_apron, fake_animal_ears, frilled_apron, heart, two-tone_hair, button_badge, red_dress, shirt, simple_background, smile, striped_thighhighs, white_background | | 9 | 5 | ![](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) | halo, maid_headdress, open_mouth, twintails, 1girl, plaid_skirt, wrist_cuffs, :d, angel_wings, looking_at_viewer, simple_background, sleeveless, solo, white_sailor_collar, blush, claw_pose, collarbone, feathered_wings, frills, midriff, multiple_girls, pink_skirt, ribbon, shirt, swimsuit, white_background, white_bow | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | open_mouth | simple_background | upper_body | white_background | :d | blush | claw_pose | collarbone | green_jacket | heart | long_sleeves | black_shirt | choker | horns | cat_hood | red_jacket | hood_up | belt_buckle | belt_collar | crescent | black_belt | black_collar | claws | short_over_long_sleeves | button_badge | skirt | smile | belt | hoodie | collar | striped_thighhighs | navel | bikini_top_only | midriff | chain | cleavage | fingerless_gloves | nail_polish | thighhighs | microphone | headphones | flat_chest | side-tie_bikini_bottom | small_breasts | white_bikini | black_bikini | black_hair | groin | hair_over_one_eye | standing | sweat | white_shirt | pleated_skirt | collared_shirt | grey_skirt | striped_bow | dress_shirt | bangs | black_thighhighs | school_uniform | blue_jacket | hood_down | black_jacket | hooded_jacket | open_jacket | backpack | blue_bowtie | short_sleeves | hair_ornament | fake_horns | plaid_skirt | solo_focus | two-tone_hair | layered_skirt | pointing | purple_skirt | red_necktie | v-shaped_eyebrows | 2girls | checkered_clothes | cat_ears | shoulder_cutout | wrist_cuffs | black_necktie | garter_straps | hairband | maid_headdress | waist_apron | white_apron | fake_animal_ears | frilled_apron | red_dress | shirt | halo | twintails | angel_wings | sleeveless | white_sailor_collar | feathered_wings | frills | multiple_girls | pink_skirt | ribbon | swimsuit | white_bow | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-------------|:--------------------|:-------------|:-------------------|:-----|:--------|:------------|:-------------|:---------------|:--------|:---------------|:--------------|:---------|:--------|:-----------|:-------------|:----------|:--------------|:--------------|:-----------|:-------------|:---------------|:--------|:--------------------------|:---------------|:--------|:--------|:-------|:---------|:---------|:---------------------|:--------|:------------------|:----------|:--------|:-----------|:--------------------|:--------------|:-------------|:-------------|:-------------|:-------------|:-------------------------|:----------------|:---------------|:---------------|:-------------|:--------|:--------------------|:-----------|:--------|:--------------|:----------------|:-----------------|:-------------|:--------------|:--------------|:--------|:-------------------|:-----------------|:--------------|:------------|:---------------|:----------------|:--------------|:-----------|:--------------|:----------------|:----------------|:-------------|:--------------|:-------------|:----------------|:----------------|:-----------|:---------------|:--------------|:--------------------|:---------|:--------------------|:-----------|:------------------|:--------------|:----------------|:----------------|:-----------|:-----------------|:--------------|:--------------|:-------------------|:----------------|:------------|:--------|:-------|:------------|:--------------|:-------------|:----------------------|:------------------|:---------|:-----------------|:-------------|:---------|:-----------|:------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | | X | | X | | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](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 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | | X | | X | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | X | | X | | X | | X | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 14 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 13 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | | X | | | | X | X | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 10 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | X | X | X | | X | | X | | | | X | | | | | | | | | | | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 9 | 5 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/hayasaka_mirei_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T09:52:36+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:02:01+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of hayasaka\_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of hayasaka\_mirei/早坂美玲 (THE iDOLM@STER: Cinderella Girls), containing 374 images and their tags. The core tags of this character are 'eyepatch, purple\_hair, multicolored\_hair, brown\_eyes, short\_hair, red\_hair, streaked\_hair, fang, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

a12907a46c0e981583a17026378758e97e516b26

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

mindchain/demo_25

[ "region:us" ]

2023-09-14T09:53:18+00:00

{}

2023-09-24T10:59:44+00:00

[]

TAGS #region-us

# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 8, 24, 32, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

6d49d849d293f37df562a8a5d4780719cd8a97a5

# Dataset Card for "messi-ronaldo-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

pranjal0198/messi-ronaldo-dataset

[ "task_categories:text-to-image", "region:us" ]

2023-09-14T09:55:32+00:00

{"task_categories": ["text-to-image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 61573451, "num_examples": 136}], "download_size": 0, "dataset_size": 61573451}}

2023-10-16T11:48:03+00:00

[]

TAGS #task_categories-text-to-image #region-us

# Dataset Card for "messi-ronaldo-dataset" More Information needed

[ "# Dataset Card for \"messi-ronaldo-dataset\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-text-to-image #region-us \n", "# Dataset Card for \"messi-ronaldo-dataset\"\n\nMore Information needed" ]

[ 18, 18 ]

[ "passage: TAGS\n#task_categories-text-to-image #region-us \n# Dataset Card for \"messi-ronaldo-dataset\"\n\nMore Information needed" ]

6569bdd801e8a83b4a496ea4833173caf076cdfd

# Dataset of Aoyama Nanami This is the dataset of Aoyama Nanami, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 709 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 709 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 709 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 709 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/aoyama_nanami_sakurasounopetnakanojo

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T09:55:35+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:15+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Aoyama Nanami ======================== This is the dataset of Aoyama Nanami, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

38c359b169454d06d4801df316e5098aea41f0c8

# Dataset Card for "donut_vqa_ISynHMP" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

warshakhan/donut_vqa_ISynHMP

[ "task_categories:visual-question-answering", "language:en", "license:unknown", "medical", " prescriptions", "region:us" ]

2023-09-14T10:10:50+00:00

{"language": ["en"], "license": "unknown", "task_categories": ["visual-question-answering"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 578804498, "num_examples": 2800}, {"name": "valid", "num_bytes": 85350687, "num_examples": 400}, {"name": "test", "num_bytes": 172300907, "num_examples": 800}], "download_size": 804418576, "dataset_size": 836456092}, "tags": ["medical", " prescriptions"]}

2023-09-15T06:12:51+00:00

[]

[ "en" ]

TAGS #task_categories-visual-question-answering #language-English #license-unknown #medical # prescriptions #region-us

# Dataset Card for "donut_vqa_ISynHMP" More Information needed

[ "# Dataset Card for \"donut_vqa_ISynHMP\"\n\nMore Information needed" ]

[ "TAGS\n#task_categories-visual-question-answering #language-English #license-unknown #medical # prescriptions #region-us \n", "# Dataset Card for \"donut_vqa_ISynHMP\"\n\nMore Information needed" ]

[ 39, 20 ]

[ "passage: TAGS\n#task_categories-visual-question-answering #language-English #license-unknown #medical # prescriptions #region-us \n# Dataset Card for \"donut_vqa_ISynHMP\"\n\nMore Information needed" ]

7e7074252d0cf9cb9d5cf52e3f936a643581453d

# Dataset Card for "args_me_sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

nailiamirzakhmedova/args_me_10k

[ "region:us" ]

2023-09-14T10:26:41+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "argument", "dtype": "string"}, {"name": "stance", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12851963, "num_examples": 10000}], "download_size": 7839623, "dataset_size": 12851963}}

2023-09-18T11:36:19+00:00

[]

TAGS #region-us

# Dataset Card for "args_me_sampled" More Information needed

[ "# Dataset Card for \"args_me_sampled\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"args_me_sampled\"\n\nMore Information needed" ]

[ 6, 18 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"args_me_sampled\"\n\nMore Information needed" ]

e1f8ee836ef59a8116aac9bc34b9ab43e3939654

# Dataset of Kamiigusa Misaki This is the dataset of Kamiigusa Misaki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 725 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 725 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 725 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 725 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/kamiigusa_misaki_sakurasounopetnakanojo

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T10:30:23+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:17+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Kamiigusa Misaki =========================== This is the dataset of Kamiigusa Misaki, containing 300 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

ab49146f3c8452a43af41721e4f969abe85be546

# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-70B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 61 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 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_oh-yeontaek__llama-2-70B-LoRA-assemble", "harness_truthfulqa_mc_0", split="train") ``` ## Latest results These are the [latest results from run 2023-09-14T11:41:03.022396](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble/blob/main/results_2023-09-14T11-41-03.022396.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": 0.6934330265245879, "acc_stderr": 0.031312838620430335, "acc_norm": 0.697335554746802, "acc_norm_stderr": 0.03128337547678218, "mc1": 0.46511627906976744, "mc1_stderr": 0.01746084997587397, "mc2": 0.6479539766332348, "mc2_stderr": 0.014916593992436448 }, "harness|arc:challenge|25": { "acc": 0.6851535836177475, "acc_stderr": 0.01357265770308495, "acc_norm": 0.7184300341296929, "acc_norm_stderr": 0.013143376735009022 }, "harness|hellaswag|10": { "acc": 0.6707827126070504, "acc_stderr": 0.00468968597815517, "acc_norm": 0.867755427205736, "acc_norm_stderr": 0.0033806414709899157 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6222222222222222, "acc_stderr": 0.04188307537595852, "acc_norm": 0.6222222222222222, "acc_norm_stderr": 0.04188307537595852 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7763157894736842, "acc_stderr": 0.03391160934343603, "acc_norm": 0.7763157894736842, "acc_norm_stderr": 0.03391160934343603 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.73, "acc_stderr": 0.04461960433384741, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384741 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7358490566037735, "acc_stderr": 0.027134291628741702, "acc_norm": 0.7358490566037735, "acc_norm_stderr": 0.027134291628741702 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8125, "acc_stderr": 0.032639560491693344, "acc_norm": 0.8125, "acc_norm_stderr": 0.032639560491693344 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.62, "acc_stderr": 0.04878317312145632, "acc_norm": 0.62, "acc_norm_stderr": 0.04878317312145632 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001974, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001974 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6705202312138728, "acc_stderr": 0.03583901754736412, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.03583901754736412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.04690650298201943, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.04690650298201943 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.73, "acc_stderr": 0.04461960433384739, "acc_norm": 0.73, "acc_norm_stderr": 0.04461960433384739 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6638297872340425, "acc_stderr": 0.030881618520676942, "acc_norm": 0.6638297872340425, "acc_norm_stderr": 0.030881618520676942 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.42105263157894735, "acc_stderr": 0.046446020912223177, "acc_norm": 0.42105263157894735, "acc_norm_stderr": 0.046446020912223177 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6275862068965518, "acc_stderr": 0.040287315329475576, "acc_norm": 0.6275862068965518, "acc_norm_stderr": 0.040287315329475576 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.4656084656084656, "acc_stderr": 0.025690321762493844, "acc_norm": 0.4656084656084656, "acc_norm_stderr": 0.025690321762493844 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4523809523809524, "acc_stderr": 0.044518079590553275, "acc_norm": 0.4523809523809524, "acc_norm_stderr": 0.044518079590553275 }, "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": 0.8290322580645161, "acc_stderr": 0.021417242936321582, "acc_norm": 0.8290322580645161, "acc_norm_stderr": 0.021417242936321582 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5320197044334976, "acc_stderr": 0.035107665979592154, "acc_norm": 0.5320197044334976, "acc_norm_stderr": 0.035107665979592154 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.77, "acc_stderr": 0.04229525846816506, "acc_norm": 0.77, "acc_norm_stderr": 0.04229525846816506 }, "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": 0.8939393939393939, "acc_stderr": 0.021938047738853113, "acc_norm": 0.8939393939393939, "acc_norm_stderr": 0.021938047738853113 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.927461139896373, "acc_stderr": 0.018718998520678178, "acc_norm": 0.927461139896373, "acc_norm_stderr": 0.018718998520678178 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6948717948717948, "acc_stderr": 0.023346335293325887, "acc_norm": 0.6948717948717948, "acc_norm_stderr": 0.023346335293325887 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.31851851851851853, "acc_stderr": 0.02840653309060846, "acc_norm": 0.31851851851851853, "acc_norm_stderr": 0.02840653309060846 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.773109243697479, "acc_stderr": 0.02720537153827947, "acc_norm": 0.773109243697479, "acc_norm_stderr": 0.02720537153827947 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.4900662251655629, "acc_stderr": 0.04081677107248436, "acc_norm": 0.4900662251655629, "acc_norm_stderr": 0.04081677107248436 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8862385321100917, "acc_stderr": 0.013613614800232805, "acc_norm": 0.8862385321100917, "acc_norm_stderr": 0.013613614800232805 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5740740740740741, "acc_stderr": 0.033723432716530624, "acc_norm": 0.5740740740740741, "acc_norm_stderr": 0.033723432716530624 }, "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": 0.7757847533632287, "acc_stderr": 0.02799153425851952, "acc_norm": 0.7757847533632287, "acc_norm_stderr": 0.02799153425851952 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8396946564885496, "acc_stderr": 0.03217829420744633, "acc_norm": 0.8396946564885496, "acc_norm_stderr": 0.03217829420744633 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8512396694214877, "acc_stderr": 0.03248470083807194, "acc_norm": 0.8512396694214877, "acc_norm_stderr": 0.03248470083807194 }, "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": 0.8220858895705522, "acc_stderr": 0.03004735765580663, "acc_norm": 0.8220858895705522, "acc_norm_stderr": 0.03004735765580663 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4732142857142857, "acc_stderr": 0.047389751192741546, "acc_norm": 0.4732142857142857, "acc_norm_stderr": 0.047389751192741546 }, "harness|hendrycksTest-management|5": { "acc": 0.8252427184466019, "acc_stderr": 0.03760178006026621, "acc_norm": 0.8252427184466019, "acc_norm_stderr": 0.03760178006026621 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8888888888888888, "acc_stderr": 0.020588491316092375, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.020588491316092375 }, "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": 0.8633461047254151, "acc_stderr": 0.012282876868629234, "acc_norm": 0.8633461047254151, "acc_norm_stderr": 0.012282876868629234 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7716763005780347, "acc_stderr": 0.022598703804321635, "acc_norm": 0.7716763005780347, "acc_norm_stderr": 0.022598703804321635 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.5743016759776536, "acc_stderr": 0.01653682964899712, "acc_norm": 0.5743016759776536, "acc_norm_stderr": 0.01653682964899712 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.738562091503268, "acc_stderr": 0.025160998214292456, "acc_norm": 0.738562091503268, "acc_norm_stderr": 0.025160998214292456 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7556270096463023, "acc_stderr": 0.024406162094668907, "acc_norm": 0.7556270096463023, "acc_norm_stderr": 0.024406162094668907 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7993827160493827, "acc_stderr": 0.02228231394977488, "acc_norm": 0.7993827160493827, "acc_norm_stderr": 0.02228231394977488 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5567375886524822, "acc_stderr": 0.029634838473766006, "acc_norm": 0.5567375886524822, "acc_norm_stderr": 0.029634838473766006 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5645371577574967, "acc_stderr": 0.012663412101248345, "acc_norm": 0.5645371577574967, "acc_norm_stderr": 0.012663412101248345 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.7426470588235294, "acc_stderr": 0.0265565194700415, "acc_norm": 0.7426470588235294, "acc_norm_stderr": 0.0265565194700415 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7418300653594772, "acc_stderr": 0.017704531653250078, "acc_norm": 0.7418300653594772, "acc_norm_stderr": 0.017704531653250078 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7545454545454545, "acc_stderr": 0.041220665028782855, "acc_norm": 0.7545454545454545, "acc_norm_stderr": 0.041220665028782855 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7877551020408163, "acc_stderr": 0.026176967197866764, "acc_norm": 0.7877551020408163, "acc_norm_stderr": 0.026176967197866764 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8805970149253731, "acc_stderr": 0.02292879327721974, "acc_norm": 0.8805970149253731, "acc_norm_stderr": 0.02292879327721974 }, "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": 0.5240963855421686, "acc_stderr": 0.03887971849597264, "acc_norm": 0.5240963855421686, "acc_norm_stderr": 0.03887971849597264 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8654970760233918, "acc_stderr": 0.026168221344662297, "acc_norm": 0.8654970760233918, "acc_norm_stderr": 0.026168221344662297 }, "harness|truthfulqa:mc|0": { "mc1": 0.46511627906976744, "mc1_stderr": 0.01746084997587397, "mc2": 0.6479539766332348, "mc2_stderr": 0.014916593992436448 } } ``` ### 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]

open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble

[ "region:us" ]

2023-09-14T10:41:18+00:00

{"pretty_name": "Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble", "dataset_summary": "Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-70B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-70B-LoRA-assemble) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-14T11:41:03.022396](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-70B-LoRA-assemble/blob/main/results_2023-09-14T11-41-03.022396.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\": 0.6934330265245879,\n \"acc_stderr\": 0.031312838620430335,\n \"acc_norm\": 0.697335554746802,\n \"acc_norm_stderr\": 0.03128337547678218,\n \"mc1\": 0.46511627906976744,\n \"mc1_stderr\": 0.01746084997587397,\n \"mc2\": 0.6479539766332348,\n \"mc2_stderr\": 0.014916593992436448\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6851535836177475,\n \"acc_stderr\": 0.01357265770308495,\n \"acc_norm\": 0.7184300341296929,\n \"acc_norm_stderr\": 0.013143376735009022\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6707827126070504,\n \"acc_stderr\": 0.00468968597815517,\n \"acc_norm\": 0.867755427205736,\n \"acc_norm_stderr\": 0.0033806414709899157\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n \"acc_norm_stderr\": 0.04188307537595852\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7763157894736842,\n \"acc_stderr\": 0.03391160934343603,\n \"acc_norm\": 0.7763157894736842,\n \"acc_norm_stderr\": 0.03391160934343603\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.027134291628741702,\n \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.027134291628741702\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.032639560491693344,\n \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.032639560491693344\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6638297872340425,\n \"acc_stderr\": 0.030881618520676942,\n \"acc_norm\": 0.6638297872340425,\n \"acc_norm_stderr\": 0.030881618520676942\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.046446020912223177,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.046446020912223177\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.040287315329475576,\n \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.040287315329475576\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4656084656084656,\n \"acc_stderr\": 0.025690321762493844,\n \"acc_norm\": 0.4656084656084656,\n \"acc_norm_stderr\": 0.025690321762493844\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.044518079590553275\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\": 0.8290322580645161,\n \"acc_stderr\": 0.021417242936321582,\n \"acc_norm\": 0.8290322580645161,\n \"acc_norm_stderr\": 0.021417242936321582\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\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\": 0.8939393939393939,\n \"acc_stderr\": 0.021938047738853113,\n \"acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.021938047738853113\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.927461139896373,\n \"acc_stderr\": 0.018718998520678178,\n \"acc_norm\": 0.927461139896373,\n \"acc_norm_stderr\": 0.018718998520678178\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6948717948717948,\n \"acc_stderr\": 0.023346335293325887,\n \"acc_norm\": 0.6948717948717948,\n \"acc_norm_stderr\": 0.023346335293325887\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.31851851851851853,\n \"acc_stderr\": 0.02840653309060846,\n \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.02840653309060846\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.773109243697479,\n \"acc_stderr\": 0.02720537153827947,\n \"acc_norm\": 0.773109243697479,\n \"acc_norm_stderr\": 0.02720537153827947\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8862385321100917,\n \"acc_stderr\": 0.013613614800232805,\n \"acc_norm\": 0.8862385321100917,\n \"acc_norm_stderr\": 0.013613614800232805\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.033723432716530624,\n \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.033723432716530624\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\": 0.7757847533632287,\n \"acc_stderr\": 0.02799153425851952,\n \"acc_norm\": 0.7757847533632287,\n \"acc_norm_stderr\": 0.02799153425851952\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8396946564885496,\n \"acc_stderr\": 0.03217829420744633,\n \"acc_norm\": 0.8396946564885496,\n \"acc_norm_stderr\": 0.03217829420744633\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8512396694214877,\n \"acc_stderr\": 0.03248470083807194,\n \"acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.03248470083807194\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 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[]

TAGS #region-us

# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard. The dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-09-14T11:41:03.022396(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T11:41:03.022396(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-09-14T11:41:03.022396(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 25, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of oh-yeontaek/llama-2-70B-LoRA-assemble## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-70B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-14T11:41:03.022396(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

3f714e7148503f9ab51debc388bb664b93222cd8

# Dataset of yorita_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls) This is the dataset of yorita_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls), containing 465 images and their tags. The core tags of this character are `brown_hair, long_hair, brown_eyes, bangs, bow, very_long_hair, hair_bow, ponytail`, 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 | 465 | 614.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 465 | 368.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1111 | 777.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 465 | 548.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1111 | 1.04 GiB | [Download](https://huggingface.co/datasets/CyberHarem/yorita_yoshino_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/yorita_yoshino_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 | 27 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, blush, floral_print, obi, smile, striped_kimono, wide_sleeves, white_background, upper_body, long_sleeves, ribbon, simple_background | | 1 | 22 | ![](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, detached_sleeves, looking_at_viewer, solo, smile, blush, kimono, wide_sleeves, hair_ornament, bare_shoulders, open_mouth, ribbon_trim, skirt, flower, white_background, white_thighhighs | | 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, hakama_skirt, looking_at_viewer, solo, blush, cherry_blossoms, floral_print, petals, pink_kimono, red_bow, wide_sleeves, long_sleeves, :d, flower, meiji_schoolgirl_uniform, open_mouth, outdoors, red_hakama | | 3 | 12 | ![](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, dress, solo, bare_shoulders, hair_flower, looking_at_viewer, smile, simple_background, blue_bow, blush, collarbone, white_background, bracelet, halterneck, high_ponytail, sidelocks | | 4 | 35 | ![](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, serafuku, solo, blush, pleated_skirt, long_sleeves, white_shirt, red_bow, smile, blue_skirt, blue_neckerchief, blue_sailor_collar, hair_ribbon, black_skirt, simple_background, white_background, parted_lips, red_ribbon | | 5 | 21 | ![](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, puffy_short_sleeves, solo, white_shirt, beret, blush, looking_at_viewer, brown_headwear, suspender_skirt, wrist_cuffs, brown_skirt, brown_bow, center_frills, smile, plaid_skirt, simple_background, white_background, collared_shirt, plaid_bow, petals, pleated_skirt | | 6 | 18 | ![](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, solo, collarbone, simple_background, navel, small_breasts, striped_bikini, blunt_bangs, jacket, white_background, shorts, smile, ribbon | | 7 | 8 | ![](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, looking_at_viewer, solo, maid_headdress, white_thighhighs, wrist_cuffs, blush, puffy_short_sleeves, frills, black_dress, breasts, detached_collar, sitting, :o, enmaided, hair_ribbon, low_ponytail, maid_apron, simple_background, underwear | | 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, black_leotard, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, strapless_leotard, wrist_cuffs, small_breasts, black_pantyhose, low_ponytail, red_bowtie, simple_background, smile, white_background, black_bowtie, sitting, thighband_pantyhose | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | 1boy, 1girl, blush, hetero, solo_focus, penis, nipples, sex, small_breasts, open_mouth, pussy, vaginal, kimono, lying, mosaic_censoring, nude, blunt_bangs, pubic_hair, spread_legs, sweat | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | blush | floral_print | obi | smile | striped_kimono | wide_sleeves | white_background | upper_body | long_sleeves | ribbon | simple_background | detached_sleeves | kimono | hair_ornament | bare_shoulders | open_mouth | ribbon_trim | skirt | flower | white_thighhighs | hakama_skirt | cherry_blossoms | petals | pink_kimono | red_bow | :d | meiji_schoolgirl_uniform | outdoors | red_hakama | dress | hair_flower | blue_bow | collarbone | bracelet | halterneck | high_ponytail | sidelocks | serafuku | pleated_skirt | white_shirt | blue_skirt | blue_neckerchief | blue_sailor_collar | hair_ribbon | black_skirt | parted_lips | red_ribbon | puffy_short_sleeves | beret | brown_headwear | suspender_skirt | wrist_cuffs | brown_skirt | brown_bow | center_frills | plaid_skirt | collared_shirt | plaid_bow | navel | small_breasts | striped_bikini | blunt_bangs | jacket | shorts | maid_headdress | frills | black_dress | breasts | detached_collar | sitting | :o | enmaided | low_ponytail | maid_apron | underwear | black_leotard | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | black_pantyhose | red_bowtie | black_bowtie | thighband_pantyhose | 1boy | hetero | solo_focus | penis | nipples | sex | pussy | vaginal | lying | mosaic_censoring | nude | pubic_hair | spread_legs | sweat | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:---------------|:------|:--------|:-----------------|:---------------|:-------------------|:-------------|:---------------|:---------|:--------------------|:-------------------|:---------|:----------------|:-----------------|:-------------|:--------------|:--------|:---------|:-------------------|:---------------|:------------------|:---------|:--------------|:----------|:-----|:---------------------------|:-----------|:-------------|:--------|:--------------|:-----------|:-------------|:-----------|:-------------|:----------------|:------------|:-----------|:----------------|:--------------|:-------------|:-------------------|:---------------------|:--------------|:--------------|:--------------|:-------------|:----------------------|:--------|:-----------------|:------------------|:--------------|:--------------|:------------|:----------------|:--------------|:-----------------|:------------|:--------|:----------------|:-----------------|:--------------|:---------|:---------|:-----------------|:---------|:--------------|:----------|:------------------|:----------|:-----|:-----------|:---------------|:-------------|:------------|:----------------|:-------------------|:----------------|:--------------|:--------------------|:------------------|:-------------|:---------------|:----------------------|:-------|:---------|:-------------|:--------|:----------|:------|:--------|:----------|:--------|:-------------------|:-------|:-------------|:--------------|:--------| | 0 | 27 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 22 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 12 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 35 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 21 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | X | | | X | | | X | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 18 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | X | X | | | X | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 8 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | X | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 6 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | X | | | | X | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | X | X | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 9 | 9 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | | | X | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/yorita_yoshino_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T10:56:29+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T13:52:22+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of yorita\_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of yorita\_yoshino/依田芳乃 (THE iDOLM@STER: Cinderella Girls), containing 465 images and their tags. The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, bangs, bow, very\_long\_hair, hair\_bow, ponytail', 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

6f6e6725cd508aff37e74886ff59b7c506c956cf

# Dataset Card for "hatilsofa" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

sohug/hatilsofa

[ "region:us" ]

2023-09-14T10:59:56+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 549167.0, "num_examples": 11}], "download_size": 300783, "dataset_size": 549167.0}}

2023-09-14T11:03:27+00:00

[]

TAGS #region-us

# Dataset Card for "hatilsofa" More Information needed

[ "# Dataset Card for \"hatilsofa\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"hatilsofa\"\n\nMore Information needed" ]

[ 6, 13 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"hatilsofa\"\n\nMore Information needed" ]

785ee99ec40cd71a3c2fb764b1b46d3f4d133513

# Dataset of Sengoku Chihiro This is the dataset of Sengoku Chihiro, containing 71 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 71 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 170 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 71 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 71 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 71 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 71 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 71 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 170 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 170 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 170 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/sengoku_chihiro_sakurasounopetnakanojo

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T11:01:29+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:21+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Sengoku Chihiro ========================== This is the dataset of Sengoku Chihiro, containing 71 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

ed3323e631e7acbf5fe49c2fdfb156b591c476aa

# Dataset of mizumoto_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls) This is the dataset of mizumoto_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls), containing 369 images and their tags. The core tags of this character are `brown_hair, long_hair, brown_eyes, bangs, 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 | 369 | 356.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 369 | 240.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 763 | 462.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 369 | 331.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 763 | 600.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mizumoto_yukari_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/mizumoto_yukari_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 | 11 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, solo, looking_at_viewer, simple_background, short_sleeves, white_background, blue_skirt, collared_shirt, school_uniform, white_shirt, blush, open_mouth, :d, pleated_skirt, blue_bow, blue_ribbon, closed_mouth, neck_ribbon | | 1 | 8 | ![](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, school_uniform, skirt, smile, looking_at_viewer, solo, blush, flute | | 2 | 7 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, looking_at_viewer, open_mouth, smile, solo, dress, hair_ornament, necklace, blush, microphone, bare_shoulders, braid, bracelet, earrings, medium_breasts | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, looking_at_viewer, navel, smile, solo, medium_breasts, cleavage, sailor_bikini, white_bikini | | 4 | 15 | ![](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, competition_swimsuit, looking_at_viewer, medium_breasts, solo, cowboy_shot, red_one-piece_swimsuit, blush, collarbone, covered_navel, smile, white_background, simple_background, standing, pink_one-piece_swimsuit | | 5 | 6 | ![](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) | smile, strapless_dress, wedding_dress, 1girl, bare_shoulders, necklace, solo, bridal_veil, earrings, hair_flower, looking_at_viewer, white_dress, white_gloves, blush, bouquet, collarbone, holding, open_mouth | | 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) | 1boy, blush, hetero, penis, 1girl, mosaic_censoring, solo_focus, sweat, vaginal, nipples, open_mouth, pussy, white_shirt, long_sleeves, medium_breasts, clothed_sex, navel, skirt, spread_legs, heart, on_back, panties_aside, saliva, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | simple_background | short_sleeves | white_background | blue_skirt | collared_shirt | school_uniform | white_shirt | blush | open_mouth | :d | pleated_skirt | blue_bow | blue_ribbon | closed_mouth | neck_ribbon | skirt | smile | flute | dress | hair_ornament | necklace | microphone | bare_shoulders | braid | bracelet | earrings | medium_breasts | navel | cleavage | sailor_bikini | white_bikini | competition_swimsuit | cowboy_shot | red_one-piece_swimsuit | collarbone | covered_navel | standing | pink_one-piece_swimsuit | strapless_dress | wedding_dress | bridal_veil | hair_flower | white_dress | white_gloves | bouquet | holding | 1boy | hetero | penis | mosaic_censoring | solo_focus | sweat | vaginal | nipples | pussy | long_sleeves | clothed_sex | spread_legs | heart | on_back | panties_aside | saliva | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------------------|:----------------|:-------------------|:-------------|:-----------------|:-----------------|:--------------|:--------|:-------------|:-----|:----------------|:-----------|:--------------|:---------------|:--------------|:--------|:--------|:--------|:--------|:----------------|:-----------|:-------------|:-----------------|:--------|:-----------|:-----------|:-----------------|:--------|:-----------|:----------------|:---------------|:-----------------------|:--------------|:-------------------------|:-------------|:----------------|:-----------|:--------------------------|:------------------|:----------------|:--------------|:--------------|:--------------|:---------------|:----------|:----------|:-------|:---------|:--------|:-------------------|:-------------|:--------|:----------|:----------|:--------|:---------------|:--------------|:--------------|:--------|:----------|:----------------|:---------|:-------------| | 0 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | | | | | | | | X | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 15 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | | | | | | | | X | X | | | | | | | | X | | | | X | | X | | | X | | | | | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 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 |

CyberHarem/mizumoto_yukari_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T11:09:05+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:41:08+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of mizumoto\_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls) ==================================================================== This is the dataset of mizumoto\_yukari/水本ゆかり (THE iDOLM@STER: Cinderella Girls), containing 369 images and their tags. The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, bangs, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

555a1265868e260121f9385be2f121b6c520a211

# Dataset of Rita Ainsworth This is the dataset of Rita Ainsworth, containing 100 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). | Name | Images | Download | Description | |:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------| | raw | 100 | [Download](dataset-raw.zip) | Raw data with meta information. | | raw-stage3 | 229 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. | | 384x512 | 100 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. | | 512x512 | 100 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. | | 512x704 | 100 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. | | 640x640 | 100 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. | | 640x880 | 100 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. | | stage3-640 | 229 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. | | stage3-800 | 229 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. | | stage3-1200 | 229 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |

CyberHarem/rita_ainsworth_sakurasounopetnakanojo

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T11:13:55+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2023-09-17T16:37:25+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of Rita Ainsworth ========================= This is the dataset of Rita Ainsworth, containing 100 images and their tags. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n" ]

f41986eaa5b3080a6e76572279d7fadfdf354ca2

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [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]

acumplido/HF_DATASET_NAME

[ "region:us" ]

2023-09-14T11:22:31+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]}

2023-09-14T12:34:20+00:00

[]

TAGS #region-us

# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name", "## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:", "### Dataset Summary", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 8, 24, 6, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

8109b61ffa9ca499edf4ce21fa145521903909e5

# Bangumi Image Base of Edomae Elf This is the image base of bangumi Edomae Elf, we detected 16 characters, 1946 images in total. The full dataset is [here](all.zip). **Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview: | # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 | |:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------| | 0 | 658 | [Download](0/dataset.zip) | ![preview 1](0/preview_1.png) | ![preview 2](0/preview_2.png) | ![preview 3](0/preview_3.png) | ![preview 4](0/preview_4.png) | ![preview 5](0/preview_5.png) | ![preview 6](0/preview_6.png) | ![preview 7](0/preview_7.png) | ![preview 8](0/preview_8.png) | | 1 | 20 | [Download](1/dataset.zip) | ![preview 1](1/preview_1.png) | ![preview 2](1/preview_2.png) | ![preview 3](1/preview_3.png) | ![preview 4](1/preview_4.png) | ![preview 5](1/preview_5.png) | ![preview 6](1/preview_6.png) | ![preview 7](1/preview_7.png) | ![preview 8](1/preview_8.png) | | 2 | 40 | [Download](2/dataset.zip) | ![preview 1](2/preview_1.png) | ![preview 2](2/preview_2.png) | ![preview 3](2/preview_3.png) | ![preview 4](2/preview_4.png) | ![preview 5](2/preview_5.png) | ![preview 6](2/preview_6.png) | ![preview 7](2/preview_7.png) | ![preview 8](2/preview_8.png) | | 3 | 80 | [Download](3/dataset.zip) | ![preview 1](3/preview_1.png) | ![preview 2](3/preview_2.png) | ![preview 3](3/preview_3.png) | ![preview 4](3/preview_4.png) | ![preview 5](3/preview_5.png) | ![preview 6](3/preview_6.png) | ![preview 7](3/preview_7.png) | ![preview 8](3/preview_8.png) | | 4 | 10 | [Download](4/dataset.zip) | ![preview 1](4/preview_1.png) | ![preview 2](4/preview_2.png) | ![preview 3](4/preview_3.png) | ![preview 4](4/preview_4.png) | ![preview 5](4/preview_5.png) | ![preview 6](4/preview_6.png) | ![preview 7](4/preview_7.png) | ![preview 8](4/preview_8.png) | | 5 | 6 | [Download](5/dataset.zip) | ![preview 1](5/preview_1.png) | ![preview 2](5/preview_2.png) | ![preview 3](5/preview_3.png) | ![preview 4](5/preview_4.png) | ![preview 5](5/preview_5.png) | ![preview 6](5/preview_6.png) | N/A | N/A | | 6 | 734 | [Download](6/dataset.zip) | ![preview 1](6/preview_1.png) | ![preview 2](6/preview_2.png) | ![preview 3](6/preview_3.png) | ![preview 4](6/preview_4.png) | ![preview 5](6/preview_5.png) | ![preview 6](6/preview_6.png) | ![preview 7](6/preview_7.png) | ![preview 8](6/preview_8.png) | | 7 | 79 | [Download](7/dataset.zip) | ![preview 1](7/preview_1.png) | ![preview 2](7/preview_2.png) | ![preview 3](7/preview_3.png) | ![preview 4](7/preview_4.png) | ![preview 5](7/preview_5.png) | ![preview 6](7/preview_6.png) | ![preview 7](7/preview_7.png) | ![preview 8](7/preview_8.png) | | 8 | 14 | [Download](8/dataset.zip) | ![preview 1](8/preview_1.png) | ![preview 2](8/preview_2.png) | ![preview 3](8/preview_3.png) | ![preview 4](8/preview_4.png) | ![preview 5](8/preview_5.png) | ![preview 6](8/preview_6.png) | ![preview 7](8/preview_7.png) | ![preview 8](8/preview_8.png) | | 9 | 13 | [Download](9/dataset.zip) | ![preview 1](9/preview_1.png) | ![preview 2](9/preview_2.png) | ![preview 3](9/preview_3.png) | ![preview 4](9/preview_4.png) | ![preview 5](9/preview_5.png) | ![preview 6](9/preview_6.png) | ![preview 7](9/preview_7.png) | ![preview 8](9/preview_8.png) | | 10 | 94 | [Download](10/dataset.zip) | ![preview 1](10/preview_1.png) | ![preview 2](10/preview_2.png) | ![preview 3](10/preview_3.png) | ![preview 4](10/preview_4.png) | ![preview 5](10/preview_5.png) | ![preview 6](10/preview_6.png) | ![preview 7](10/preview_7.png) | ![preview 8](10/preview_8.png) | | 11 | 58 | [Download](11/dataset.zip) | ![preview 1](11/preview_1.png) | ![preview 2](11/preview_2.png) | ![preview 3](11/preview_3.png) | ![preview 4](11/preview_4.png) | ![preview 5](11/preview_5.png) | ![preview 6](11/preview_6.png) | ![preview 7](11/preview_7.png) | ![preview 8](11/preview_8.png) | | 12 | 32 | [Download](12/dataset.zip) | ![preview 1](12/preview_1.png) | ![preview 2](12/preview_2.png) | ![preview 3](12/preview_3.png) | ![preview 4](12/preview_4.png) | ![preview 5](12/preview_5.png) | ![preview 6](12/preview_6.png) | ![preview 7](12/preview_7.png) | ![preview 8](12/preview_8.png) | | 13 | 21 | [Download](13/dataset.zip) | ![preview 1](13/preview_1.png) | ![preview 2](13/preview_2.png) | ![preview 3](13/preview_3.png) | ![preview 4](13/preview_4.png) | ![preview 5](13/preview_5.png) | ![preview 6](13/preview_6.png) | ![preview 7](13/preview_7.png) | ![preview 8](13/preview_8.png) | | 14 | 11 | [Download](14/dataset.zip) | ![preview 1](14/preview_1.png) | ![preview 2](14/preview_2.png) | ![preview 3](14/preview_3.png) | ![preview 4](14/preview_4.png) | ![preview 5](14/preview_5.png) | ![preview 6](14/preview_6.png) | ![preview 7](14/preview_7.png) | ![preview 8](14/preview_8.png) | | noise | 76 | [Download](-1/dataset.zip) | ![preview 1](-1/preview_1.png) | ![preview 2](-1/preview_2.png) | ![preview 3](-1/preview_3.png) | ![preview 4](-1/preview_4.png) | ![preview 5](-1/preview_5.png) | ![preview 6](-1/preview_6.png) | ![preview 7](-1/preview_7.png) | ![preview 8](-1/preview_8.png) |

BangumiBase/edomaeelf

[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]

2023-09-14T11:37:02+00:00

{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}

2023-09-29T06:50:59+00:00

[]

TAGS #size_categories-1K<n<10K #license-mit #art #region-us

Bangumi Image Base of Edomae Elf ================================ This is the image base of bangumi Edomae Elf, we detected 16 characters, 1946 images in total. The full dataset is here. Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability). Here is the characters' preview:

[]

[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

[ 25 ]

[ "passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

495a68c6ec4c030effcf0f5580e353274a577d21

A RL environment called AirHockey for the Godot Game Engine. This environment was created with: https://github.com/edbeeching/godot_rl_agents ## Downloading the environment After installing Godot RL Agents, download the environment with: ``` gdrl.env_from_hub -r edbeeching/godot_rl_AirHockey ```

edbeeching/godot_rl_AirHockey

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

2023-09-14T11:42:36+00:00

{"library_name": "godot-rl", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games"]}

2024-01-07T09:46:34+00:00

[]

TAGS #deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us

A RL environment called AirHockey for the Godot Game Engine. This environment was created with: URL ## Downloading the environment After installing Godot RL Agents, download the environment with:

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ 32, 20 ]

[ "passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

1a8d171aa2c218743c4b474e3627939b6b549c2c

# Dataset Card for "pooling_net_embeddings_dim_16" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

johannes-garstenauer/pooling_net_embeddings_dim_16

[ "region:us" ]

2023-09-14T11:50:02+00:00

{"dataset_info": {"features": [{"name": "last_cls", "sequence": "float32"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3800, "num_examples": 50}], "download_size": 5640, "dataset_size": 3800}}

2023-09-14T11:50:04+00:00

[]

TAGS #region-us

# Dataset Card for "pooling_net_embeddings_dim_16" More Information needed

[ "# Dataset Card for \"pooling_net_embeddings_dim_16\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pooling_net_embeddings_dim_16\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"pooling_net_embeddings_dim_16\"\n\nMore Information needed" ]

35dc1964537f71a977092d10835bbfb7c8a5a09e

# 学習データセット OpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。このデータセットは、LINE社のInstruction Tuningに利用されている。

ToPo-ToPo/oasst1-89k-ja

[ "region:us" ]

2023-09-14T11:52:11+00:00

{}

2023-09-26T00:14:46+00:00

[]

TAGS #region-us

# 学習データセット OpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。このデータセットは、LINE社のInstruction Tuningに利用されている。

[ "# 学習データセット\nOpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。\nこのデータセットは、LINE社のInstruction Tuningに利用されている。" ]

[ "TAGS\n#region-us \n", "# 学習データセット\nOpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。\nこのデータセットは、LINE社のInstruction Tuningに利用されている。" ]

[ 6, 55 ]

[ "passage: TAGS\n#region-us \n# 学習データセット\nOpenAssistant/oasst1を日本語化したデータセットであるkunishou/oasst1-89k-jaをすべて利用した。\nこのデータセットは、LINE社のInstruction Tuningに利用されている。" ]

d223b6f631053d6b8a896c78a37ad49d94ed54b3

# Dataset Card for "prs-v2-sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

loubnabnl/prs-v2-sample

[ "region:us" ]

2023-09-14T11:55:08+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "pull_request.guid", "dtype": "string"}, {"name": "pull_request.code_review_events", "dtype": "string"}, {"name": "pull_request.events", "dtype": "string"}, {"name": "pull_request.issue_events", "dtype": "string"}, {"name": "bucket", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 201909231, "num_examples": 10000}], "download_size": 38860265, "dataset_size": 201909231}}

2023-09-14T11:55:12+00:00

[]

TAGS #region-us

# Dataset Card for "prs-v2-sample" More Information needed

[ "# Dataset Card for \"prs-v2-sample\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"prs-v2-sample\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"prs-v2-sample\"\n\nMore Information needed" ]

14bcf5d0ebd2ba94e2e286e3912f91cd21c5f07d

# Dataset Card for "structs_token_size_4_reduced_labelled_train" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the training dataset. Validation set at: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_eval Data structures and labels are extracted from: https://zenodo.org/records/6537904 Thesis and associated scripts: https://zenodo.org/records/10053730

johannes-garstenauer/structs_token_size_4_reduced_labelled_train

[ "region:us" ]

2023-09-14T12:12:00+00:00

{"dataset_info": {"features": [{"name": "struct", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 372362495.3356041, "num_examples": 1518855}], "download_size": 138213330, "dataset_size": 372362495.3356041}}

2023-10-30T13:26:23+00:00

[]

TAGS #region-us

# Dataset Card for "structs_token_size_4_reduced_labelled_train" Dataset created for thesis: "Generating Robust Representations of Structures in OpenSSH Heap Dumps" by Johannes Garstenauer. This dataset contains raw heap data structures along with their labels. This is the training dataset. Validation set at: URL Data structures and labels are extracted from: URL Thesis and associated scripts: URL

[ "# Dataset Card for \"structs_token_size_4_reduced_labelled_train\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the training dataset. Validation set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"structs_token_size_4_reduced_labelled_train\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the training dataset. Validation set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]

[ 6, 110 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"structs_token_size_4_reduced_labelled_train\"\n\nDataset created for thesis: \"Generating Robust Representations of\nStructures in OpenSSH Heap Dumps\" by Johannes Garstenauer.\n\nThis dataset contains raw heap data structures along with their labels. \n\nThis is the training dataset. Validation set at: URL\n\nData structures and labels are extracted from: URL\n\nThesis and associated scripts: URL" ]

67bc56c60548f0c764e7c7f83be8d10d1ec5b716

As of 2019.2.7

shaowenchen/wiki_zh

[ "region:us" ]

2023-09-14T12:35:14+00:00

{}

2023-09-15T04:43:18+00:00

[]

TAGS #region-us

As of 2019.2.7

[]

[ "TAGS\n#region-us \n" ]

[ 6 ]

[ "passage: TAGS\n#region-us \n" ]

8158a98771bae3d80213240e67d47901c328136f

# Dataset Card for "celeba_attr_feliu" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fformosa/celeba_attr_feliu

[ "region:us" ]

2023-09-14T12:35:38+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "attributes", "struct": [{"name": "5_o_Clock_Shadow", "dtype": "string"}, {"name": "Arched_Eyebrows", "dtype": "string"}, {"name": "Attractive", "dtype": "string"}, {"name": "Bags_Under_Eyes", "dtype": "string"}, {"name": "Bald", "dtype": "string"}, {"name": "Bangs", "dtype": "string"}, {"name": "Big_Lips", "dtype": "string"}, {"name": "Big_Nose", "dtype": "string"}, {"name": "Black_Hair", "dtype": "string"}, {"name": "Blond_Hair", "dtype": "string"}, {"name": "Blurry", "dtype": "string"}, {"name": "Brown_Hair", "dtype": "string"}, {"name": "Bushy_Eyebrows", "dtype": "string"}, {"name": "Chubby", "dtype": "string"}, {"name": "Double_Chin", "dtype": "string"}, {"name": "Eyeglasses", "dtype": "string"}, {"name": "Goatee", "dtype": "string"}, {"name": "Gray_Hair", "dtype": "string"}, {"name": "Heavy_Makeup", "dtype": "string"}, {"name": "High_Cheekbones", "dtype": "string"}, {"name": "Male", "dtype": "string"}, {"name": "Mouth_Slightly_Open", "dtype": "string"}, {"name": "Mustache", "dtype": "string"}, {"name": "Narrow_Eyes", "dtype": "string"}, {"name": "No_Beard", "dtype": "string"}, {"name": "Oval_Face", "dtype": "string"}, {"name": "Pale_Skin", "dtype": "string"}, {"name": "Pointy_Nose", "dtype": "string"}, {"name": "Receding_Hairline", "dtype": "string"}, {"name": "Rosy_Cheeks", "dtype": "string"}, {"name": "Sideburns", "dtype": "string"}, {"name": "Smiling", "dtype": "string"}, {"name": "Straight_Hair", "dtype": "string"}, {"name": "Wavy_Hair", "dtype": "string"}, {"name": "Wearing_Earrings", "dtype": "string"}, {"name": "Wearing_Hat", "dtype": "string"}, {"name": "Wearing_Lipstick", "dtype": "string"}, {"name": "Wearing_Necklace", "dtype": "string"}, {"name": "Wearing_Necktie", "dtype": "string"}, {"name": "Young", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1456173097.427, "num_examples": 202599}], "download_size": 1410042939, "dataset_size": 1456173097.427}}

2023-09-14T13:27:11+00:00

[]

TAGS #region-us

# Dataset Card for "celeba_attr_feliu" More Information needed

[ "# Dataset Card for \"celeba_attr_feliu\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"celeba_attr_feliu\"\n\nMore Information needed" ]

[ 6, 18 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"celeba_attr_feliu\"\n\nMore Information needed" ]

b2ace1d80cdf90d2a6437d4ba288e122768a1c80

# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/wei123602/llama2-13b-fintune2-4E - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-fintune2-4E](https://huggingface.co/wei123602/llama2-13b-fintune2-4E) 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_wei123602__llama2-13b-fintune2-4E", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-23T08:37:29.290046](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E/blob/main/results_2023-10-23T08-37-29.290046.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": 0.33913590604026844, "em_stderr": 0.004848223319148492, "f1": 0.3781501677852353, "f1_stderr": 0.004773695048987946, "acc": 0.42001695814855794, "acc_stderr": 0.01052750062588995 }, "harness|drop|3": { "em": 0.33913590604026844, "em_stderr": 0.004848223319148492, "f1": 0.3781501677852353, "f1_stderr": 0.004773695048987946 }, "harness|gsm8k|5": { "acc": 0.10917361637604246, "acc_stderr": 0.00859008930051116 }, "harness|winogrande|5": { "acc": 0.7308602999210734, "acc_stderr": 0.012464911951268738 } } ``` ### 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]

open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E

[ "region:us" ]

2023-09-14T12:46:07+00:00

{"pretty_name": "Evaluation run of wei123602/llama2-13b-fintune2-4E", "dataset_summary": "Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-fintune2-4E](https://huggingface.co/wei123602/llama2-13b-fintune2-4E) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T08:37:29.290046](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-fintune2-4E/blob/main/results_2023-10-23T08-37-29.290046.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\": 0.33913590604026844,\n \"em_stderr\": 0.004848223319148492,\n \"f1\": 0.3781501677852353,\n \"f1_stderr\": 0.004773695048987946,\n \"acc\": 0.42001695814855794,\n \"acc_stderr\": 0.01052750062588995\n },\n \"harness|drop|3\": {\n \"em\": 0.33913590604026844,\n \"em_stderr\": 0.004848223319148492,\n \"f1\": 0.3781501677852353,\n \"f1_stderr\": 0.004773695048987946\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10917361637604246,\n \"acc_stderr\": 0.00859008930051116\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7308602999210734,\n \"acc_stderr\": 0.012464911951268738\n }\n}\n```", "repo_url": "https://huggingface.co/wei123602/llama2-13b-fintune2-4E", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-23T07:37:42+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-23T08:37:29.290046(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T08:37:29.290046(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-23T08:37:29.290046(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 25, 31, 173, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of wei123602/llama2-13b-fintune2-4E## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-fintune2-4E on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T08:37:29.290046(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

701b1e0fde0ba57091540d5fdc8b77a33b491edf

# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST) 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_wei123602__llama2-13b-FINETUNE3_TEST", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-24T13:49:39.272665](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST/blob/main/results_2023-10-24T13-49-39.272665.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": 0.21885486577181207, "em_stderr": 0.004234319461313102, "f1": 0.2578481543624162, "f1_stderr": 0.004224663408638886, "acc": 0.45241934856534904, "acc_stderr": 0.010864913504975222 }, "harness|drop|3": { "em": 0.21885486577181207, "em_stderr": 0.004234319461313102, "f1": 0.2578481543624162, "f1_stderr": 0.004224663408638886 }, "harness|gsm8k|5": { "acc": 0.14556482183472327, "acc_stderr": 0.009714267797726266 }, "harness|winogrande|5": { "acc": 0.7592738752959748, "acc_stderr": 0.01201555921222418 } } ``` ### 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]

open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST

[ "region:us" ]

2023-09-14T12:48:42+00:00

{"pretty_name": "Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST", "dataset_summary": "Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T13:49:39.272665](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST/blob/main/results_2023-10-24T13-49-39.272665.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\": 0.21885486577181207,\n \"em_stderr\": 0.004234319461313102,\n \"f1\": 0.2578481543624162,\n \"f1_stderr\": 0.004224663408638886,\n \"acc\": 0.45241934856534904,\n \"acc_stderr\": 0.010864913504975222\n },\n \"harness|drop|3\": {\n \"em\": 0.21885486577181207,\n \"em_stderr\": 0.004234319461313102,\n \"f1\": 0.2578481543624162,\n \"f1_stderr\": 0.004224663408638886\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14556482183472327,\n \"acc_stderr\": 0.009714267797726266\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.01201555921222418\n }\n}\n```", "repo_url": "https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-24T12:49:53+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-24T13:49:39.272665(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:49:39.272665(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-24T13:49:39.272665(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 27, 31, 175, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T13:49:39.272665(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

c9086f8da87902e4a469e60d9ee64b8d1a25f4ae

# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 ## Dataset Description - **Homepage:** - **Repository:** https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2 - **Paper:** - **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard - **Point of Contact:** [email protected] ### Dataset Summary Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884, "acc": 0.4441419290522286, "acc_stderr": 0.010548755752104734 }, "harness|drop|3": { "em": 0.2633179530201342, "em_stderr": 0.004510450588757746, "f1": 0.3047556627516783, "f1_stderr": 0.004459334625484884 }, "harness|gsm8k|5": { "acc": 0.12585291887793784, "acc_stderr": 0.009136212598406319 }, "harness|winogrande|5": { "acc": 0.7624309392265194, "acc_stderr": 0.01196129890580315 } } ``` ### 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]

open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2

[ "region:us" ]

2023-09-14T12:51:50+00:00

{"pretty_name": "Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2", "dataset_summary": "Dataset automatically created during the evaluation run of model [wei123602/llama2-13b-FINETUNE3_TEST2](https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T09:53:17.709619](https://huggingface.co/datasets/open-llm-leaderboard/details_wei123602__llama2-13b-FINETUNE3_TEST2/blob/main/results_2023-10-28T09-53-17.709619.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\": 0.2633179530201342,\n \"em_stderr\": 0.004510450588757746,\n \"f1\": 0.3047556627516783,\n \"f1_stderr\": 0.004459334625484884,\n \"acc\": 0.4441419290522286,\n \"acc_stderr\": 0.010548755752104734\n },\n \"harness|drop|3\": {\n \"em\": 0.2633179530201342,\n \"em_stderr\": 0.004510450588757746,\n \"f1\": 0.3047556627516783,\n \"f1_stderr\": 0.004459334625484884\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12585291887793784,\n \"acc_stderr\": 0.009136212598406319\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.01196129890580315\n }\n}\n```", "repo_url": "https://huggingface.co/wei123602/llama2-13b-FINETUNE3_TEST2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_14T13_51_34.438102", "path": ["**/details_harness|arc:challenge|25_2023-09-14T13-51-34.438102.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-14T13-51-34.438102.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T06_56_58.916586", "path": ["**/details_harness|drop|3_2023-10-28T06-56-58.916586.parquet"]}, {"split": "2023_10_28T09_53_17.709619", "path": ["**/details_harness|drop|3_2023-10-28T09-53-17.709619.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T09-53-17.709619.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T06_56_58.916586", "path": ["**/details_harness|gsm8k|5_2023-10-28T06-56-58.916586.parquet"]}, {"split": "2023_10_28T09_53_17.709619", "path": ["**/details_harness|gsm8k|5_2023-10-28T09-53-17.709619.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T09-53-17.709619.parquet"]}]}, {"config_name": 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2023-10-28T08:53:25+00:00

[]

TAGS #region-us

# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2 ## Dataset Description - Homepage: - Repository: URL - Paper: - Leaderboard: URL - Point of Contact: clementine@URL ### Dataset Summary Dataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard. The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2023-10-28T09:53:17.709619(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): ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields ### Data Splits ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

[ "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T09:53:17.709619(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2", "## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL", "### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2023-10-28T09:53:17.709619(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):", "### Supported Tasks and Leaderboards", "### Languages", "## Dataset Structure", "### Data Instances", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 6, 28, 31, 176, 67, 10, 4, 6, 6, 5, 5, 5, 7, 4, 10, 10, 5, 5, 9, 8, 8, 7, 8, 7, 5, 6, 6, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of wei123602/llama2-13b-FINETUNE3_TEST2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model wei123602/llama2-13b-FINETUNE3_TEST2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T09:53:17.709619(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):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions" ]

a1a8f3821be022cf59f7b3038bc1d95d092b9694

# Dataset of takafuji_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls) This is the dataset of takafuji_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls), containing 341 images and their tags. The core tags of this character are `black_hair, short_hair, breasts, yellow_eyes, bangs, large_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 341 | 446.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 341 | 259.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 830 | 551.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 341 | 398.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 830 | 772.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_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/takafuji_kako_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 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, cleavage, looking_at_viewer, necklace, smile, solo, navel, open_mouth, blush, frilled_bikini, outdoors, bracelet, collarbone, day, hair_flower, medium_breasts, beach, blue_bikini, floral_print, front-tie_top, side-tie_bikini_bottom | | 1 | 18 | ![](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) | kimono, smile, 1girl, hair_flower, looking_at_viewer, solo, blush, obi, floral_print, upper_body | | 2 | 34 | ![](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, solo, smile, hair_bow, looking_at_viewer, detached_sleeves, cleavage, medium_breasts, navel, blush, midriff, bare_shoulders, open_mouth, japanese_clothes | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, blush, collarbone, looking_at_viewer, navel, nipples, solo, completely_nude, brown_eyes, cowboy_shot, medium_breasts, simple_background, smile, white_background | | 4 | 7 | ![](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) | cleavage, detached_collar, playboy_bunny, rabbit_ears, 1girl, brown_eyes, wrist_cuffs, bowtie, looking_at_viewer, rabbit_tail, smile, solo, open_mouth, strapless_leotard, black_leotard, black_pantyhose, cowboy_shot, fake_animal_ears, fishnets, white_background | | 5 | 6 | ![](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, hetero, solo_focus, nipples, smile, breast_grab, collarbone, grabbing, looking_at_viewer, nude, penis, pov, censored, cum_on_body, male_pubic_hair, on_back, open_mouth, paizuri, sweat | | 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, onsen, solo, blush, looking_at_viewer, naked_towel, smile, collarbone, full_moon, water, closed_mouth, medium_breasts, night_sky, nipples, ponytail, snow, wet | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | looking_at_viewer | necklace | smile | solo | navel | open_mouth | blush | frilled_bikini | outdoors | bracelet | collarbone | day | hair_flower | medium_breasts | beach | blue_bikini | floral_print | front-tie_top | side-tie_bikini_bottom | kimono | obi | upper_body | hair_bow | detached_sleeves | midriff | bare_shoulders | japanese_clothes | nipples | completely_nude | brown_eyes | cowboy_shot | simple_background | white_background | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | bowtie | rabbit_tail | strapless_leotard | black_leotard | black_pantyhose | fake_animal_ears | fishnets | 1boy | hetero | solo_focus | breast_grab | grabbing | nude | penis | pov | censored | cum_on_body | male_pubic_hair | on_back | paizuri | sweat | onsen | naked_towel | full_moon | water | closed_mouth | night_sky | ponytail | snow | wet | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:-----------|:--------|:-------|:--------|:-------------|:--------|:-----------------|:-----------|:-----------|:-------------|:------|:--------------|:-----------------|:--------|:--------------|:---------------|:----------------|:-------------------------|:---------|:------|:-------------|:-----------|:-------------------|:----------|:-----------------|:-------------------|:----------|:------------------|:-------------|:--------------|:--------------------|:-------------------|:------------------|:----------------|:--------------|:--------------|:---------|:--------------|:--------------------|:----------------|:------------------|:-------------------|:-----------|:-------|:---------|:-------------|:--------------|:-----------|:-------|:--------|:------|:-----------|:--------------|:------------------|:----------|:----------|:--------|:--------|:--------------|:------------|:--------|:---------------|:------------|:-----------|:-------|:------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 18 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 34 | ![](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 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | | X | | X | X | X | | X | | | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 7 | ![](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 | 6 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | | X | | X | | | X | X | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 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 |

CyberHarem/takafuji_kako_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T12:52:28+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T19:09:29+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of takafuji\_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls) ================================================================== This is the dataset of takafuji\_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls), containing 341 images and their tags. The core tags of this character are 'black\_hair, short\_hair, breasts, yellow\_eyes, bangs, large\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

7d6407ebe4e8b760e0222038d1dab562e1d345b7

# Dataset of mukai_takumi (THE iDOLM@STER: Cinderella Girls) This is the dataset of mukai_takumi (THE iDOLM@STER: Cinderella Girls), containing 467 images and their tags. The core tags of this character are `breasts, long_hair, black_hair, large_breasts, brown_hair, green_eyes, brown_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 | 467 | 541.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 467 | 323.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1087 | 656.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 467 | 484.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1087 | 916.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mukai_takumi_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/mukai_takumi_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 | 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, blush, cleavage, navel, solo, side-tie_bikini_bottom, looking_at_viewer, simple_background, white_background, yellow_eyes, open_clothes | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, looking_at_viewer, simple_background, solo, white_background, cleavage, upper_body, grin, jacket, sarashi, collarbone, hand_on_hip, open_clothes | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | 1girl, blush, cleavage, dress, earrings, necklace, looking_at_viewer, solo, bare_shoulders, smile, collarbone, ponytail, sideboob | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | 1girl, cleavage, looking_at_viewer, open_jacket, ponytail, smile, solo, black_skirt, crop_top, earrings, midriff, miniskirt, navel, necklace, blush, bracelet, collarbone, hand_on_hip, parted_bangs, suspender_skirt, tattoo, thighs, white_jacket, black_choker, closed_mouth, cropped_jacket, hair_flower, idol, sidelocks, thigh_strap, white_background, white_belt, white_gloves | | 4 | 11 | ![](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) | 1boy, 1girl, blush, hetero, solo_focus, sweat, nipples, nude, penis, huge_breasts, mosaic_censoring, open_mouth, vaginal, dark-skinned_male, sex_from_behind | | 5 | 15 | ![](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, hetero, solo_focus, nipples, blush, paizuri, sweat, cum_on_breasts, huge_breasts, penis, censored, pov, ejaculation, smile, teeth, breasts_squeezed_together, looking_at_viewer | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | 1boy, 1girl, blush, hetero, nipples, solo_focus, sex, cum_in_pussy, sweat, vaginal, navel, open_mouth, completely_nude, pov, spread_legs, bar_censor, cowgirl_position, female_pubic_hair, girl_on_top, huge_breasts, penis | | 7 | 8 | ![](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, playboy_bunny, rabbit_ears, solo, blush, fake_animal_ears, wrist_cuffs, cleavage, bowtie, looking_at_viewer, rabbit_tail, bangs, bare_shoulders, covered_navel, strapless_leotard, anger_vein, black_leotard, cowboy_shot, fishnet_pantyhose, grin, open_mouth | | 8 | 5 | ![](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, blush, red_neckerchief, looking_at_viewer, simple_background, solo, white_background, bangs, black_sailor_collar, black_serafuku, black_skirt, collarbone, covering_mouth, crying_with_eyes_open, pleated_skirt, short_sleeves, sitting, upper_body, white_shirt, yellow_eyes | | 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) | maid_apron, blush, looking_at_viewer, 1girl, black_dress, enmaided, maid_headdress, simple_background, solo, white_apron, white_background, bangs, black_footwear, frilled_apron, full_body, juliet_sleeves, shoes, sidelocks, smile, standing | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | cleavage | navel | solo | side-tie_bikini_bottom | looking_at_viewer | simple_background | white_background | yellow_eyes | open_clothes | upper_body | grin | jacket | sarashi | collarbone | hand_on_hip | dress | earrings | necklace | bare_shoulders | smile | ponytail | sideboob | open_jacket | black_skirt | crop_top | midriff | miniskirt | bracelet | parted_bangs | suspender_skirt | tattoo | thighs | white_jacket | black_choker | closed_mouth | cropped_jacket | hair_flower | idol | sidelocks | thigh_strap | white_belt | white_gloves | 1boy | hetero | solo_focus | sweat | nipples | nude | penis | huge_breasts | mosaic_censoring | open_mouth | vaginal | dark-skinned_male | sex_from_behind | paizuri | cum_on_breasts | censored | pov | ejaculation | teeth | breasts_squeezed_together | sex | cum_in_pussy | completely_nude | spread_legs | bar_censor | cowgirl_position | female_pubic_hair | girl_on_top | detached_collar | playboy_bunny | rabbit_ears | fake_animal_ears | wrist_cuffs | bowtie | rabbit_tail | bangs | covered_navel | strapless_leotard | anger_vein | black_leotard | cowboy_shot | fishnet_pantyhose | red_neckerchief | black_sailor_collar | black_serafuku | covering_mouth | crying_with_eyes_open | pleated_skirt | short_sleeves | sitting | white_shirt | maid_apron | black_dress | enmaided | maid_headdress | white_apron | black_footwear | frilled_apron | full_body | juliet_sleeves | shoes | standing | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------|:--------|:-------|:-------------------------|:--------------------|:--------------------|:-------------------|:--------------|:---------------|:-------------|:-------|:---------|:----------|:-------------|:--------------|:--------|:-----------|:-----------|:-----------------|:--------|:-----------|:-----------|:--------------|:--------------|:-----------|:----------|:------------|:-----------|:---------------|:------------------|:---------|:---------|:---------------|:---------------|:---------------|:-----------------|:--------------|:-------|:------------|:--------------|:-------------|:---------------|:-------|:---------|:-------------|:--------|:----------|:-------|:--------|:---------------|:-------------------|:-------------|:----------|:--------------------|:------------------|:----------|:-----------------|:-----------|:------|:--------------|:--------|:----------------------------|:------|:---------------|:------------------|:--------------|:-------------|:-------------------|:--------------------|:--------------|:------------------|:----------------|:--------------|:-------------------|:--------------|:---------|:--------------|:--------|:----------------|:--------------------|:-------------|:----------------|:--------------|:--------------------|:------------------|:----------------------|:-----------------|:-----------------|:------------------------|:----------------|:----------------|:----------|:--------------|:-------------|:--------------|:-----------|:-----------------|:--------------|:-----------------|:----------------|:------------|:-----------------|:--------|:-----------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | | X | | X | | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 | ![](samples/2/clu2-sample0.png) | ![](samples/2/clu2-sample1.png) | ![](samples/2/clu2-sample2.png) | ![](samples/2/clu2-sample3.png) | ![](samples/2/clu2-sample4.png) | X | X | X | | X | | X | | | | | | | | | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | | X | | X | | | | | | | X | X | | X | X | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 11 | ![](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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 15 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | X | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 10 | ![](samples/6/clu6-sample0.png) | ![](samples/6/clu6-sample1.png) | ![](samples/6/clu6-sample2.png) | ![](samples/6/clu6-sample3.png) | ![](samples/6/clu6-sample4.png) | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | X | X | | X | X | | | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 8 | ![](samples/7/clu7-sample0.png) | ![](samples/7/clu7-sample1.png) | ![](samples/7/clu7-sample2.png) | ![](samples/7/clu7-sample3.png) | ![](samples/7/clu7-sample4.png) | X | X | X | | X | | X | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 8 | 5 | ![](samples/8/clu8-sample0.png) | ![](samples/8/clu8-sample1.png) | ![](samples/8/clu8-sample2.png) | ![](samples/8/clu8-sample3.png) | ![](samples/8/clu8-sample4.png) | X | X | | | X | | X | X | X | X | | X | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 9 | 6 | ![](samples/9/clu9-sample0.png) | ![](samples/9/clu9-sample1.png) | ![](samples/9/clu9-sample2.png) | ![](samples/9/clu9-sample3.png) | ![](samples/9/clu9-sample4.png) | X | X | | | X | | X | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/mukai_takumi_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T12:56:03+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-17T00:16:40+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of mukai\_takumi (THE iDOLM@STER: Cinderella Girls) =========================================================== This is the dataset of mukai\_takumi (THE iDOLM@STER: Cinderella Girls), containing 467 images and their tags. The core tags of this character are 'breasts, long\_hair, black\_hair, large\_breasts, brown\_hair, green\_eyes, brown\_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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

a9730f479f045b8300988877ad10effc266a9ff1

# Dataset Card for "simplepile-lite" Interleaved dataset using 'first exhausted' strategy. Counts: ```python DatasetDict({ train: Dataset({ features: ['text'], num_rows: 452432 }) validation: Dataset({ features: ['text'], num_rows: 1000 }) test: Dataset({ features: ['text'], num_rows: 11908 }) }) ``` ## token counts - train using GPTNeoX Tokenizer: | | token_count | |:------|-----------------:| | count | 452432 | | mean | 868.642 | | std | 4791.71 | | min | 3 | | 25% | 88 | | 50% | 232 | | 75% | 590 | | max | 1.39747e+06 | ---

pszemraj/simplepile-lite

[ "task_categories:fill-mask", "task_categories:text-generation", "size_categories:100K<n<1M", "source_datasets:pszemraj/simple_wikipedia_LM", "source_datasets:JeanKaddour/minipile", "language:en", "license:apache-2.0", "region:us" ]

2023-09-14T13:17:39+00:00

{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "source_datasets": ["pszemraj/simple_wikipedia_LM", "JeanKaddour/minipile"], "task_categories": ["fill-mask", "text-generation"], "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": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1552622685, "num_examples": 452432}, {"name": "validation", "num_bytes": 3202346, "num_examples": 1000}, {"name": "test", "num_bytes": 41145686, "num_examples": 11908}], "download_size": 867798625, "dataset_size": 1596970717}}

2023-10-04T06:50:40+00:00

[]

[ "en" ]

TAGS #task_categories-fill-mask #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-pszemraj/simple_wikipedia_LM #source_datasets-JeanKaddour/minipile #language-English #license-apache-2.0 #region-us

Dataset Card for "simplepile-lite" ================================== Interleaved dataset using 'first exhausted' strategy. Counts: token counts - train -------------------- using GPTNeoX Tokenizer: ---

[]

[ "TAGS\n#task_categories-fill-mask #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-pszemraj/simple_wikipedia_LM #source_datasets-JeanKaddour/minipile #language-English #license-apache-2.0 #region-us \n" ]

[ 84 ]

[ "passage: TAGS\n#task_categories-fill-mask #task_categories-text-generation #size_categories-100K<n<1M #source_datasets-pszemraj/simple_wikipedia_LM #source_datasets-JeanKaddour/minipile #language-English #license-apache-2.0 #region-us \n" ]

83edecb2dd5b5a25259047ebd2f4be6c4f2666a0

# Overview This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist. The dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse. ## Dataset details The dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset consists of 990 sentences and the test dataset consists of 660 sentences. ### Dataset annotations - 0: non-offensive - 1: offensive

metabloit/offensive-swahili-text

[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:sw", "license:mit", "region:us" ]

2023-09-14T13:18:50+00:00

{"language": ["sw"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "viewer": true}

2023-09-14T13:33:52+00:00

[]

[ "sw" ]

TAGS #task_categories-text-classification #size_categories-1K<n<10K #language-Swahili (macrolanguage) #license-mit #region-us

# Overview This dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist. The dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse. ## Dataset details The dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset consists of 990 sentences and the test dataset consists of 660 sentences. ### Dataset annotations - 0: non-offensive - 1: offensive

[ "# Overview\nThis dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.\nThe dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse.", "## Dataset details\nThe dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset\nconsists of 990 sentences and the test dataset consists of 660 sentences.", "### Dataset annotations\n- 0: non-offensive\n- 1: offensive" ]

[ "TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Swahili (macrolanguage) #license-mit #region-us \n", "# Overview\nThis dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.\nThe dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse.", "## Dataset details\nThe dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset\nconsists of 990 sentences and the test dataset consists of 660 sentences.", "### Dataset annotations\n- 0: non-offensive\n- 1: offensive" ]

[ 45, 65, 55, 18 ]

[ "passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Swahili (macrolanguage) #license-mit #region-us \n# Overview\nThis dataset contains offensive and non-offensive sentences. The data was scraped from JamiiForums using a prepared wordlist.\nThe dataset contains sentences that consists of swahili abusive words (in the wordlist) but does not contain sarcastic abuse.## Dataset details\nThe dataset is divided into train, evaluation and test datasets. The training dataset consists of 4954 sentences, evaluation dataset\nconsists of 990 sentences and the test dataset consists of 660 sentences.### Dataset annotations\n- 0: non-offensive\n- 1: offensive" ]

fec05d7e8614fa94e6033204bb6c4a264cbc4a09

This dataset uses the system instructions from the orca-wizardlm dataset, but attaches the original evolved instruction responses and two cluster solutions. The dolphin clusters are clusters for the instructions only, using sgpt approach with the dolphin-7b model. The GTE clusters are clusters for the instructions and responses, as rough topic mapping. I use these cluster solutions to downsample the dataset by topic, whilst preserving clusters of 'evolved instructions'.

KnutJaegersberg/orca-wizardlm-v1-clustered

[ "license:cc-by-nc-4.0", "region:us" ]

2023-09-14T13:31:53+00:00

{"license": "cc-by-nc-4.0"}

2023-09-14T13:39:25+00:00

[]

TAGS #license-cc-by-nc-4.0 #region-us

This dataset uses the system instructions from the orca-wizardlm dataset, but attaches the original evolved instruction responses and two cluster solutions. The dolphin clusters are clusters for the instructions only, using sgpt approach with the dolphin-7b model. The GTE clusters are clusters for the instructions and responses, as rough topic mapping. I use these cluster solutions to downsample the dataset by topic, whilst preserving clusters of 'evolved instructions'.

[]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

[ 17 ]

[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

b1078c0aa2f4fc7eb09b2ccb8499e4471a6819ea

This dataset is the gpt-4 generated subset of the dolphin / orca dataset, with clusters from: - gte embeddings - dolphin-7b sgpt embeddings - bigbird document embeddings (simsce sentence transformer based, for the whole document)

KnutJaegersberg/dolphin_orca_clustered

[ "license:cc-by-nc-4.0", "region:us" ]

2023-09-14T13:50:21+00:00

{"license": "cc-by-nc-4.0"}

2023-09-14T14:24:42+00:00

[]

TAGS #license-cc-by-nc-4.0 #region-us

This dataset is the gpt-4 generated subset of the dolphin / orca dataset, with clusters from: - gte embeddings - dolphin-7b sgpt embeddings - bigbird document embeddings (simsce sentence transformer based, for the whole document)

[]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

[ 17 ]

[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

f48bf1ad11a8036c69031cdf1904d498115f9b97

Synthetic languages for nmt testing

aatherton2024/eng-nah-svo

[ "region:us" ]

2023-09-14T13:55:30+00:00

{"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": "en", "dtype": "string"}, {"name": "fr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 291262, "num_examples": 7292}, {"name": "validation", "num_bytes": 39653, "num_examples": 1001}, {"name": "test", "num_bytes": 39857, "num_examples": 1001}], "download_size": 207430, "dataset_size": 370772}}

2023-09-15T14:15:08+00:00

[]

TAGS #region-us

Synthetic languages for nmt testing

[]

[ "TAGS\n#region-us \n" ]

[ 6 ]

[ "passage: TAGS\n#region-us \n" ]

e65f0362ca7e4d0e4ed64c5922b3c60bedc7b785

# Dataset Card for Geo-Tagged Social Media Posts with Timestamps ## Dataset Description - **Homepage:** https://huggingface.co/datasets/yachay/text_coordinates_seasons - **Repository:** https://github.com/Yachay-AI/byt5-geotagging#datasets - **Paper:** https://dev.to/yachayai/applying-machine-learning-to-geolocate-twitter-posts-2m1d - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary The "Seasons" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: **Cuba, Iran, Russia, North Korea, Syria, and Venezuela,** with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages **Multilingual Dataset** Mainly contains English, Spanish, Persian, Russian, Korean, and Arabic. ## Dataset Structure ### Data Instances The "Seasons" dataset consists of over 600,000 data instances, each representing a social media post. ### Data Fields **Text (text):** This field contains the textual content. **Timestamp (created_at):** The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format. **Geographical Coordinates (geo_geo_bbox):** This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format. ```json { "text": "sample text", "geo_geo_bbox": "[-67.220209, 9.934294, -65.428322, 10.6496277]" }, { "created_at": { "$numberLong": "1633049378000" } ``` ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation ### Curation Rationale The "Seasons" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Seasons" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT

yachay/text_coordinates_seasons

[ "task_categories:feature-extraction", "task_categories:token-classification", "task_categories:text-classification", "size_categories:100M<n<1B", "language:en", "language:es", "language:ru", "language:co", "language:ar", "language:fa", "license:mit", "multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit", "region:us" ]

2023-09-14T14:27:55+00:00

{"language": ["en", "es", "ru", "co", "ar", "fa"], "license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["feature-extraction", "token-classification", "text-classification"], "pretty_name": "Geo-Tagged Social Media Posts with Timestamps", "tags": ["multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit"]}

2023-09-22T12:28:15+00:00

[]

[ "en", "es", "ru", "co", "ar", "fa" ]

TAGS #task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Spanish #language-Russian #language-Corsican #language-Arabic #language-Persian #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us

# Dataset Card for Geo-Tagged Social Media Posts with Timestamps ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: admin-team@URL ### Dataset Summary The "Seasons" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages Multilingual Dataset Mainly contains English, Spanish, Persian, Russian, Korean, and Arabic. ## Dataset Structure ### Data Instances The "Seasons" dataset consists of over 600,000 data instances, each representing a social media post. ### Data Fields Text (text): This field contains the textual content. Timestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format. Geographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format. ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation ### Curation Rationale The "Seasons" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Seasons" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT

[ "# Dataset Card for Geo-Tagged Social Media Posts with Timestamps", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Seasons\" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nMainly contains English, Spanish, Persian, Russian, Korean, and Arabic.", "## Dataset Structure", "### Data Instances\n\nThe \"Seasons\" dataset consists of over 600,000 data instances, each representing a social media post.", "### Data Fields\n\nText (text): This field contains the textual content.\n\nTimestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format.\n\nGeographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation", "### Curation Rationale\n\nThe \"Seasons\" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Seasons\" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]

[ "TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Spanish #language-Russian #language-Corsican #language-Arabic #language-Persian #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n", "# Dataset Card for Geo-Tagged Social Media Posts with Timestamps", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Seasons\" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nMainly contains English, Spanish, Persian, Russian, Korean, and Arabic.", "## Dataset Structure", "### Data Instances\n\nThe \"Seasons\" dataset consists of over 600,000 data instances, each representing a social media post.", "### Data Fields\n\nText (text): This field contains the textual content.\n\nTimestamp (created_at): The dataset includes timestamps to track the exact time when each social media post was created. Timestamps are recorded in Unix epoch time format.\n\nGeographical Coordinates (geo_geo_bbox): This field contains geocoordinates that describe the geographical location associated with each social media post. These coordinates are represented as latitude and longitude ranges in a bounding box format.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation", "### Curation Rationale\n\nThe \"Seasons\" dataset was created with an objective to advancing research in NLP by investigating the intricate relationships between temporal factors, content, and author location in social media posts. This dataset was assembled to provide a resource for understanding how time zones and seasonal events influence the model's results.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Seasons\" dataset has a potential to enhance our understanding of the intricate relationship between temporal dynamics, content, and location in social media posts.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions/seasons. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]

[ 149, 17, 32, 144, 67, 27, 6, 33, 120, 68, 5, 77, 4, 37, 12, 5, 21, 21, 8, 42, 64, 88, 5, 10, 7 ]

[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Spanish #language-Russian #language-Corsican #language-Arabic #language-Persian #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n# Dataset Card for Geo-Tagged Social Media Posts with Timestamps## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL### Dataset Summary\n\nThe \"Seasons\" dataset is a collection of over 600,000 social media posts spanning 12 months and encompassing 15 distinct time zones. It focuses on six countries: Cuba, Iran, Russia, North Korea, Syria, and Venezuela, with each post containing textual content, timestamps, and geographical coordinates. The dataset's primary objective is to investigate the correlation between the timing of posts, their content, and the geographical locations. Researchers can leverage this dataset to advance studies in geospatial NLP and gain insights into how temporal factors and seasonality impact the results.### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.### Languages\n\nMultilingual Dataset\n\nMainly contains English, Spanish, Persian, Russian, Korean, and Arabic.## Dataset Structure### Data Instances\n\nThe \"Seasons\" dataset consists of over 600,000 data instances, each representing a social media post." ]

b9fa48181e3c93791d0613382c77aeb91214f324

# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions) ## Dataset Description - **Homepage:** https://huggingface.co/datasets/yachay/text_coordinates_regions - **Repository:** https://github.com/Yachay-AI/byt5-geotagging#datasets - **Paper:** https://dev.to/yachayai/applying-machine-learning-to-geolocate-twitter-posts-2m1d - **Leaderboard:** - **Point of Contact:** [email protected] ### Dataset Summary The "Regions" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates. **Key Features:** - **Textual Data:** The dataset contains 500,000 text samples. - **Geocoordinates:** Each text sample is associated with geocoordinates. - **Regional Coverage:** The dataset encompasses 123 of the world's most populated regions. - **Tweet Data:** Within each region, there are 5,000 individual tweets/comments. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages **Multilingual Dataset** This dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset. ## Dataset Structure **Structure and Naming Convention:** The naming convention for the JSON files follows the format "c_0.json" to "c_122.json," where "c_" represents the region category followed by a unique identifier ```bash / ├── .gitattributes ├── README.md ├── c_0.json # Each .json file attributes to one of 123 regions ├── c_1.json ├── ... ├── c_122.json ``` ### Data Instances The Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file. ### Data Fields **Text (text):** This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content. **Coordinates (coordinates):** This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text. ```json { "text": "sample text", "coordinates": [ "-75.04057630341867", "40.01714225600481" ] } ``` ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation 2021 ### Curation Rationale The "Regions" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Regions" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT

yachay/text_coordinates_regions

[ "task_categories:feature-extraction", "task_categories:token-classification", "task_categories:text-classification", "size_categories:100M<n<1B", "language:en", "language:zh", "language:es", "language:hi", "language:ar", "language:bn", "language:pt", "language:ru", "language:ja", "language:pa", "language:de", "language:jv", "language:ms", "language:te", "language:vi", "language:ko", "language:fr", "language:mr", "language:ta", "language:ur", "language:tr", "language:it", "language:th", "language:gu", "language:fa", "language:pl", "license:mit", "multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit", "region:us" ]

2023-09-14T14:28:51+00:00

{"language": ["en", "zh", "es", "hi", "ar", "bn", "pt", "ru", "ja", "pa", "de", "jv", "ms", "te", "vi", "ko", "fr", "mr", "ta", "ur", "tr", "it", "th", "gu", "fa", "pl"], "license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["feature-extraction", "token-classification", "text-classification"], "pretty_name": "Multilingual Geo-Tagged Social Media Posts (by 123 world regions)", "tags": ["multilingual", "text", "coordinates", "geospatial", "translation", "NER", "geo", "geo-tagged", "named-entity-recognition", "natural-language-processing", "geographic-data", "geolocation", "twitter", "reddit"]}

2023-09-21T15:19:16+00:00

[]

[ "en", "zh", "es", "hi", "ar", "bn", "pt", "ru", "ja", "pa", "de", "jv", "ms", "te", "vi", "ko", "fr", "mr", "ta", "ur", "tr", "it", "th", "gu", "fa", "pl" ]

TAGS #task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Chinese #language-Spanish #language-Hindi #language-Arabic #language-Bengali #language-Portuguese #language-Russian #language-Japanese #language-Panjabi #language-German #language-Javanese #language-Malay (macrolanguage) #language-Telugu #language-Vietnamese #language-Korean #language-French #language-Marathi #language-Tamil #language-Urdu #language-Turkish #language-Italian #language-Thai #language-Gujarati #language-Persian #language-Polish #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us

# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions) ## Dataset Description - Homepage: URL - Repository: URL - Paper: URL - Leaderboard: - Point of Contact: admin-team@URL ### Dataset Summary The "Regions" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates. Key Features: - Textual Data: The dataset contains 500,000 text samples. - Geocoordinates: Each text sample is associated with geocoordinates. - Regional Coverage: The dataset encompasses 123 of the world's most populated regions. - Tweet Data: Within each region, there are 5,000 individual tweets/comments. ### Supported Tasks and Leaderboards This dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification. ### Languages Multilingual Dataset This dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset. ## Dataset Structure Structure and Naming Convention: The naming convention for the JSON files follows the format "c_0.json" to "c_122.json," where "c_" represents the region category followed by a unique identifier ### Data Instances The Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file. ### Data Fields Text (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content. Coordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text. ### Data Splits This dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements. ## Dataset Creation 2021 ### Curation Rationale The "Regions" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool. ### Source Data #### Initial Data Collection and Normalization The initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter. #### Who are the source language producers? Twitter Community ### Annotations #### Annotation process The coordinates in this dataset have been derived from metadata sources. #### Who are the annotators? No manual annotation was conducted for this dataset. ## Considerations for Using the Data ### Social Impact of Dataset The "Regions" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse. ### Discussion of Biases It's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses. ### Other Known Limitations - The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions. - The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. - The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection. ## Additional Information ### Dataset Curators Yachay AI ### Licensing Information MIT

[ "# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions)", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Regions\" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates.\n\nKey Features:\n\n- Textual Data: The dataset contains 500,000 text samples.\n- Geocoordinates: Each text sample is associated with geocoordinates.\n- Regional Coverage: The dataset encompasses 123 of the world's most populated regions.\n- Tweet Data: Within each region, there are 5,000 individual tweets/comments.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nThis dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset.", "## Dataset Structure\n\nStructure and Naming Convention:\n\nThe naming convention for the JSON files follows the format \"c_0.json\" to \"c_122.json,\" where \"c_\" represents the region category followed by a unique identifier", "### Data Instances\n\nThe Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file.", "### Data Fields\n\nText (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content.\n\nCoordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation\n\n2021", "### Curation Rationale\n\nThe \"Regions\" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Regions\" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]

[ "TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Chinese #language-Spanish #language-Hindi #language-Arabic #language-Bengali #language-Portuguese #language-Russian #language-Japanese #language-Panjabi #language-German #language-Javanese #language-Malay (macrolanguage) #language-Telugu #language-Vietnamese #language-Korean #language-French #language-Marathi #language-Tamil #language-Urdu #language-Turkish #language-Italian #language-Thai #language-Gujarati #language-Persian #language-Polish #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n", "# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions)", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL", "### Dataset Summary\n\nThe \"Regions\" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates.\n\nKey Features:\n\n- Textual Data: The dataset contains 500,000 text samples.\n- Geocoordinates: Each text sample is associated with geocoordinates.\n- Regional Coverage: The dataset encompasses 123 of the world's most populated regions.\n- Tweet Data: Within each region, there are 5,000 individual tweets/comments.", "### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.", "### Languages\n\nMultilingual Dataset\n\nThis dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset.", "## Dataset Structure\n\nStructure and Naming Convention:\n\nThe naming convention for the JSON files follows the format \"c_0.json\" to \"c_122.json,\" where \"c_\" represents the region category followed by a unique identifier", "### Data Instances\n\nThe Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file.", "### Data Fields\n\nText (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content.\n\nCoordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text.", "### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.", "## Dataset Creation\n\n2021", "### Curation Rationale\n\nThe \"Regions\" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool.", "### Source Data", "#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.", "#### Who are the source language producers?\n\nTwitter Community", "### Annotations", "#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources.", "#### Who are the annotators?\n\nNo manual annotation was conducted for this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe \"Regions\" dataset, with its multilingual text and geographic coordinates, presents an opportunity to advance research in geospatial NLP. However, it is crucial for users to exercise caution and ethical responsibility when handling location-related data to mitigate any potential privacy concerns or misuse.", "### Discussion of Biases\n\nIt's essential to acknowledge that the data collected from social media platforms may contain inherent biases, influenced by user demographics and platform dynamics. Researchers should be mindful of these biases and consider potential implications in their analyses.", "### Other Known Limitations\n\n- The dataset's multilingual nature may lead to varying data quality and linguistic diversity across regions.\n- The use of geotagged social media comments means that the dataset may not cover less active or less represented regions. \n- The accuracy of geocoordinates is subject to inherent limitations of the data sources used for collection.", "## Additional Information", "### Dataset Curators\n\nYachay AI", "### Licensing Information\n\nMIT" ]

[ 258, 23, 32, 157, 67, 59, 61, 54, 72, 68, 6, 94, 4, 37, 12, 5, 21, 21, 8, 77, 64, 84, 5, 10, 7 ]

[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-token-classification #task_categories-text-classification #size_categories-100M<n<1B #language-English #language-Chinese #language-Spanish #language-Hindi #language-Arabic #language-Bengali #language-Portuguese #language-Russian #language-Japanese #language-Panjabi #language-German #language-Javanese #language-Malay (macrolanguage) #language-Telugu #language-Vietnamese #language-Korean #language-French #language-Marathi #language-Tamil #language-Urdu #language-Turkish #language-Italian #language-Thai #language-Gujarati #language-Persian #language-Polish #license-mit #multilingual #text #coordinates #geospatial #translation #NER #geo #geo-tagged #named-entity-recognition #natural-language-processing #geographic-data #geolocation #twitter #reddit #region-us \n# Dataset Card for Multilingual Geo-Tagged Social Media Posts (by 123 world regions)## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact: admin-team@URL### Dataset Summary\n\nThe \"Regions\" dataset is a multilingual corpus that encompasses textual data from the 123 most populated regions worldwide, with each region's data organized into separate .json files. This dataset consists of approximately 500,000 text samples, each paired with its geographic coordinates.\n\nKey Features:\n\n- Textual Data: The dataset contains 500,000 text samples.\n- Geocoordinates: Each text sample is associated with geocoordinates.\n- Regional Coverage: The dataset encompasses 123 of the world's most populated regions.\n- Tweet Data: Within each region, there are 5,000 individual tweets/comments.", "passage: ### Supported Tasks and Leaderboards\n\nThis dataset is well-suited for tasks such as geotagging, where the objective is to associate text with specific geographical locations. It can also be utilized for geolocation analysis, sentiment analysis in regional contexts, and regional text classification.### Languages\n\nMultilingual Dataset\n\nThis dataset is multilingual and contains text data in various languages from around the world. It does not have a fixed set of languages, and the language composition may vary across different versions or updates of the dataset.## Dataset Structure\n\nStructure and Naming Convention:\n\nThe naming convention for the JSON files follows the format \"c_0.json\" to \"c_122.json,\" where \"c_\" represents the region category followed by a unique identifier### Data Instances\n\nThe Regions dataset consists of a total of 500,000 data instances, with each instance comprising a text sample and its associated geocoordinates. These instances are distributed across the 123 in each json file.### Data Fields\n\nText (text): This field contains the text sample, typically holds natural language text data, such as comments, tweets, or any text-based content.\n\nCoordinates (coordinates): This field includes geographical coordinates, latitude and longitude, providing the geographic location associated with the text.### Data Splits\n\nThis dataset is not pre-partitioned into training, validation, and test data splits, providing flexibility for users to split the data according to their specific research or application needs. Users can customize the data partitioning to suit their machine learning experiments and analytical requirements.## Dataset Creation\n\n2021### Curation Rationale\n\nThe \"Regions\" dataset was created with an objective to train and enhance geotagging textual models. With 500,000 text samples, each paired with geocoordinates, it offers a resource for developing models that can associate text with specific geographical locations. Whether for geolocation analysis or other tasks merging text and geographic information, this dataset serves as a valuable training tool.### Source Data#### Initial Data Collection and Normalization\n\nThe initial data collection process focused on gathering geotagged comments from social media platforms, with a primary emphasis on Twitter.#### Who are the source language producers?\n\nTwitter Community### Annotations#### Annotation process\n\nThe coordinates in this dataset have been derived from metadata sources." ]

474c9ac0b09f0fc7732812e675e5b164c466fc5d

# Dataset of yusa_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls) This is the dataset of yusa_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls), containing 382 images and their tags. The core tags of this character are `blonde_hair, green_eyes, ahoge, twintails, long_hair, low_twintails, 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 | 382 | 475.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 382 | 270.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 893 | 576.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 382 | 422.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 893 | 843.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yusa_kozue_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/yusa_kozue_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 | 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, blush, cat_ears, cat_girl, cat_tail, dress, looking_at_viewer, open_mouth, solo, animal_ear_fluff, simple_background, white_background, bell, between_legs, collarbone, long_sleeves, sitting | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, solo, dress, open_mouth, looking_at_viewer, socks, sitting, wrist_cuffs | | 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) | 1girl, solo, blush, looking_at_viewer, open_mouth, white_background, brown_dress, hair_bow, simple_background, :d, frilled_dress, long_sleeves, plaid_dress, kneehighs, shoes, white_socks | | 3 | 15 | ![](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) | blush, hair_flower, looking_at_viewer, 1girl, head_wreath, solo, navel, open_mouth, white_background, wrist_cuffs, skirt, bare_shoulders, dress, pink_flower, collarbone, :o, flower_necklace, simple_background, :d, fairy_wings, flower_wreath, sandals, shirt, swept_bangs, upper_body | | 4 | 7 | ![](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, loli, navel, simple_background, solo, white_background, groin, nipples, flat_chest, looking_at_viewer, open_mouth, ass_visible_through_thighs, clothes_lift, lifted_by_self, nude, pussy, shirt | | 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, loli, open_mouth, 1boy, flat_chest, hetero, navel, nipples, sex, spread_legs, censored, completely_nude, cum_in_pussy, penis, solo_focus, thighs, vaginal, girl_on_top, overflow, pov, smile, straddling | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | cat_ears | cat_girl | cat_tail | dress | looking_at_viewer | open_mouth | solo | animal_ear_fluff | simple_background | white_background | bell | between_legs | collarbone | long_sleeves | sitting | socks | wrist_cuffs | brown_dress | hair_bow | :d | frilled_dress | plaid_dress | kneehighs | shoes | white_socks | hair_flower | head_wreath | navel | skirt | bare_shoulders | pink_flower | :o | flower_necklace | fairy_wings | flower_wreath | sandals | shirt | swept_bangs | upper_body | loli | groin | nipples | flat_chest | ass_visible_through_thighs | clothes_lift | lifted_by_self | nude | pussy | 1boy | hetero | sex | spread_legs | censored | completely_nude | cum_in_pussy | penis | solo_focus | thighs | vaginal | girl_on_top | overflow | pov | smile | straddling | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-----------|:-----------|:-----------|:--------|:--------------------|:-------------|:-------|:-------------------|:--------------------|:-------------------|:-------|:---------------|:-------------|:---------------|:----------|:--------|:--------------|:--------------|:-----------|:-----|:----------------|:--------------|:------------|:--------|:--------------|:--------------|:--------------|:--------|:--------|:-----------------|:--------------|:-----|:------------------|:--------------|:----------------|:----------|:--------|:--------------|:-------------|:-------|:--------|:----------|:-------------|:-----------------------------|:---------------|:-----------------|:-------|:--------|:-------|:---------|:------|:--------------|:-----------|:------------------|:---------------|:--------|:-------------|:---------|:----------|:--------------|:-----------|:------|:--------|:-------------| | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 14 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | | | | X | X | X | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | 15 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | | | | X | X | X | X | | X | X | | | X | | | | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 7 | ![](samples/4/clu4-sample0.png) | ![](samples/4/clu4-sample1.png) | ![](samples/4/clu4-sample2.png) | ![](samples/4/clu4-sample3.png) | ![](samples/4/clu4-sample4.png) | X | X | | | | | X | X | X | | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 5 | 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 | X | X | X | X | X | X | X |

CyberHarem/yusa_kozue_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T14:44:19+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T16:47:46+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of yusa\_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls) ===================================================================== This is the dataset of yusa\_kozue/遊佐こずえ/유사코즈에 (THE iDOLM@STER: Cinderella Girls), containing 382 images and their tags. The core tags of this character are 'blonde\_hair, green\_eyes, ahoge, twintails, long\_hair, low\_twintails, 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(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]

4a426e9a8a9ffa006b060a695f9f813fbe4b5eae

# Dataset of himekawa_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls) This is the dataset of himekawa_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls), containing 334 images and their tags. The core tags of this character are `long_hair, brown_hair, green_eyes, hair_ornament, hairclip, breasts, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 334 | 300.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 334 | 217.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 756 | 429.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 334 | 285.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 756 | 529.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/himekawa_yuki_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/himekawa_yuki_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 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | 1girl, blush, open_mouth, solo, :d, looking_at_viewer, baseball_cap, skirt | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | 1girl, blush, midriff, navel, open_mouth, skirt, solo, cleavage, :d, crop_top, looking_at_viewer, pom_pom_(cheerleading), cheerleader, baseball_cap, bow, detached_sleeves, thighhighs | | 2 | 8 | ![](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, navel, open_mouth, smile, cleavage, solo, ;d, blush, looking_at_viewer, one_eye_closed, orange_bikini, large_breasts, character_name, side-tie_bikini_bottom | | 3 | 9 | ![](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, cleavage, looking_at_viewer, solo, bangs, collarbone, open_mouth, teeth, cross-laced_clothes, navel, shark_hood, spiked_bracelet, :d, bare_shoulders, blue_sky, day, front-tie_bikini_top, large_breasts, outdoors, spiked_collar, stomach, hair_between_eyes, hood_up, cloud | | 4 | 5 | ![](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, cleavage, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, simple_background, smile, solo, strapless_leotard, black_pantyhose, open_mouth, white_background, wrist_cuffs, alcohol, bangs, black_leotard, covered_navel, dated, holding, large_breasts, teeth, bare_shoulders, black_bowtie, bottle, cowboy_shot, fishnet_pantyhose, hair_between_eyes, highleg_leotard, orange_leotard, rabbit_tail, signature, sitting, yellow_bowtie | | 5 | 17 | ![](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, hetero, solo_focus, 1boy, nipples, penis, open_mouth, sweat, sex, bar_censor, navel, smile, vaginal, large_breasts, cum_in_pussy, female_pubic_hair, heart, completely_nude | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | open_mouth | solo | :d | looking_at_viewer | baseball_cap | skirt | midriff | navel | cleavage | crop_top | pom_pom_(cheerleading) | cheerleader | bow | detached_sleeves | thighhighs | smile | ;d | one_eye_closed | orange_bikini | large_breasts | character_name | side-tie_bikini_bottom | bangs | collarbone | teeth | cross-laced_clothes | shark_hood | spiked_bracelet | bare_shoulders | blue_sky | day | front-tie_bikini_top | outdoors | spiked_collar | stomach | hair_between_eyes | hood_up | cloud | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | simple_background | strapless_leotard | black_pantyhose | white_background | wrist_cuffs | alcohol | black_leotard | covered_navel | dated | holding | black_bowtie | bottle | cowboy_shot | fishnet_pantyhose | highleg_leotard | orange_leotard | rabbit_tail | signature | sitting | yellow_bowtie | hetero | solo_focus | 1boy | nipples | penis | sweat | sex | bar_censor | vaginal | cum_in_pussy | female_pubic_hair | heart | completely_nude | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------|:-------|:-----|:--------------------|:---------------|:--------|:----------|:--------|:-----------|:-----------|:-------------------------|:--------------|:------|:-------------------|:-------------|:--------|:-----|:-----------------|:----------------|:----------------|:-----------------|:-------------------------|:--------|:-------------|:--------|:----------------------|:-------------|:------------------|:-----------------|:-----------|:------|:-----------------------|:-----------|:----------------|:----------|:--------------------|:----------|:--------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------------|:------------------|:-------------------|:--------------|:----------|:----------------|:----------------|:--------|:----------|:---------------|:---------|:--------------|:--------------------|:------------------|:-----------------|:--------------|:------------|:----------|:----------------|:---------|:-------------|:-------|:----------|:--------|:--------|:------|:-------------|:----------|:---------------|:--------------------|:--------|:------------------| | 0 | 9 | ![](samples/0/clu0-sample0.png) | ![](samples/0/clu0-sample1.png) | ![](samples/0/clu0-sample2.png) | ![](samples/0/clu0-sample3.png) | ![](samples/0/clu0-sample4.png) | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 | ![](samples/1/clu1-sample0.png) | ![](samples/1/clu1-sample1.png) | ![](samples/1/clu1-sample2.png) | ![](samples/1/clu1-sample3.png) | ![](samples/1/clu1-sample4.png) | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 | ![](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 | 9 | ![](samples/3/clu3-sample0.png) | ![](samples/3/clu3-sample1.png) | ![](samples/3/clu3-sample2.png) | ![](samples/3/clu3-sample3.png) | ![](samples/3/clu3-sample4.png) | X | X | X | X | X | X | | | | X | X | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 | ![](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 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 5 | 17 | ![](samples/5/clu5-sample0.png) | ![](samples/5/clu5-sample1.png) | ![](samples/5/clu5-sample2.png) | ![](samples/5/clu5-sample3.png) | ![](samples/5/clu5-sample4.png) | X | X | X | | | | | | | X | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/himekawa_yuki_idolmastercinderellagirls

[ "task_categories:text-to-image", "size_categories:n<1K", "license:mit", "art", "not-for-all-audiences", "region:us" ]

2023-09-14T14:50:50+00:00

{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}

2024-01-16T17:19:30+00:00

[]

TAGS #task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us

Dataset of himekawa\_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls) ======================================================================== This is the dataset of himekawa\_yuki/姫川友紀/히메카와유키 (THE iDOLM@STER: Cinderella Girls), containing 334 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, green\_eyes, hair\_ornament, hairclip, breasts, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization). List of Packages ---------------- ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code List of Clusters ---------------- List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version ### Table Version

[ "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.", "### Raw Text Version", "### Table Version" ]

[ 44, 61, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version" ]