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5d5cd07bb49954d0812af0e721fbecddd42a611e

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

CyberHarem/kashin_koji_fgo

2024-01-17T11:19:30+00:00

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

2024-01-17T11:29:23+00:00

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

Dataset of Kashin Koji/果心居士 (Fate/Grand Order) ============================================== This is the dataset of Kashin Koji/果心居士 (Fate/Grand Order), containing 34 images and their tags. The core tags of this character are 'heterochromia, long\_hair, multicolored\_hair, red\_eyes, white\_hair, black\_hair, bangs, grey\_hair, two-tone\_hair, hair\_ornament, green\_eyes, twintails, very\_long\_hair, blue\_eyes', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

d4ff1d7811910aa78e494ebad0c65b072b8630b2

# Dataset of Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order) This is the dataset of Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order), containing 25 images and their tags. The core tags of this character are `brown_hair, breasts, brown_eyes, large_breasts, yellow_eyes, ahoge, 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 | 25 | 37.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sugitani_zenjubou_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 25 | 18.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sugitani_zenjubou_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 60 | 41.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sugitani_zenjubou_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 25 | 31.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sugitani_zenjubou_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 60 | 65.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sugitani_zenjubou_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/sugitani_zenjubou_fgo', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------| | 0 | 7 |  |  |  |  |  | necklace, solo, bandages, scarf, 1girl, prayer_beads, hat, looking_at_viewer, short_hair, smile, cleavage | | 1 | 9 |  |  |  |  |  | 1girl, scarf, solo, bandaged_arm, sideboob, long_hair, looking_at_viewer, smile | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | necklace | solo | bandages | scarf | 1girl | prayer_beads | hat | looking_at_viewer | short_hair | smile | cleavage | bandaged_arm | sideboob | long_hair | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------|:-------|:-----------|:--------|:--------|:---------------|:------|:--------------------|:-------------|:--------|:-----------|:---------------|:-----------|:------------| | 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | 1 | 9 |  |  |  |  |  | | X | | X | X | | | X | | X | | X | X | X |

CyberHarem/sugitani_zenjubou_fgo

2024-01-17T11:20:06+00:00

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

2024-01-17T11:26:05+00:00

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

Dataset of Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order) ====================================================== This is the dataset of Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order), containing 25 images and their tags. The core tags of this character are 'brown\_hair, breasts, brown\_eyes, large\_breasts, yellow\_eyes, ahoge, 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

7a2ceddb8d37b5cf3670b34e1cb32110b610a770

## Description Weather. Channel. ## Model SVD ## Voice Julian # Tags - News # Style groundhog, live tv channel, weather news report, tv studio # Music soft breaking news intro ## Prompt Groundhog TV is an AI tube channel generating videos to summarize the weather forecast of the day. The channel should keep the tone light, eventually making joke depending on the weather, sun, rain etc

jbilcke-hf/ai-tube-groundhog-tv

2024-01-17T11:22:11+00:00

{"license": "cc-by-nc-4.0", "pretty_name": "Groundhog TV"}

2024-01-31T20:55:20+00:00

TAGS #license-cc-by-nc-4.0 #region-us

## Description Weather. Channel. ## Model SVD ## Voice Julian # Tags - News # Style groundhog, live tv channel, weather news report, tv studio # Music soft breaking news intro ## Prompt Groundhog TV is an AI tube channel generating videos to summarize the weather forecast of the day. The channel should keep the tone light, eventually making joke depending on the weather, sun, rain etc

cae1fd6435c03dca005205b54c77ba55454cf01d

# **Function Invocation and Time-Based Greeting Dataset** This unique dataset is designed for advanced natural language understanding and features function-calling capabilities. Each entry includes a contextual timestamp, a function definition, user inquiries, function calls, function responses, and an assistant's response. What makes this dataset stand out is the assistant's ability to tailor its greetings based on the time of day. For example, in the morning, it greets with "Good morning," while in the evening, the greeting changes accordingly. Notably, if the time is late at night, the assistant thoughtfully reminds the user about the importance of rest but still remains responsive to commands. This dynamic interaction based on timestamps showcases potential for creating more intuitive and human-like AI assistants. ```text "context": "Wednesday, 02.06.2038 09:51", "functions": "{'description': 'Get report data', 'name': 'v3_media', 'parameters': {'properties': {'file_id': {'description': 'UUID of the file.', 'type': 'string'}}, 'required': ['file_id'], 'type': 'object'}}", "user": "I'm curious about the data in this report. Can you fetch it for me?", "function_call": "{'name': 'v3_media', 'arguments': {'file_id': '12345'}}", "function_response": "{'file_id': '12345', 'data': {'title': 'Sales Report', 'date': '2022-01-01', 'total_sales': 50000, 'top_selling_product': 'Widget X'}}", "assistant_response": "Good Wednesday morning. Here is the data from the report:\n\n- Title: Sales Report\n- Date: 2022-01-01\n- Total Sales: $50,000\n- Top Selling Product: Widget X" ```

Isaak-Carter/Openai-function-invocations-20k-with-greetings

2024-01-17T11:22:44+00:00

{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["conversational", "text-generation"], "dataset_info": {"features": [{"name": "function_call", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "functions", "dtype": "string"}, {"name": "user", "dtype": "string"}, {"name": "assistant_response", "dtype": "string"}, {"name": "function_response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18475155, "num_examples": 20432}], "download_size": 7342675, "dataset_size": 18475155}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-01-17T11:33:50+00:00

TAGS #task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us

# Function Invocation and Time-Based Greeting Dataset This unique dataset is designed for advanced natural language understanding and features function-calling capabilities. Each entry includes a contextual timestamp, a function definition, user inquiries, function calls, function responses, and an assistant's response. What makes this dataset stand out is the assistant's ability to tailor its greetings based on the time of day. For example, in the morning, it greets with "Good morning," while in the evening, the greeting changes accordingly. Notably, if the time is late at night, the assistant thoughtfully reminds the user about the importance of rest but still remains responsive to commands. This dynamic interaction based on timestamps showcases potential for creating more intuitive and human-like AI assistants.

d0b3910af57de6fadb5baca27b43b7fef011ea01

# Dataset of lyn/リンディス (Fire Emblem) This is the dataset of lyn/リンディス (Fire Emblem), containing 500 images and their tags. The core tags of this character are `green_hair, long_hair, ponytail, green_eyes, breasts, earrings, large_breasts, bangs, very_long_hair, high_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 | 500 | 687.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyn_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 392.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyn_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1200 | 816.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyn_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 611.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lyn_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1200 | 1.14 GiB | [Download](https://huggingface.co/datasets/CyberHarem/lyn_fireemblem/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/lyn_fireemblem', 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 |  |  |  |  |  | 1girl, blush, hair_flower, jewelry, official_alternate_costume, solo, bare_shoulders, blue_bikini, looking_at_viewer, choker, cleavage, collarbone, smile, simple_background, closed_mouth, strapless_bikini, white_background, navel | | 1 | 11 |  |  |  |  |  | 1girl, hair_flower, jewelry, official_alternate_costume, outdoors, blue_bikini, day, solo, blue_sky, cleavage, cloud, looking_at_viewer, ocean, beach, smile, navel, bare_shoulders, blush, open_mouth, one_eye_closed, strapless_bikini, thighs, water | | 2 | 6 |  |  |  |  |  | 1girl, fingerless_gloves, jewelry, looking_at_viewer, sword, open_mouth, solo, blue_dress, simple_background, smile, side_slit | | 3 | 13 |  |  |  |  |  | 1girl, fingerless_gloves, holding_sword, thighs, jewelry, pelvic_curtain, solo, blue_dress, katana, looking_at_viewer, short_sleeves, side_slit, sheath, black_gloves, boots, simple_background, medium_breasts, smile, white_background, full_body, open_mouth, sash | | 4 | 6 |  |  |  |  |  | 1girl, dress, jewelry, solo, arrow_(projectile), fingerless_gloves, holding_bow_(weapon), looking_at_viewer, quiver, simple_background, smile, white_background, feathers, armor, boots, pelvic_curtain, thighs | | 5 | 5 |  |  |  |  |  | 1girl, arrow_(projectile), dress, elbow_gloves, feathers, fingerless_gloves, full_body, hair_ornament, holding_bow_(weapon), jewelry, knee_boots, medium_breasts, short_sleeves, solo, brown_footwear, fur_trim, shoulder_armor, simple_background, belt, fur_cape, pelvic_curtain, quiver, sheath, standing, white_background, grey_background, looking_away, parted_lips, side_slit, sword, thighs, torn_clothes | | 6 | 6 |  |  |  |  |  | cleavage, japanese_clothes, jewelry, ninja, pelvic_curtain, sword, 1girl, bare_shoulders, fingerless_gloves, scarf, shuriken, solo, elbow_gloves, holding_weapon, medium_breasts, official_alternate_costume, thighs, looking_at_viewer, simple_background, smile | | 7 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, jewelry, penis, solo_focus, open_mouth, paizuri, bar_censor, fingerless_gloves, nipples, cum_on_breasts, breasts_squeezed_together, male_pubic_hair, black_gloves, blue_dress, breasts_out, clothes_lift, ejaculation, facial, smile, sweatdrop, white_background | | 8 | 5 |  |  |  |  |  | 1girl, bare_shoulders, bride, hair_flower, necklace, wedding_dress, white_dress, bouquet, cleavage, official_alternate_costume, open_mouth, solo, strapless_dress, blush, detached_sleeves, holding, looking_at_viewer, simple_background, white_background, bridal_veil, medium_breasts, petals, smile, sweatdrop | | 9 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, sex, solo_focus, mosaic_censoring, penis, vaginal, closed_eyes, nipples, spread_legs, cum_in_pussy, rape, tears | | 10 | 12 |  |  |  |  |  | 1girl, hetero, nipples, sex, 1boy, sweat, blush, solo_focus, vaginal, completely_nude, girl_on_top, penis, mosaic_censoring, jewelry, medium_breasts, cowgirl_position, cum, looking_at_viewer, pubic_hair | | 11 | 5 |  |  |  |  |  | 1girl, bandages, bandeau, blue_shirt, blue_skirt, chest_sarashi, full_body, holding_polearm, mask_on_head, midriff, navel, official_alternate_costume, paper_lantern, single_bare_shoulder, single_sleeve, solo, spear, stomach, tube_top, white_background, cleavage, simple_background, thighs, looking_at_viewer, short_sleeves, standing, :d, collarbone, fire, grin, jewelry, lips, open_mouth, teeth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | hair_flower | jewelry | official_alternate_costume | solo | bare_shoulders | blue_bikini | looking_at_viewer | choker | cleavage | collarbone | smile | simple_background | closed_mouth | strapless_bikini | white_background | navel | outdoors | day | blue_sky | cloud | ocean | beach | open_mouth | one_eye_closed | thighs | water | fingerless_gloves | sword | blue_dress | side_slit | holding_sword | pelvic_curtain | katana | short_sleeves | sheath | black_gloves | boots | medium_breasts | full_body | sash | dress | arrow_(projectile) | holding_bow_(weapon) | quiver | feathers | armor | elbow_gloves | hair_ornament | knee_boots | brown_footwear | fur_trim | shoulder_armor | belt | fur_cape | standing | grey_background | looking_away | parted_lips | torn_clothes | japanese_clothes | ninja | scarf | shuriken | holding_weapon | 1boy | hetero | penis | solo_focus | paizuri | bar_censor | nipples | cum_on_breasts | breasts_squeezed_together | male_pubic_hair | breasts_out | clothes_lift | ejaculation | facial | sweatdrop | bride | necklace | wedding_dress | white_dress | bouquet | strapless_dress | detached_sleeves | holding | bridal_veil | petals | sex | mosaic_censoring | vaginal | closed_eyes | spread_legs | cum_in_pussy | rape | tears | sweat | completely_nude | girl_on_top | cowgirl_position | cum | pubic_hair | bandages | bandeau | blue_shirt | blue_skirt | chest_sarashi | holding_polearm | mask_on_head | midriff | paper_lantern | single_bare_shoulder | single_sleeve | spear | stomach | tube_top | :d | fire | grin | lips | teeth | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------|:----------|:-----------------------------|:-------|:-----------------|:--------------|:--------------------|:---------|:-----------|:-------------|:--------|:--------------------|:---------------|:-------------------|:-------------------|:--------|:-----------|:------|:-----------|:--------|:--------|:--------|:-------------|:-----------------|:---------|:--------|:--------------------|:--------|:-------------|:------------|:----------------|:-----------------|:---------|:----------------|:---------|:---------------|:--------|:-----------------|:------------|:-------|:--------|:---------------------|:-----------------------|:---------|:-----------|:--------|:---------------|:----------------|:-------------|:-----------------|:-----------|:-----------------|:-------|:-----------|:-----------|:------------------|:---------------|:--------------|:---------------|:-------------------|:--------|:--------|:-----------|:-----------------|:-------|:---------|:--------|:-------------|:----------|:-------------|:----------|:-----------------|:----------------------------|:------------------|:--------------|:---------------|:--------------|:---------|:------------|:--------|:-----------|:----------------|:--------------|:----------|:------------------|:-------------------|:----------|:--------------|:---------|:------|:-------------------|:----------|:--------------|:--------------|:---------------|:-------|:--------|:--------|:------------------|:--------------|:-------------------|:------|:-------------|:-----------|:----------|:-------------|:-------------|:----------------|:------------------|:---------------|:----------|:----------------|:-----------------------|:----------------|:--------|:----------|:-----------|:-----|:-------|:-------|:-------|:--------| | 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | X | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 |  |  |  |  |  | X | | | X | | X | | | X | | | | X | X | | | | | | | | | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 13 |  |  |  |  |  | X | | | X | | X | | | X | | | | X | X | | | X | | | | | | | | X | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 |  |  |  |  |  | X | | | X | | X | | | X | | | | X | X | | | X | | | | | | | | | | X | | X | | | | | X | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 |  |  |  |  |  | X | | | X | | X | | | | | | | | X | | | X | | | | | | | | | | X | | X | X | | 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 | 6 |  |  |  |  |  | X | | | X | X | X | X | | X | | X | | X | X | | | | | | | | | | | | | X | | X | X | | | | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 8 |  |  |  |  |  | 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 |  |  |  |  |  | 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 | 6 |  |  |  |  |  | X | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 12 |  |  |  |  |  | X | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | X | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 11 | 5 |  |  |  |  |  | X | | | X | X | X | | | X | | X | X | | X | | | X | X | | | | | | | 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/lyn_fireemblem

2024-01-17T11:25:15+00:00

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

2024-01-17T13:07:25+00:00

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

Dataset of lyn/リンディス (Fire Emblem) ================================== This is the dataset of lyn/リンディス (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'green\_hair, long\_hair, ponytail, green\_eyes, breasts, earrings, large\_breasts, bangs, very\_long\_hair, high\_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

c2372ae32a48f94542b58fa664e1275729c4ffe5

# Dataset of sakura/サクラ (Fire Emblem) This is the dataset of sakura/サクラ (Fire Emblem), containing 362 images and their tags. The core tags of this character are `short_hair, pink_hair, hairband, pink_eyes, breasts, red_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 | 362 | 369.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakura_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 362 | 239.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakura_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 769 | 463.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakura_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 362 | 339.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakura_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 769 | 611.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakura_fireemblem/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/sakura_fireemblem', 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 | 21 |  |  |  |  |  | 1boy, 1girl, hetero, blush, nipples, sex, open_mouth, solo_focus, penis, sweat, vaginal, pussy, mosaic_censoring, medium_breasts, spread_legs, navel, red_hair, small_breasts, thighhighs, completely_nude | | 1 | 5 |  |  |  |  |  | 1girl, gloves, japanese_clothes, simple_background, smile, solo, white_background, capelet, looking_at_viewer | | 2 | 6 |  |  |  |  |  | 1girl, japanese_clothes, smile, solo, capelet, gloves, blush, open_mouth, thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | blush | nipples | sex | open_mouth | solo_focus | penis | sweat | vaginal | pussy | mosaic_censoring | medium_breasts | spread_legs | navel | red_hair | small_breasts | thighhighs | completely_nude | gloves | japanese_clothes | simple_background | smile | solo | white_background | capelet | looking_at_viewer | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:--------|:----------|:------|:-------------|:-------------|:--------|:--------|:----------|:--------|:-------------------|:-----------------|:--------------|:--------|:-----------|:----------------|:-------------|:------------------|:---------|:-------------------|:--------------------|:--------|:-------|:-------------------|:----------|:--------------------| | 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 1 | 5 |  |  |  |  |  | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | 2 | 6 |  |  |  |  |  | | X | | X | | | X | | | | | | | | | | | | X | | X | X | | X | X | | X | |

CyberHarem/sakura_fireemblem

2024-01-17T11:26:46+00:00

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

2024-01-17T13:16:55+00:00

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

Dataset of sakura/サクラ (Fire Emblem) =================================== This is the dataset of sakura/サクラ (Fire Emblem), containing 362 images and their tags. The core tags of this character are 'short\_hair, pink\_hair, hairband, pink\_eyes, breasts, red\_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

345016b2cdd07bd75ce7fbdaee94e1d5e4cc1350

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

ashish23/filtered_wikibook

2024-01-17T11:31:17+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2567855206.0077558, "num_examples": 8433293}, {"name": "test", "num_bytes": 7727048.207960854, "num_examples": 25377}], "download_size": 11760058784, "dataset_size": 2575582254.215717}}

2024-01-17T11:49:42+00:00

TAGS #region-us

# Dataset Card for "filtered_wikibook" More Information needed

bea123f296f3c915365aeeb244f7f5065b4c75fb

# Dataset of katua/カチュア/카츄아 (Fire Emblem) This is the dataset of katua/カチュア/카츄아 (Fire Emblem), containing 270 images and their tags. The core tags of this character are `blue_hair, short_hair, blue_eyes, headband, 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 | 270 | 245.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katua_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 270 | 165.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katua_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 534 | 304.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katua_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 270 | 227.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katua_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 534 | 387.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/katua_fireemblem/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/katua_fireemblem', 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 | 6 |  |  |  |  |  | 1girl, elbow_gloves, full_body, solo, thigh_boots, thighhighs, breastplate, fingerless_gloves, looking_at_viewer, short_dress, side_slit, simple_background, spear, white_background, holding_weapon, standing, sword, pegasus_knight_uniform_(fire_emblem), sheath, shoulder_armor, smile, zettai_ryouiki | | 1 | 22 |  |  |  |  |  | 1girl, solo, elbow_gloves, fingerless_gloves, pegasus_knight_uniform_(fire_emblem), thighhighs, spear, breastplate, smile, boots, simple_background, zettai_ryouiki | | 2 | 26 |  |  |  |  |  | 1girl, solo, nipples, blush, nude, large_breasts, pussy, open_mouth | | 3 | 15 |  |  |  |  |  | 1girl, white_dress, bare_shoulders, smile, solo, wedding_dress, simple_background, bangs, detached_collar, strapless_dress, hair_flower, white_background, full_body, feather_trim, official_alternate_costume, skirt_hold, white_footwear, closed_mouth, detached_sleeves, holding, looking_at_viewer | | 4 | 14 |  |  |  |  |  | fake_animal_ears, rabbit_ears, rabbit_tail, 1girl, pegasus_knight_uniform_(fire_emblem), solo, elbow_gloves, thighhighs, blush, playboy_bunny, hair_flower, looking_at_viewer, simple_background, white_gloves, cleavage, egg, detached_collar, open_mouth, white_background | | 5 | 7 |  |  |  |  |  | 1boy, hetero, nipples, open_mouth, 1girl, blush, sex, solo_focus, sweat, vaginal, pussy, spread_legs, closed_eyes, completely_nude, female_pubic_hair, girl_on_top, mosaic_censoring, navel, penis, cowgirl_position | | 6 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, sex, solo_focus, open_mouth, penis, thighhighs, vaginal, white_headband, blush, censored, cum_in_pussy, fingerless_gloves, spread_legs, sweat, arm_grab, armor, ass, breasts_out, closed_eyes, on_back | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | full_body | solo | thigh_boots | thighhighs | breastplate | fingerless_gloves | looking_at_viewer | short_dress | side_slit | simple_background | spear | white_background | holding_weapon | standing | sword | pegasus_knight_uniform_(fire_emblem) | sheath | shoulder_armor | smile | zettai_ryouiki | boots | nipples | blush | nude | large_breasts | pussy | open_mouth | white_dress | bare_shoulders | wedding_dress | bangs | detached_collar | strapless_dress | hair_flower | feather_trim | official_alternate_costume | skirt_hold | white_footwear | closed_mouth | detached_sleeves | holding | fake_animal_ears | rabbit_ears | rabbit_tail | playboy_bunny | white_gloves | cleavage | egg | 1boy | hetero | sex | solo_focus | sweat | vaginal | spread_legs | closed_eyes | completely_nude | female_pubic_hair | girl_on_top | mosaic_censoring | navel | penis | cowgirl_position | white_headband | censored | cum_in_pussy | arm_grab | armor | ass | breasts_out | on_back | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:------------|:-------|:--------------|:-------------|:--------------|:--------------------|:--------------------|:--------------|:------------|:--------------------|:--------|:-------------------|:-----------------|:-----------|:--------|:---------------------------------------|:---------|:-----------------|:--------|:-----------------|:--------|:----------|:--------|:-------|:----------------|:--------|:-------------|:--------------|:-----------------|:----------------|:--------|:------------------|:------------------|:--------------|:---------------|:-----------------------------|:-------------|:-----------------|:---------------|:-------------------|:----------|:-------------------|:--------------|:--------------|:----------------|:---------------|:-----------|:------|:-------|:---------|:------|:-------------|:--------|:----------|:--------------|:--------------|:------------------|:--------------------|:--------------|:-------------------|:--------|:--------|:-------------------|:-----------------|:-----------|:---------------|:-----------|:--------|:------|:--------------|:----------| | 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 22 |  |  |  |  |  | X | X | | X | | X | X | X | | | | X | X | | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 26 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 15 |  |  |  |  |  | X | | X | X | | | | | X | | | X | | X | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 14 |  |  |  |  |  | X | X | | X | | X | | | X | | | X | | X | | | | X | | | | | | | X | | | | X | | | | | X | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 6 | 5 |  |  |  |  |  | X | | | | | X | | X | | | | | | | | | | | | | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | X | | X | X | X | X | X | X | X | X |

CyberHarem/katua_fireemblem

2024-01-17T11:35:07+00:00

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

2024-01-17T12:32:06+00:00

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

Dataset of katua/カチュア/카츄아 (Fire Emblem) ======================================= This is the dataset of katua/カチュア/카츄아 (Fire Emblem), containing 270 images and their tags. The core tags of this character are 'blue\_hair, short\_hair, blue\_eyes, headband, 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

8c33020dcdc2e20305cf14f42cbff0dfbb017542

# Dataset of elise/エリーゼ (Fire Emblem) This is the dataset of elise/エリーゼ (Fire Emblem), containing 335 images and their tags. The core tags of this character are `blonde_hair, long_hair, twintails, purple_eyes, bow, hair_bow, ribbon, drill_hair, purple_hair, hair_ribbon, 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 | 335 | 313.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elise_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 335 | 213.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elise_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 676 | 400.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elise_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 335 | 288.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elise_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 676 | 505.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elise_fireemblem/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/elise_fireemblem', 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 | 45 |  |  |  |  |  | 1girl, solo, gloves, smile, dress, open_mouth, staff, thighhighs, armor, thigh_boots | | 1 | 11 |  |  |  |  |  | 1girl, black_bow, open_mouth, bangs, white_background, white_rose, simple_background, solo, :d, black_dress, blush, looking_at_viewer, very_long_hair, black_gloves, earrings, two-tone_hair, upper_body, black_capelet, holding_staff, long_sleeves, pink_bowtie | | 2 | 5 |  |  |  |  |  | 1girl, ninja, official_alternate_costume, open_mouth, red_scarf, bangs, looking_at_viewer, obi, :d, bare_shoulders, black_gloves, blush, fingerless_gloves, multicolored_hair, solo, 2girls, earrings, holding, scroll, shuriken, sidelocks, simple_background, sleeveless_kimono, upper_body, very_long_hair | | 3 | 7 |  |  |  |  |  | 1girl, navel, solo, blush, nipples, medium_breasts, open_mouth, completely_nude, looking_at_viewer, one_eye_closed, smile | | 4 | 8 |  |  |  |  |  | 1girl, hetero, nipples, solo_focus, 1boy, completely_nude, open_mouth, pussy, sex, navel, penis, blush, girl_on_top, small_breasts, smile, spread_legs, vaginal, cowgirl_position, medium_breasts, uncensored | | 5 | 6 |  |  |  |  |  | 1boy, 1girl, hetero, sex, solo_focus, vaginal, cum_in_pussy, gloves, nipples, penis, open_mouth, small_breasts, thighhighs, blush, mosaic_censoring | | 6 | 7 |  |  |  |  |  | open_mouth, smile, black_bow, blush, multicolored_hair, flower_necklace, small_breasts, very_long_hair, 1girl, 2girls, bikini, black_one-piece_swimsuit, blue_sky, casual_one-piece_swimsuit, closed_eyes, cloud, day, outdoors, water | | 7 | 6 |  |  |  |  |  | 1girl, bondage, solo, arms_behind_back, gagged, rope, shibari, improvised_gag, thighhighs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | gloves | smile | dress | open_mouth | staff | thighhighs | armor | thigh_boots | black_bow | bangs | white_background | white_rose | simple_background | :d | black_dress | blush | looking_at_viewer | very_long_hair | black_gloves | earrings | two-tone_hair | upper_body | black_capelet | holding_staff | long_sleeves | pink_bowtie | ninja | official_alternate_costume | red_scarf | obi | bare_shoulders | fingerless_gloves | multicolored_hair | 2girls | holding | scroll | shuriken | sidelocks | sleeveless_kimono | navel | nipples | medium_breasts | completely_nude | one_eye_closed | hetero | solo_focus | 1boy | pussy | sex | penis | girl_on_top | small_breasts | spread_legs | vaginal | cowgirl_position | uncensored | cum_in_pussy | mosaic_censoring | flower_necklace | bikini | black_one-piece_swimsuit | blue_sky | casual_one-piece_swimsuit | closed_eyes | cloud | day | outdoors | water | bondage | arms_behind_back | gagged | rope | shibari | improvised_gag | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------|:--------|:--------|:-------------|:--------|:-------------|:--------|:--------------|:------------|:--------|:-------------------|:-------------|:--------------------|:-----|:--------------|:--------|:--------------------|:-----------------|:---------------|:-----------|:----------------|:-------------|:----------------|:----------------|:---------------|:--------------|:--------|:-----------------------------|:------------|:------|:-----------------|:--------------------|:--------------------|:---------|:----------|:---------|:-----------|:------------|:--------------------|:--------|:----------|:-----------------|:------------------|:-----------------|:---------|:-------------|:-------|:--------|:------|:--------|:--------------|:----------------|:--------------|:----------|:-------------------|:-------------|:---------------|:-------------------|:------------------|:---------|:---------------------------|:-----------|:----------------------------|:--------------|:--------|:------|:-----------|:--------|:----------|:-------------------|:---------|:-------|:----------|:-----------------| | 0 | 45 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 |  |  |  |  |  | X | X | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 |  |  |  |  |  | X | 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 | 7 |  |  |  |  |  | X | X | | X | | X | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | 5 | 6 |  |  |  |  |  | X | | X | | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | | X | X | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | 6 | 7 |  |  |  |  |  | X | | | X | | X | | | | | X | | | | | | | X | | X | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | 7 | 6 |  |  |  |  |  | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |

CyberHarem/elise_fireemblem

2024-01-17T11:35:16+00:00

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

2024-01-17T12:56:30+00:00

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

Dataset of elise/エリーゼ (Fire Emblem) =================================== This is the dataset of elise/エリーゼ (Fire Emblem), containing 335 images and their tags. The core tags of this character are 'blonde\_hair, long\_hair, twintails, purple\_eyes, bow, hair\_bow, ribbon, drill\_hair, purple\_hair, hair\_ribbon, 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

40056ec4725839d3a4e765f374b40158ff7773aa

# Dataset of wayu/ワユ (Fire Emblem) This is the dataset of wayu/ワユ (Fire Emblem), containing 500 images and their tags. The core tags of this character are `long_hair, green_eyes, blue_hair, hairband, white_hairband, breasts, ahoge, purple_hair, headband`, 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 | 620.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wayu_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 370.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wayu_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1194 | 781.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wayu_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 558.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wayu_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1194 | 1.05 GiB | [Download](https://huggingface.co/datasets/CyberHarem/wayu_fireemblem/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/wayu_fireemblem', 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 |  |  |  |  |  | 1girl, fingerless_gloves, holding_sword, looking_at_viewer, smile, solo, detached_sleeves, armor, belt, simple_background, thighhighs, closed_mouth | | 1 | 13 |  |  |  |  |  | 1girl, smile, solo, fingerless_gloves, sword, simple_background, thighhighs, belt, detached_sleeves, bare_shoulders, white_background, open_mouth, zettai_ryouiki | | 2 | 5 |  |  |  |  |  | 1girl, detached_sleeves, fingerless_gloves, holding_sword, navel, official_alternate_costume, smile, solo, thighhighs, cape, looking_at_viewer, open_mouth, short_shorts, midriff, sheathed, white_shorts, bare_shoulders, belt, medium_breasts, simple_background, white_background, white_gloves | | 3 | 9 |  |  |  |  |  | 1girl, belt, halloween_costume, navel_cutout, solo, white_headband, witch_hat, detached_sleeves, fingerless_gloves, open_mouth, black_gloves, garter_straps, broom_riding, thighhighs | | 4 | 8 |  |  |  |  |  | 1girl, blue_sky, day, hair_flower, orange_bikini, solo, cloud, navel, cleavage, looking_at_viewer, grin, armpits, holding, large_breasts, open_mouth, thigh_strap, wristband | | 5 | 13 |  |  |  |  |  | hair_flower, orange_bikini, 1girl, smile, solo, medium_breasts, simple_background, cleavage, looking_at_viewer, navel, white_background, open_mouth, wristband, official_alternate_costume, orange_flower, thigh_strap, holding | | 6 | 9 |  |  |  |  |  | 1girl, blush, hair_flower, hetero, orange_bikini, penis, sex, solo_focus, vaginal, nipples, open_mouth, 1boy, cum_in_pussy, spread_legs, thigh_strap, breasts_out, day, large_breasts, mosaic_censoring, navel, orange_flower, outdoors, clothes_lift, ejaculation, wristband, bangs, blue_sky, clothing_aside, cloud, stomach | | 7 | 7 |  |  |  |  |  | 1girl, cum_in_pussy, gangbang, hetero, multiple_penises, nipples, solo_focus, thighhighs, vaginal, 3boys, blush, ejaculation, spread_legs, clothed_female_nude_male, cum_on_breasts, large_breasts, open_mouth, breast_grab, breasts_out, clothed_sex, detached_sleeves, facial, fingerless_gloves, grabbing, testicles, bar_censor, belt, gloved_handjob, mosaic_censoring, rape | | 8 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, open_mouth, penis, solo_focus, thighhighs, vaginal, breasts_out, clothed_female_nude_male, clothed_sex, fingerless_gloves, medium_breasts, mosaic_censoring, spread_legs, belt, cum_in_pussy, elbow_gloves, lying, black_gloves, detached_sleeves, large_breasts, tears | | 9 | 6 |  |  |  |  |  | 1girl, navel, nipples, smile, solo, pussy, blush, looking_at_viewer, medium_breasts, simple_background, bar_censor, completely_nude, large_breasts, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fingerless_gloves | holding_sword | looking_at_viewer | smile | solo | detached_sleeves | armor | belt | simple_background | thighhighs | closed_mouth | sword | bare_shoulders | white_background | open_mouth | zettai_ryouiki | navel | official_alternate_costume | cape | short_shorts | midriff | sheathed | white_shorts | medium_breasts | white_gloves | halloween_costume | navel_cutout | white_headband | witch_hat | black_gloves | garter_straps | broom_riding | blue_sky | day | hair_flower | orange_bikini | cloud | cleavage | grin | armpits | holding | large_breasts | thigh_strap | wristband | orange_flower | blush | hetero | penis | sex | solo_focus | vaginal | nipples | 1boy | cum_in_pussy | spread_legs | breasts_out | mosaic_censoring | outdoors | clothes_lift | ejaculation | bangs | clothing_aside | stomach | gangbang | multiple_penises | 3boys | clothed_female_nude_male | cum_on_breasts | breast_grab | clothed_sex | facial | grabbing | testicles | bar_censor | gloved_handjob | rape | elbow_gloves | lying | tears | pussy | completely_nude | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:----------------|:--------------------|:--------|:-------|:-------------------|:--------|:-------|:--------------------|:-------------|:---------------|:--------|:-----------------|:-------------------|:-------------|:-----------------|:--------|:-----------------------------|:-------|:---------------|:----------|:-----------|:---------------|:-----------------|:---------------|:--------------------|:---------------|:-----------------|:------------|:---------------|:----------------|:---------------|:-----------|:------|:--------------|:----------------|:--------|:-----------|:-------|:----------|:----------|:----------------|:--------------|:------------|:----------------|:--------|:---------|:--------|:------|:-------------|:----------|:----------|:-------|:---------------|:--------------|:--------------|:-------------------|:-----------|:---------------|:--------------|:--------|:-----------------|:----------|:-----------|:-------------------|:--------|:---------------------------|:-----------------|:--------------|:--------------|:---------|:-----------|:------------|:-------------|:-----------------|:-------|:---------------|:--------|:--------|:--------|:------------------| | 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 13 |  |  |  |  |  | X | X | | | X | X | X | | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | X | X | X | | | X | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 9 |  |  |  |  |  | X | X | | | | X | X | | X | | X | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 13 |  |  |  |  |  | X | | | X | X | X | | | | X | | | | | X | X | | X | X | | | | | | X | | | | | | | | | | | X | X | | X | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 9 |  |  |  |  |  | X | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | X | X | X | 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 |  |  |  |  |  | X | X | | | | | X | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | 6 |  |  |  |  |  | X | 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 | 6 |  |  |  |  |  | X | | | X | X | X | | | | X | | | | | X | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X |

CyberHarem/wayu_fireemblem

2024-01-17T11:35:24+00:00

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

2024-01-17T13:17:09+00:00

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

Dataset of wayu/ワユ (Fire Emblem) ================================ This is the dataset of wayu/ワユ (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'long\_hair, green\_eyes, blue\_hair, hairband, white\_hairband, breasts, ahoge, purple\_hair, headband', 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

f9b83e1776ed4d269129750ef6f7e7f9e22bccc9

# Dataset of eirik/エイリーク (Fire Emblem) This is the dataset of eirik/エイリーク (Fire Emblem), containing 500 images and their tags. The core tags of this character are `long_hair, blue_eyes, aqua_hair, blue_hair, bangs, breasts, aqua_eyes, 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 | 718.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eirik_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 409.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eirik_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1192 | 846.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eirik_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 639.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/eirik_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1192 | 1.16 GiB | [Download](https://huggingface.co/datasets/CyberHarem/eirik_fireemblem/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/eirik_fireemblem', 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 |  |  |  |  |  | 1girl, breastplate, closed_mouth, red_shirt, solo, upper_body, looking_at_viewer, pauldrons, simple_background, smile, short_sleeves, cape, earrings, hair_between_eyes, white_background | | 1 | 14 |  |  |  |  |  | 1girl, breastplate, cape, holding_sword, miniskirt, red_gloves, thigh_boots, white_skirt, zettai_ryouiki, fingerless_gloves, solo, looking_at_viewer, short_sleeves, shoulder_armor, red_footwear, red_shirt, earrings, full_body, closed_mouth, rapier, red_thighhighs, hair_between_eyes, standing, simple_background, smile, white_background | | 2 | 5 |  |  |  |  |  | 1girl, armor, cape, rapier, skirt, solo, thighhighs, earrings, thigh_boots, zettai_ryouiki, white_background, fingerless_gloves | | 3 | 6 |  |  |  |  |  | 1girl, breastplate, cape, looking_at_viewer, red_gloves, shoulder_armor, solo, shirt, white_skirt, fingerless_gloves, hair_between_eyes, short_sleeves, holding_sword, smile | | 4 | 6 |  |  |  |  |  | 1girl, breastplate, fingerless_gloves, looking_at_viewer, red_gloves, shoulder_armor, solo, upper_body, earrings, open_mouth, red_shirt, cape, simple_background, smile, blush, hair_between_eyes, shiny_hair | | 5 | 6 |  |  |  |  |  | 1girl, alternate_hairstyle, breastplate, cosplay, ponytail, solo, upper_body, cape, closed_mouth, looking_at_viewer, official_alternate_costume, simple_background, smile, earrings, white_background | | 6 | 25 |  |  |  |  |  | 1girl, solo, breastplate, cape, looking_at_viewer, ponytail, cosplay, holding_polearm, official_alternate_costume, spear, earrings, brown_gloves, thighhighs, skirt, smile, alternate_hairstyle, belt, shoulder_armor, boots, hair_ornament, simple_background | | 7 | 11 |  |  |  |  |  | 1girl, christmas, fur_trim, santa_costume, santa_hat, smile, cape, looking_at_viewer, red_gloves, solo, dress, official_alternate_costume, open_mouth, red_headwear, blush, holding_staff, skirt | | 8 | 9 |  |  |  |  |  | 1girl, hair_flower, smile, solo, earrings, looking_at_viewer, simple_background, white_background, bare_shoulders, closed_mouth, wedding_dress, white_dress | | 9 | 17 |  |  |  |  |  | 1girl, hair_flower, official_alternate_costume, smile, yellow_bikini, solo, navel, cleavage, off-shoulder_bikini, bare_shoulders, looking_at_viewer, bracelet, hibiscus, necklace, two-tone_bikini, open_mouth, blush, layered_bikini, bikini_skirt, innertube, medium_breasts, simple_background, white_background | | 10 | 11 |  |  |  |  |  | 1girl, black_bikini, cleavage, hair_flower, large_breasts, official_alternate_costume, solo, looking_at_viewer, smile, cloud, navel, ocean, outdoors, blue_sky, dagger, day, beach, hair_between_eyes, hibiscus, water, closed_mouth, holding, parted_lips, sheathed, black_capelet, collarbone, cowboy_shot, red_flower, sitting | | 11 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, nipples, blush, solo, barefoot, completely_nude, smile, ass, full_body, large_breasts, medium_breasts, navel, dakimakura_(medium), on_back | | 12 | 10 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, penis, blush, large_breasts, open_mouth, vaginal, solo_focus, thighhighs, cum_in_pussy, mosaic_censoring, spread_legs, straddling, breasts_out, clothed_sex, girl_on_top, navel, panties | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | breastplate | closed_mouth | red_shirt | solo | upper_body | looking_at_viewer | pauldrons | simple_background | smile | short_sleeves | cape | earrings | hair_between_eyes | white_background | holding_sword | miniskirt | red_gloves | thigh_boots | white_skirt | zettai_ryouiki | fingerless_gloves | shoulder_armor | red_footwear | full_body | rapier | red_thighhighs | standing | armor | skirt | thighhighs | shirt | open_mouth | blush | shiny_hair | alternate_hairstyle | cosplay | ponytail | official_alternate_costume | holding_polearm | spear | brown_gloves | belt | boots | hair_ornament | christmas | fur_trim | santa_costume | santa_hat | dress | red_headwear | holding_staff | hair_flower | bare_shoulders | wedding_dress | white_dress | yellow_bikini | navel | cleavage | off-shoulder_bikini | bracelet | hibiscus | necklace | two-tone_bikini | layered_bikini | bikini_skirt | innertube | medium_breasts | black_bikini | large_breasts | cloud | ocean | outdoors | blue_sky | dagger | day | beach | water | holding | parted_lips | sheathed | black_capelet | collarbone | cowboy_shot | red_flower | sitting | nipples | barefoot | completely_nude | ass | dakimakura_(medium) | on_back | 1boy | hetero | penis | vaginal | solo_focus | cum_in_pussy | mosaic_censoring | spread_legs | straddling | breasts_out | clothed_sex | girl_on_top | panties | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:---------------|:------------|:-------|:-------------|:--------------------|:------------|:--------------------|:--------|:----------------|:-------|:-----------|:--------------------|:-------------------|:----------------|:------------|:-------------|:--------------|:--------------|:-----------------|:--------------------|:-----------------|:---------------|:------------|:---------|:-----------------|:-----------|:--------|:--------|:-------------|:--------|:-------------|:--------|:-------------|:----------------------|:----------|:-----------|:-----------------------------|:------------------|:--------|:---------------|:-------|:--------|:----------------|:------------|:-----------|:----------------|:------------|:--------|:---------------|:----------------|:--------------|:-----------------|:----------------|:--------------|:----------------|:--------|:-----------|:----------------------|:-----------|:-----------|:-----------|:------------------|:-----------------|:---------------|:------------|:-----------------|:---------------|:----------------|:--------|:--------|:-----------|:-----------|:---------|:------|:--------|:--------|:----------|:--------------|:-----------|:----------------|:-------------|:--------------|:-------------|:----------|:----------|:-----------|:------------------|:------|:----------------------|:----------|:-------|:---------|:--------|:----------|:-------------|:---------------|:-------------------|:--------------|:-------------|:--------------|:--------------|:--------------|:----------| 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| | | X | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 |  |  |  |  |  | X | | X | | X | | X | | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 17 |  |  |  |  |  | X | | | | X | | X | | X | X | | | | | X | | | | | | | | | | | | | | | | | | X | X | | | | | X | | | | | | | | | | | | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 11 |  |  |  |  |  | X | | X | | X | | X | | | X | | | | 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 | 8 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CyberHarem/eirik_fireemblem

2024-01-17T11:35:47+00:00

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

2024-01-17T13:34:22+00:00

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

Dataset of eirik/エイリーク (Fire Emblem) ==================================== This is the dataset of eirik/エイリーク (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'long\_hair, blue\_eyes, aqua\_hair, blue\_hair, bangs, breasts, aqua\_eyes, 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

f0a4329cf9f9027b095be7743a6db068fa425e5f

This is a cleaned version from the Quora dataset that solves a single problem with the original dataset - sentences can have multiple ids. We also removed 2 rows from the original dataset that contained an empty string - this causes problem when trying to run an embedding because a empty string cannot... be embedded by OpenAI and has no valid representation? Created to minimise data leakage from your train, test and validation sets by allowing you to segregate and split by ID.

567-labs/cleaned-quora-dataset

2024-01-17T11:44:18+00:00

{"language": ["en"], "license": "mit", "dataset_info": {"features": [{"name": "questions", "struct": [{"name": "id", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "is_duplicate", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 61389323, "num_examples": 404288}], "download_size": 36181628, "dataset_size": 61389323}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-01-29T14:31:06+00:00

TAGS #language-English #license-mit #region-us

This is a cleaned version from the Quora dataset that solves a single problem with the original dataset - sentences can have multiple ids. We also removed 2 rows from the original dataset that contained an empty string - this causes problem when trying to run an embedding because a empty string cannot... be embedded by OpenAI and has no valid representation? Created to minimise data leakage from your train, test and validation sets by allowing you to segregate and split by ID.

943afd821c0f96ad9e0b6472ee60c10b668f81ae

# Dataset of felicia/フェリシア (Fire Emblem) This is the dataset of felicia/フェリシア (Fire Emblem), containing 258 images and their tags. The core tags of this character are `long_hair, pink_hair, blue_eyes, maid_headdress, ponytail, breasts, 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 | 258 | 288.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/felicia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 258 | 175.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/felicia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 572 | 350.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/felicia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 258 | 259.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/felicia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 572 | 480.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/felicia_fireemblem/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/felicia_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 |  |  |  |  |  | 1girl, solo, long_sleeves, maid, smile, looking_at_viewer, puffy_sleeves, simple_background, open_mouth, white_background, apron, upper_body | | 1 | 8 |  |  |  |  |  | 1girl, solo, knife, maid_apron, simple_background, weapon, black_thighhighs, holding, white_background, long_sleeves, open_mouth, smile, full_body, looking_at_viewer, puffy_sleeves, zettai_ryouiki, bridal_gauntlets, gem | | 2 | 6 |  |  |  |  |  | 1boy, 1girl, blush, completely_nude, hetero, large_breasts, solo_focus, nipples, open_mouth, tongue_out, ahegao, heart-shaped_pupils, sweat, blunt_bangs, collarbone, doggystyle, saliva, sex_from_behind, simple_background, smile | | 3 | 8 |  |  |  |  |  | 1boy, 1girl, hetero, open_mouth, sex, thighhighs, blush, maid, solo_focus, vaginal, large_breasts, nipples, penis, breasts_out, cum_in_pussy, spread_legs, uncensored, bar_censor, girl_on_top, heart-shaped_pupils, long_sleeves, lying, straddling | | 4 | 6 |  |  |  |  |  | 1boy, blush, completely_nude, hetero, medium_breasts, navel, nipples, open_mouth, penis, sex, vaginal, 1girl, sweat, blunt_bangs, solo_focus, uncensored, cum_in_pussy | | 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, nipples, blunt_bangs, cum_in_mouth, large_breasts, looking_at_viewer, nude, pov, maid, mosaic_censoring, open_mouth, oral, paizuri, smile, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | long_sleeves | maid | smile | looking_at_viewer | puffy_sleeves | simple_background | open_mouth | white_background | apron | upper_body | knife | maid_apron | weapon | black_thighhighs | holding | full_body | zettai_ryouiki | bridal_gauntlets | gem | 1boy | blush | completely_nude | hetero | large_breasts | solo_focus | nipples | tongue_out | ahegao | heart-shaped_pupils | sweat | blunt_bangs | collarbone | doggystyle | saliva | sex_from_behind | sex | thighhighs | vaginal | penis | breasts_out | cum_in_pussy | spread_legs | uncensored | bar_censor | girl_on_top | lying | straddling | medium_breasts | navel | cum_in_mouth | nude | pov | mosaic_censoring | oral | paizuri | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:-------|:--------|:--------------------|:----------------|:--------------------|:-------------|:-------------------|:--------|:-------------|:--------|:-------------|:---------|:-------------------|:----------|:------------|:-----------------|:-------------------|:------|:-------|:--------|:------------------|:---------|:----------------|:-------------|:----------|:-------------|:---------|:----------------------|:--------|:--------------|:-------------|:-------------|:---------|:------------------|:------|:-------------|:----------|:--------|:--------------|:---------------|:--------------|:-------------|:-------------|:--------------|:--------|:-------------|:-----------------|:--------|:---------------|:-------|:------|:-------------------|:-------|:----------| | 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 |  |  |  |  |  | X | X | X | | X | X | X | X | X | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 |  |  |  |  |  | X | | | | X | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 3 | 8 |  |  |  |  |  | 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 |  |  |  |  |  | X | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | | X | X | | | | X | X | | | | | X | | X | X | | X | | X | | | | | X | X | | | | | | | | 5 | 5 |  |  |  |  |  | X | | | X | X | X | | | X | | | | | | | | | | | | | X | X | | X | X | X | X | X | | | | X | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X |

CyberHarem/felicia_fireemblem

2024-01-17T11:49:58+00:00

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

2024-01-17T12:51:37+00:00

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

Dataset of felicia/フェリシア (Fire Emblem) ====================================== This is the dataset of felicia/フェリシア (Fire Emblem), containing 258 images and their tags. The core tags of this character are 'long\_hair, pink\_hair, blue\_eyes, maid\_headdress, ponytail, breasts, 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

ed43c565e4d2a8d9d9db4e05ee6591b6f7061d99

# Dataset of olivia/オリヴィエ (Fire Emblem) This is the dataset of olivia/オリヴィエ (Fire Emblem), containing 397 images and their tags. The core tags of this character are `long_hair, pink_hair, braid, ponytail, twin_braids, hairband, breasts, pink_eyes, side_braid, 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 | 397 | 465.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/olivia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 397 | 270.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/olivia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 881 | 552.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/olivia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 397 | 411.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/olivia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 881 | 766.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/olivia_fireemblem/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/olivia_fireemblem', 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 |  |  |  |  |  | 1girl, simple_background, solo, bare_shoulders, closed_mouth, midriff, navel, blush, detached_sleeves, looking_at_viewer, o-ring, smile, twitter_username, upper_body, white_background, dancer, cleavage, grey_background | | 1 | 7 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, navel, simple_background, smile, solo, dancer, midriff, bridal_gauntlets, full_body, sandals, thighhighs, white_background | | 2 | 10 |  |  |  |  |  | 1girl, ass, bare_shoulders, looking_at_viewer, smile, solo, see-through, simple_background, dancer, looking_back, from_behind, full_body | | 3 | 5 |  |  |  |  |  | 1girl, ass, blush, from_behind, looking_at_viewer, looking_back, simple_background, solo, white_background, bare_shoulders, dancer, black_panties, bridal_gauntlets, thighhighs | | 4 | 5 |  |  |  |  |  | 1girl, cleavage, fake_animal_ears, playboy_bunny, rabbit_ears, solo, alternate_costume, black_pantyhose, detached_collar, strapless_leotard, wrist_cuffs, bare_shoulders, full_body, high_heels, looking_at_viewer, open_mouth, simple_background, white_background, black_leotard, blush, bowtie, covered_navel, necktie, smile, white_leotard | | 5 | 7 |  |  |  |  |  | 1boy, 1girl, blush, cum_in_pussy, hetero, penis, sex, vaginal, nipples, solo_focus, large_breasts, open_mouth, spread_legs, mosaic_censoring, nude, purple_eyes, thighhighs | | 6 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, panties_aside, penis, sex, solo_focus, vaginal, nipples, open_mouth, pussy, spread_legs, thighhighs, white_panties, censored, large_breasts, navel, purple_eyes, sweat | | 7 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, faceless_male, heart, simple_background, sweat, tongue_out, kiss, open_mouth, bare_shoulders, cleavage, armpits, bald, bridal_gauntlets, licking_armpit, midriff, navel, nude, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | simple_background | solo | bare_shoulders | closed_mouth | midriff | navel | blush | detached_sleeves | looking_at_viewer | o-ring | smile | twitter_username | upper_body | white_background | dancer | cleavage | grey_background | bridal_gauntlets | full_body | sandals | thighhighs | ass | see-through | looking_back | from_behind | black_panties | fake_animal_ears | playboy_bunny | rabbit_ears | alternate_costume | black_pantyhose | detached_collar | strapless_leotard | wrist_cuffs | high_heels | open_mouth | black_leotard | bowtie | covered_navel | necktie | white_leotard | 1boy | cum_in_pussy | hetero | penis | sex | vaginal | nipples | solo_focus | large_breasts | spread_legs | mosaic_censoring | nude | purple_eyes | panties_aside | pussy | white_panties | censored | sweat | faceless_male | heart | tongue_out | kiss | armpits | bald | licking_armpit | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------------|:---------------|:----------|:--------|:--------|:-------------------|:--------------------|:---------|:--------|:-------------------|:-------------|:-------------------|:---------|:-----------|:------------------|:-------------------|:------------|:----------|:-------------|:------|:--------------|:---------------|:--------------|:----------------|:-------------------|:----------------|:--------------|:--------------------|:------------------|:------------------|:--------------------|:--------------|:-------------|:-------------|:----------------|:---------|:----------------|:----------|:----------------|:-------|:---------------|:---------|:--------|:------|:----------|:----------|:-------------|:----------------|:--------------|:-------------------|:-------|:--------------|:----------------|:--------|:----------------|:-----------|:--------|:----------------|:--------|:-------------|:-------|:----------|:-------|:-----------------| | 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 |  |  |  |  |  | X | X | X | X | | X | X | | | X | | X | | | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 10 |  |  |  |  |  | X | X | X | X | | | | | | X | | X | | | | X | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | X | X | X | | | | X | | X | | | | | X | X | | | X | | | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | X | 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 | 7 |  |  |  |  |  | X | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 6 | 9 |  |  |  |  |  | X | | | | | | X | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | X | | X | X | X | X | X | X | X | X | | | X | X | X | X | X | X | | | | | | | | | 7 | 8 |  |  |  |  |  | X | X | | X | | X | X | X | | | | | | X | | | X | | X | | | | | | | | | | | | | | | | | | X | | | | | | X | | X | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X |

CyberHarem/olivia_fireemblem

2024-01-17T11:50:02+00:00

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

2024-01-17T13:17:33+00:00

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

Dataset of olivia/オリヴィエ (Fire Emblem) ===================================== This is the dataset of olivia/オリヴィエ (Fire Emblem), containing 397 images and their tags. The core tags of this character are 'long\_hair, pink\_hair, braid, ponytail, twin\_braids, hairband, breasts, pink\_eyes, side\_braid, 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

dfcc9898ac587e21245e006751ed4583e027163f

# Dataset of paola/パオラ (Fire Emblem) This is the dataset of paola/パオラ (Fire Emblem), containing 466 images and their tags. The core tags of this character are `green_hair, long_hair, green_eyes, breasts, headband, 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 | 466 | 520.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/paola_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 466 | 320.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/paola_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1059 | 648.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/paola_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 466 | 474.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/paola_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1059 | 872.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/paola_fireemblem/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/paola_fireemblem', 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 |  |  |  |  |  | 1girl, elbow_gloves, green_footwear, solo, pegasus_knight_uniform_(fire_emblem), thigh_boots, green_skirt, dress, green_thighhighs, sitting, smile | | 1 | 18 |  |  |  |  |  | 1girl, smile, solo, breastplate, looking_at_viewer, upper_body, simple_background, shoulder_armor, white_background, blush, elbow_gloves, closed_mouth | | 2 | 11 |  |  |  |  |  | 1girl, dress, elbow_gloves, solo, thighhighs, green_gloves, side_slit, bare_shoulders, blush, simple_background, white_background, looking_at_viewer, polearm, sleeveless | | 3 | 5 |  |  |  |  |  | 1girl, bangs, belt, breastplate, capelet, elbow_gloves, full_body, open_mouth, pelvic_curtain, shoulder_armor, solo, thigh_boots, thighhighs, thighs, white_footwear, white_gloves, gold_trim, shiny_hair, sleeveless, white_dress, holding_sword, medium_breasts, simple_background, white_background, hand_on_own_chest, hand_up, high_heel_boots, looking_at_viewer, sheathed, short_dress, smile, standing, transparent_background | | 4 | 11 |  |  |  |  |  | 1girl, nipples, nude, smile, solo, looking_at_viewer, blush, navel | | 5 | 6 |  |  |  |  |  | 1girl, blush, completely_nude, navel, nipples, spread_legs, bangs, female_pubic_hair, mosaic_censoring, on_back, open_mouth, solo, looking_at_viewer, pillow, smile, spread_pussy | | 6 | 5 |  |  |  |  |  | 1girl, blush, navel, nipples, open_mouth, solo, sweat, armpits, arms_behind_head, arms_up, bangs, completely_nude, female_pubic_hair, looking_at_viewer, cowboy_shot, very_long_hair, white_headband | | 7 | 14 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, nipples, penis, thighhighs, vaginal, blush, cum_in_pussy, elbow_gloves, open_mouth, female_pubic_hair, mosaic_censoring, girl_on_top, spread_legs, torn_clothes, breasts_out, clothed_sex, cowgirl_position, medium_breasts, tears, rape | | 8 | 8 |  |  |  |  |  | 1girl, navel, solo, cleavage, looking_at_viewer, collarbone, simple_background, smile, white_background, bangs, bare_shoulders, blush, green_bikini, stomach, armpits, thigh_gap | | 9 | 5 |  |  |  |  |  | 1girl, bare_shoulders, smile, solo, blush, elbow_gloves, hair_flower, looking_at_viewer, simple_background, white_background, white_gloves, bangs, official_alternate_costume, green_dress, green_footwear, medium_breasts, open_mouth, skirt, sleeveless_dress, thigh_boots, thighhighs, upper_body | | 10 | 36 |  |  |  |  |  | fake_animal_ears, rabbit_ears, 1girl, solo, cleavage, playboy_bunny, smile, pantyhose, white_gloves, choker, hair_flower, rabbit_tail, leotard, simple_background, looking_at_viewer, blush, open_mouth, see-through, white_background | | 11 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, nude, open_mouth, blush, censored, licking_penis, tongue_out, bangs, cum, sweat | | 12 | 5 |  |  |  |  |  | 1girl, anus, blush, elbow_gloves, female_pubic_hair, outdoors, pussy, thighhighs, bar_censor, looking_at_viewer, no_panties, open_mouth, solo, squatting, thigh_boots, white_headband, day, peeing, sky, ass, bare_shoulders, brown_gloves, bush, dress, green_footwear, green_gloves, looking_back, sleeveless, spread_legs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | green_footwear | solo | pegasus_knight_uniform_(fire_emblem) | thigh_boots | green_skirt | dress | green_thighhighs | sitting | smile | breastplate | looking_at_viewer | upper_body | simple_background | shoulder_armor | white_background | blush | closed_mouth | thighhighs | green_gloves | side_slit | bare_shoulders | polearm | sleeveless | bangs | belt | capelet | full_body | open_mouth | pelvic_curtain | thighs | white_footwear | white_gloves | gold_trim | shiny_hair | white_dress | holding_sword | medium_breasts | hand_on_own_chest | hand_up | high_heel_boots | sheathed | short_dress | standing | transparent_background | nipples | nude | navel | completely_nude | spread_legs | female_pubic_hair | mosaic_censoring | on_back | pillow | spread_pussy | sweat | armpits | arms_behind_head | arms_up | cowboy_shot | very_long_hair | white_headband | 1boy | hetero | solo_focus | penis | vaginal | cum_in_pussy | girl_on_top | torn_clothes | breasts_out | clothed_sex | cowgirl_position | tears | rape | cleavage | collarbone | green_bikini | stomach | thigh_gap | hair_flower | official_alternate_costume | green_dress | skirt | sleeveless_dress | fake_animal_ears | rabbit_ears | playboy_bunny | pantyhose | choker | rabbit_tail | leotard | see-through | censored | licking_penis | tongue_out | cum | anus | outdoors | pussy | bar_censor | no_panties | squatting | day | peeing | sky | ass | brown_gloves | bush | looking_back | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:-----------------|:-------|:---------------------------------------|:--------------|:--------------|:--------|:-------------------|:----------|:--------|:--------------|:--------------------|:-------------|:--------------------|:-----------------|:-------------------|:--------|:---------------|:-------------|:---------------|:------------|:-----------------|:----------|:-------------|:--------|:-------|:----------|:------------|:-------------|:-----------------|:---------|:-----------------|:---------------|:------------|:-------------|:--------------|:----------------|:-----------------|:--------------------|:----------|:------------------|:-----------|:--------------|:-----------|:-------------------------|:----------|:-------|:--------|:------------------|:--------------|:--------------------|:-------------------|:----------|:---------|:---------------|:--------|:----------|:-------------------|:----------|:--------------|:-----------------|:-----------------|:-------|:---------|:-------------|:--------|:----------|:---------------|:--------------|:---------------|:--------------|:--------------|:-------------------|:--------|:-------|:-----------|:-------------|:---------------|:----------|:------------|:--------------|:-----------------------------|:--------------|:--------|:-------------------|:-------------------|:--------------|:----------------|:------------|:---------|:--------------|:----------|:--------------|:-----------|:----------------|:-------------|:------|:-------|:-----------|:--------|:-------------|:-------------|:------------|:------|:---------|:------|:------|:---------------|:-------|:---------------| | 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 18 |  |  |  |  |  | X | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 11 |  |  |  |  |  | X | X | | X | | | | X | | | | | X | | X | | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | X | | X | | X | | | | | X | X | X | | X | X | 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 |  |  |  |  |  | X | | | X | | | | | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 |  |  |  |  |  | X | | | X | | | | | | | X | | X | | | | | X | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 |  |  |  |  |  | X | | | X | | | | | | | | | X | | | | | X | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | X | | X | X | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 14 |  |  |  |  |  | X | X | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | X | | | | | | | | | X | | | | | | | | X | | | | X | X | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 8 |  |  |  |  |  | X | | | X | | | | | | | X | | X | | X | | X | X | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 |  |  |  |  |  | X | X | X | X | | X | | | | | X | | X | X | X | | X | X | | X | | | X | | | X | | | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 36 |  |  |  |  |  | X | | | X | | | | | | | X | | X | | X | | X | X | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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CyberHarem/paola_fireemblem

2024-01-17T11:50:10+00:00

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

2024-01-17T13:24:34+00:00

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

Dataset of paola/パオラ (Fire Emblem) ================================== This is the dataset of paola/パオラ (Fire Emblem), containing 466 images and their tags. The core tags of this character are 'green\_hair, long\_hair, green\_eyes, breasts, headband, 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

d2623048d49ddb62a45217bb0edb7e0e72c552d0

# Dataset of lucina/#ルキナ (Fire Emblem) This is the dataset of lucina/#ルキナ (Fire Emblem), containing 500 images and their tags. The core tags of this character are `blue_hair, blue_eyes, long_hair, hair_between_eyes, breasts, 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 | 655.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lucina_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 371.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lucina_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1138 | 759.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lucina_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 577.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lucina_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1138 | 1.05 GiB | [Download](https://huggingface.co/datasets/CyberHarem/lucina_fireemblem/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/lucina_fireemblem', 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 |  |  |  |  |  | 1girl, cape, falchion_(fire_emblem), solo, sword, tiara, fingerless_gloves, armor, looking_at_viewer, smile | | 1 | 11 |  |  |  |  |  | 1girl, cape, falchion_(fire_emblem), solo, tiara, armor, fingerless_gloves, looking_at_viewer, holding_sword, simple_background, belt | | 2 | 5 |  |  |  |  |  | 1girl, cape, simple_background, solo, tiara, white_background, looking_at_viewer, polearm, shield, armored_boots, full_body, holding_weapon, official_alternate_costume, shoulder_armor, smile | | 3 | 7 |  |  |  |  |  | 1girl, bare_shoulders, hair_flower, looking_at_viewer, official_alternate_costume, solo, white_dress, cleavage, sleeveless_dress, smile, blush, closed_mouth, collarbone, symbol-shaped_pupils, armlet, small_breasts | | 4 | 5 |  |  |  |  |  | 1girl, crop_top, looking_at_viewer, midriff, navel, short_shorts, solo, tiara, bare_shoulders, blush, official_alternate_costume, small_breasts, open_mouth, simple_background, sleeveless, thighs, white_background, :d, arm_up, armpits, belt, bikini, blue_shorts, innertube | | 5 | 7 |  |  |  |  |  | 1girl, day, navel, smile, solo, tiara, blue_bikini, crop_top, looking_at_viewer, midriff, outdoors, armpits, bare_shoulders, cloud, ocean, short_shorts, small_breasts, water, alternate_costume, arm_up, beach, belt, blue_sky, blush, closed_mouth, cowboy_shot, innertube, sleeveless, thighs, tree, wet | | 6 | 6 |  |  |  |  |  | 1girl, alternate_breast_size, large_breasts, smile, solo, looking_at_viewer, navel, tiara, blue_bikini, patreon_username | | 7 | 11 |  |  |  |  |  | 1girl, tiara, 1boy, hetero, nipples, penis, solo_focus, blush, navel, pussy, uncensored, completely_nude, spread_legs, looking_at_viewer, open_mouth, sex, vaginal, cum, pubic_hair, small_breasts, clitoris, lying, pov, sweat | | 8 | 18 |  |  |  |  |  | 1girl, looking_at_viewer, solo, alternate_costume, orange_shorts, short_shorts, tiara, waitress, beer_mug, smile, cleavage, employee_uniform, blush, medium_breasts, open_mouth, white_tank_top, chicken_(food), holding_plate, navel, tray | | 9 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, playboy_bunny, rabbit_ears, solo, fake_animal_ears, official_alternate_costume, smile, leotard, white_pantyhose, rabbit_tail, blush, cleavage, egg, open_mouth, simple_background, frills, small_breasts, choker, puffy_short_sleeves, white_background, white_gloves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cape | falchion_(fire_emblem) | solo | sword | tiara | fingerless_gloves | armor | looking_at_viewer | smile | holding_sword | simple_background | belt | white_background | polearm | shield | armored_boots | full_body | holding_weapon | official_alternate_costume | shoulder_armor | bare_shoulders | hair_flower | white_dress | cleavage | sleeveless_dress | blush | closed_mouth | collarbone | symbol-shaped_pupils | armlet | small_breasts | crop_top | midriff | navel | short_shorts | open_mouth | sleeveless | thighs | :d | arm_up | armpits | bikini | blue_shorts | innertube | day | blue_bikini | outdoors | cloud | ocean | water | alternate_costume | beach | blue_sky | cowboy_shot | tree | wet | alternate_breast_size | large_breasts | patreon_username | 1boy | hetero | nipples | penis | solo_focus | pussy | uncensored | completely_nude | spread_legs | sex | vaginal | cum | pubic_hair | clitoris | lying | pov | sweat | orange_shorts | waitress | beer_mug | employee_uniform | medium_breasts | white_tank_top | chicken_(food) | holding_plate | tray | playboy_bunny | rabbit_ears | fake_animal_ears | leotard | white_pantyhose | rabbit_tail | egg | frills | choker | puffy_short_sleeves | white_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------------------|:-------|:--------|:--------|:--------------------|:--------|:--------------------|:--------|:----------------|:--------------------|:-------|:-------------------|:----------|:---------|:----------------|:------------|:-----------------|:-----------------------------|:-----------------|:-----------------|:--------------|:--------------|:-----------|:-------------------|:--------|:---------------|:-------------|:-----------------------|:---------|:----------------|:-----------|:----------|:--------|:---------------|:-------------|:-------------|:---------|:-----|:---------|:----------|:---------|:--------------|:------------|:------|:--------------|:-----------|:--------|:--------|:--------|:--------------------|:--------|:-----------|:--------------|:-------|:------|:------------------------|:----------------|:-------------------|:-------|:---------|:----------|:--------|:-------------|:--------|:-------------|:------------------|:--------------|:------|:----------|:------|:-------------|:-----------|:--------|:------|:--------|:----------------|:-----------|:-----------|:-------------------|:-----------------|:-----------------|:-----------------|:----------------|:-------|:----------------|:--------------|:-------------------|:----------|:------------------|:--------------|:------|:---------|:---------|:----------------------|:---------------| | 0 | 27 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 |  |  |  |  |  | X | X | X | X | | X | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 |  |  |  |  |  | X | X | | X | | X | | | X | X | | X | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 |  |  |  |  |  | X | | | X | | | | | X | X | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | 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 | 7 |  |  |  |  |  | X | | | X | | X | | | X | X | | | X | | | | | | | | | X | | | | | 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 | 6 |  |  |  |  |  | X | | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 11 |  |  |  |  |  | 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 | 18 |  |  |  |  |  | X | | | X | | X | | | X | X | | | | | | | | | | | | | | | X | | X | | | | | | | | X | X | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 9 | 12 |  |  |  |  |  | X | | | X | | | | | X | X | | X | | X | | | | | | X | | | | | X | | X | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/lucina_fireemblem

2024-01-17T11:50:26+00:00

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

2024-01-17T13:39:21+00:00

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

Dataset of lucina/#ルキナ (Fire Emblem) ==================================== This is the dataset of lucina/#ルキナ (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'blue\_hair, blue\_eyes, long\_hair, hair\_between\_eyes, breasts, 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

9fb64fa5af6e3d4a2d0ebf3906980754f9e4dc6d

We collect a 2.5B training dataset from various domains for long-context continual pre-training. The composition of this dataset is as follows (partially inspired by [Long-Data-Collection](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections)): | Domain | Proportion | Source | | ------------- | ---------- | ------ | | Book | 40% | [Redpajama-Book](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | | Arxiv | 20% | [Redpajama-Arxiv](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | | General | 20% | [Redpajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | | Code | 10% | [LCC-Python](https://huggingface.co/datasets/microsoft/LCC_python) | | QA | 5% | [Natural Questions](https://ai.google.com/research/NaturalQuestions/) | | Summarization | 5% | [BookSum](https://github.com/salesforce/booksum) | We have also curated a test dataset comprising 250 million tokens, mirroring the same composition. The selection criteria ensured that the average n-gram similarity (for n=2, 3, 4) with the training set is below 10%. This threshold effectively excludes all QA and Summarization data, resulting in a test corpus where the distribution of tokens across Book, Arxiv, General, and Code categories follows a ratio of 4:2:2:1, respectively.

DAMO-NLP-SG/LongCorpus-2.5B

2024-01-17T11:56:31+00:00

{"license": "mit", "task_categories": ["text-generation"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train_*"}, {"split": "test", "path": "data/test_*"}]}]}

2024-01-19T18:15:18+00:00

TAGS #task_categories-text-generation #license-mit #region-us

We collect a 2.5B training dataset from various domains for long-context continual pre-training. The composition of this dataset is as follows (partially inspired by Long-Data-Collection): Domain: Book, Proportion: 40%, Source: Redpajama-Book Domain: Arxiv, Proportion: 20%, Source: Redpajama-Arxiv Domain: General, Proportion: 20%, Source: Redpajama Domain: Code, Proportion: 10%, Source: LCC-Python Domain: QA, Proportion: 5%, Source: Natural Questions Domain: Summarization, Proportion: 5%, Source: BookSum We have also curated a test dataset comprising 250 million tokens, mirroring the same composition. The selection criteria ensured that the average n-gram similarity (for n=2, 3, 4) with the training set is below 10%. This threshold effectively excludes all QA and Summarization data, resulting in a test corpus where the distribution of tokens across Book, Arxiv, General, and Code categories follows a ratio of 4:2:2:1, respectively.

844aa403486950f583e399578cb11f89fa05f87f

# Forecasting Future World Events with Neural Networks This is an (unofficial) repository for "[Forecasting Future World Events with Neural Networks](http://arxiv.org/abs/2206.15474)" by [Andy Zou](https://andyzoujm.github.io/), [Tristan Xiao](https://www.linkedin.com/in/tristan-xiao/), [Ryan Jia](https://www.linkedin.com/in/ryanjia/), [Joe Kwon](joekwon.io), [Mantas Mazeika](https://www.linkedin.com/in/mmazeika/), [Richard Li](https://www.linkedin.com/in/lirichard23/), [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/), [Owain Evans](https://owainevans.github.io/), and [Dan Hendrycks](https://danhendrycks.com/). <img align="center" src="assets/splash.png" width="750"> ## Introduction Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits. ## Autocast Dataset The original version of the [Autocast dataset can be downloaded here](https://people.eecs.berkeley.edu/~hendrycks/autocast.tar.gz). For more details on how to use the Autocast dataset and news articles, please refer to our short demonstration in `usage.ipynb`. Each question has the following fields: ```json { "id": "unique identifier (str)", "question": "question body (str)", "background": "question context/details (str)", "qtype": "question type (str)", "status": "question status (str)", "choices": "choices or possible ranges (List or Dict)", "answer": "question resolution (str or float)", "crowd": "human crowd forecasts over time (List)", "publish_time": "publish timestamp (str)", "close_time": "close timestamp (str)", "prediction_count": "number of crowd predictions (int)", "forecaster_count": "number of crowd forecasters (int)", "tags": "question category (List)", "source_links": "source links from comments (List)" } ``` The authors obtained permission from [Metaculus](https://www.metaculus.com/) to host the dataset on GitHub for research purposes only. ## IntervalQA Dataset Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration. [Download the IntervalQA dataset here](https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz). ## Citation If you find this useful in your research, please consider citing: @article{zouforecasting2022, title={Forecasting Future World Events with Neural Networks}, author={Andy Zou and Tristan Xiao and Ryan Jia and Joe Kwon and Mantas Mazeika and Richard Li and Dawn Song and Jacob Steinhardt and Owain Evans and Dan Hendrycks}, journal={NeurIPS}, year={2022} }

AlgoveraAI/autocast

2024-01-17T12:05:02+00:00

{"task_categories": ["time-series-forecasting"]}

2024-01-23T11:34:41+00:00

TAGS #task_categories-time-series-forecasting #arxiv-2206.15474 #region-us

# Forecasting Future World Events with Neural Networks This is an (unofficial) repository for "Forecasting Future World Events with Neural Networks" by Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks. <img align="center" src="assets/URL" width="750"> ## Introduction Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits. ## Autocast Dataset The original version of the Autocast dataset can be downloaded here. For more details on how to use the Autocast dataset and news articles, please refer to our short demonstration in 'URL'. Each question has the following fields: The authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only. ## IntervalQA Dataset Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration. Download the IntervalQA dataset here. If you find this useful in your research, please consider citing: @article{zouforecasting2022, title={Forecasting Future World Events with Neural Networks}, author={Andy Zou and Tristan Xiao and Ryan Jia and Joe Kwon and Mantas Mazeika and Richard Li and Dawn Song and Jacob Steinhardt and Owain Evans and Dan Hendrycks}, journal={NeurIPS}, year={2022} }

5ef2c5b8df689c7eb2cad93b9f0edb68aac3eda8

# Dataset Card for "20000-50000-ultrafeedback-binarized-preferences-cleaned-ita" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

giux78/20000-50000-ultrafeedback-binarized-preferences-cleaned-ita

2024-01-17T12:12:21+00:00

{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "chosen", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "chosen-rating", "dtype": "float64"}, {"name": "chosen-model", "dtype": "string"}, {"name": "rejected", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "rejected-rating", "dtype": "float64"}, {"name": "rejected-model", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 197228907, "num_examples": 30000}], "download_size": 87134816, "dataset_size": 197228907}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-01-17T12:12:28+00:00

TAGS #region-us

# Dataset Card for "20000-50000-ultrafeedback-binarized-preferences-cleaned-ita" More Information needed

e8661dd21fc838cd9c38b4bf5ace22dc8cf46566

# Dataset of velour/ベロア (Fire Emblem) This is the dataset of velour/ベロア (Fire Emblem), containing 184 images and their tags. The core tags of this character are `animal_ears, multicolored_hair, grey_hair, wolf_ears, long_hair, red_eyes, black_hair, tail, wolf_tail, streaked_hair, two-tone_hair, 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 | 184 | 195.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velour_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 184 | 119.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velour_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 418 | 245.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velour_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 184 | 177.54 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velour_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 418 | 331.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/velour_fireemblem/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/velour_fireemblem', 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 | 20 |  |  |  |  |  | 1girl, solo, gloves, simple_background, white_background, boots, hood_up | | 1 | 7 |  |  |  |  |  | 1girl, bangs, long_sleeves, looking_at_viewer, white_shirt, brown_gloves, corset, frills, hood_up, neck_ribbon, solo, belt_buckle, cape, closed_mouth, simple_background, black_ribbon, blush, brown_belt, white_background, black_pants, boots, brown_footwear, pouch, standing | | 2 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, open_mouth, solo_focus, nipples, vaginal, blush, cum_in_pussy, large_breasts, penis, sex_from_behind, completely_nude, medium_breasts, straddling, tears | | 3 | 10 |  |  |  |  |  | hetero, 1boy, 1girl, nipples, penis, solo_focus, hood, facial, bar_censor, blush, large_breasts, open_mouth, white_hair, cum_on_breasts, bangs, cum_on_hair, paizuri, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | gloves | simple_background | white_background | boots | hood_up | bangs | long_sleeves | looking_at_viewer | white_shirt | brown_gloves | corset | frills | neck_ribbon | belt_buckle | cape | closed_mouth | black_ribbon | blush | brown_belt | black_pants | brown_footwear | pouch | standing | 1boy | hetero | open_mouth | solo_focus | nipples | vaginal | cum_in_pussy | large_breasts | penis | sex_from_behind | completely_nude | medium_breasts | straddling | tears | hood | facial | bar_censor | white_hair | cum_on_breasts | cum_on_hair | paizuri | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------|:--------------------|:-------------------|:--------|:----------|:--------|:---------------|:--------------------|:--------------|:---------------|:---------|:---------|:--------------|:--------------|:-------|:---------------|:---------------|:--------|:-------------|:--------------|:-----------------|:--------|:-----------|:-------|:---------|:-------------|:-------------|:----------|:----------|:---------------|:----------------|:--------|:------------------|:------------------|:-----------------|:-------------|:--------|:-------|:---------|:-------------|:-------------|:-----------------|:--------------|:----------|:-------------| | 0 | 20 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 |  |  |  |  |  | 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 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | 3 | 10 |  |  |  |  |  | X | | | | | | | X | | | | | | | | | | | | X | | | | | | X | X | X | X | X | | | X | X | | | | | | X | X | X | X | X | X | X | X |

CyberHarem/velour_fireemblem

2024-01-17T13:08:37+00:00

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

2024-01-17T13:46:14+00:00

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

Dataset of velour/ベロア (Fire Emblem) =================================== This is the dataset of velour/ベロア (Fire Emblem), containing 184 images and their tags. The core tags of this character are 'animal\_ears, multicolored\_hair, grey\_hair, wolf\_ears, long\_hair, red\_eyes, black\_hair, tail, wolf\_tail, streaked\_hair, two-tone\_hair, 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

957836bfd09d63d1989e5dc2a956030b1d053e30

# Dataset of lute/ルーテ (Fire Emblem) This is the dataset of lute/ルーテ (Fire Emblem), containing 241 images and their tags. The core tags of this character are `purple_hair, purple_eyes, breasts, twintails`, 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 | 241 | 249.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lute_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 241 | 151.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lute_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 493 | 280.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lute_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 241 | 225.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lute_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 493 | 374.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/lute_fireemblem/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/lute_fireemblem', 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 |  |  |  |  |  | 1girl, solo, cleavage, simple_background, hair_flower, holding_book, navel, long_hair, medium_breasts, white_background, bare_shoulders, looking_at_viewer, bangs, closed_mouth, purple_bikini, collarbone, full_body, sandals | | 1 | 8 |  |  |  |  |  | 1girl, bare_shoulders, dress, solo, cape, holding_book, simple_background, white_background, low_twintails, full_body, looking_at_viewer, short_hair, smile, upper_body | | 2 | 21 |  |  |  |  |  | 1girl, navel, nipples, solo, collarbone, small_breasts, blush, completely_nude, pussy, looking_at_viewer, holding_book, standing, bangs, mosaic_censoring, medium_hair, full_body, open_mouth | | 3 | 11 |  |  |  |  |  | 1girl, bare_shoulders, fur_trim, hat, long_sleeves, solo, bangs, official_alternate_costume, choker, flower, looking_at_viewer, twin_braids, boots, long_hair, open_mouth, simple_background, white_dress, white_footwear, christmas, closed_mouth, collarbone, food, white_background, full_body, holding, white_headwear | | 4 | 5 |  |  |  |  |  | 1girl, completely_nude, hetero, mosaic_censoring, multiple_penises, nipples, solo_focus, blush, navel, on_back, 3boys, collarbone, cum_on_hair, facial, gangbang, medium_breasts, small_breasts, spread_legs, sweat, 2boys, bangs, bukkake, closed_eyes, cum_in_pussy, cum_on_breasts, double_handjob, ejaculation, hand_on_another's_head, heart, leg_grab, open_mouth, rape | | 5 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, nipples, open_mouth, sex, vaginal, blush, cum_in_pussy, medium_breasts, mosaic_censoring, nude, cowgirl_position, girl_on_top, oral | | 6 | 5 |  |  |  |  |  | 1girl, blush, solo, tears, arms_behind_back, crotch_rope, nipples, pussy_juice, torn_clothes, white_panties, open_mouth, peeing_self, shibari_over_clothes, small_breasts, wet_panties | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | simple_background | hair_flower | holding_book | navel | long_hair | medium_breasts | white_background | bare_shoulders | looking_at_viewer | bangs | closed_mouth | purple_bikini | collarbone | full_body | sandals | dress | cape | low_twintails | short_hair | smile | upper_body | nipples | small_breasts | blush | completely_nude | pussy | standing | mosaic_censoring | medium_hair | open_mouth | fur_trim | hat | long_sleeves | official_alternate_costume | choker | flower | twin_braids | boots | white_dress | white_footwear | christmas | food | holding | white_headwear | hetero | multiple_penises | solo_focus | on_back | 3boys | cum_on_hair | facial | gangbang | spread_legs | sweat | 2boys | bukkake | closed_eyes | cum_in_pussy | cum_on_breasts | double_handjob | ejaculation | hand_on_another's_head | heart | leg_grab | rape | 1boy | penis | sex | vaginal | nude | cowgirl_position | girl_on_top | oral | tears | arms_behind_back | crotch_rope | pussy_juice | torn_clothes | white_panties | peeing_self | shibari_over_clothes | wet_panties | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:--------------------|:--------------|:---------------|:--------|:------------|:-----------------|:-------------------|:-----------------|:--------------------|:--------|:---------------|:----------------|:-------------|:------------|:----------|:--------|:-------|:----------------|:-------------|:--------|:-------------|:----------|:----------------|:--------|:------------------|:--------|:-----------|:-------------------|:--------------|:-------------|:-----------|:------|:---------------|:-----------------------------|:---------|:---------|:--------------|:--------|:--------------|:-----------------|:------------|:-------|:----------|:-----------------|:---------|:-------------------|:-------------|:----------|:--------|:--------------|:---------|:-----------|:--------------|:--------|:--------|:----------|:--------------|:---------------|:-----------------|:-----------------|:--------------|:-------------------------|:--------|:-----------|:-------|:-------|:--------|:------|:----------|:-------|:-------------------|:--------------|:-------|:--------|:-------------------|:--------------|:--------------|:---------------|:----------------|:--------------|:-----------------------|:--------------| | 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 8 |  |  |  |  |  | X | X | | X | | X | | | | X | X | X | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 21 |  |  |  |  |  | X | X | | | | X | X | | | | | X | X | | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 11 |  |  |  |  |  | X | X | | 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 |  |  |  |  |  | X | | | | | | X | | X | | | | X | | | X | | | | | | | | | X | X | X | 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 |  |  |  |  |  | X | | | | | | | | X | | | | | | | | | | | | | | | | X | | X | | | | X | | X | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | 6 | 5 |  |  |  |  |  | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |

CyberHarem/lute_fireemblem

2024-01-17T13:08:44+00:00

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

2024-01-17T13:51:26+00:00

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

Dataset of lute/ルーテ (Fire Emblem) ================================= This is the dataset of lute/ルーテ (Fire Emblem), containing 241 images and their tags. The core tags of this character are 'purple\_hair, purple\_eyes, breasts, twintails', 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

0e422be8475fc97c8e1113bdb79cebdbfe5acb4e

# LIMA数据集中文版 ## 数据集构造方法 - 使用GPT-4-Turbo将原始LIMA数据集的问题部分翻译为中文。 - 使用GPT-4-Turbo回答翻译后的问题。 - 注意，本数据集不包含原始LIMA数据集的多轮问答部分

Jellyfish042/Chinese-LIMA-V0

2024-01-17T13:08:52+00:00

{"language": ["zh"], "license": "mit", "dataset_info": {"features": [{"name": "User", "dtype": "string"}, {"name": "Assistant", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1425302, "num_examples": 1000}], "download_size": 934484, "dataset_size": 1425302}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-01-17T13:14:46+00:00

TAGS #language-Chinese #license-mit #region-us

# LIMA数据集中文版 ## 数据集构造方法 - 使用GPT-4-Turbo将原始LIMA数据集的问题部分翻译为中文。 - 使用GPT-4-Turbo回答翻译后的问题。 - 注意，本数据集不包含原始LIMA数据集的多轮问答部分

96f853a330994d4e89ea4ab6643fa1d3ba700f14

# Dataset of yunaka/ユナカ (Fire Emblem) This is the dataset of yunaka/ユナカ (Fire Emblem), containing 285 images and their tags. The core tags of this character are `long_hair, red_hair, breasts, red_eyes, large_breasts, bangs, hair_ornament`, 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 | 285 | 454.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 285 | 225.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 714 | 504.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 285 | 386.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 714 | 783.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yunaka_fireemblem/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/yunaka_fireemblem', 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 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, star_(symbol), white_shirt, blush, collared_shirt, black_skirt, simple_background, tattoo, white_background, medium_breasts, open_mouth | | 1 | 11 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, simple_background, solo, star_(symbol), white_background, open_mouth, cape, choker, facial_mark, one_eye_closed, upper_body, blush, :d, ;d | | 2 | 7 |  |  |  |  |  | 1girl, christmas, gloves, looking_at_viewer, santa_hat, smile, solo, star_(symbol), cleavage, santa_costume, bell, open_mouth, blush, candy_cane, holding, official_alternate_costume, one_eye_closed, cape, fur_trim, medium_breasts, sack | | 3 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cape, holding_weapon, cleavage, bodysuit, smile, dagger, holding_knife, white_background, simple_background, one_eye_closed, open_mouth, star_hair_ornament | | 4 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, star_(symbol), nipples, open_mouth, penis, sex, tattoo, blush, nude, vaginal, facial_mark, mosaic_censoring, pussy, smile, torn_clothes, choker, collarbone, pubic_hair, spread_legs, sweat | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | star_(symbol) | white_shirt | blush | collared_shirt | black_skirt | simple_background | tattoo | white_background | medium_breasts | open_mouth | cleavage | cape | choker | facial_mark | one_eye_closed | upper_body | :d | ;d | christmas | gloves | santa_hat | santa_costume | bell | candy_cane | holding | official_alternate_costume | fur_trim | sack | holding_weapon | bodysuit | dagger | holding_knife | star_hair_ornament | 1boy | hetero | solo_focus | nipples | penis | sex | nude | vaginal | mosaic_censoring | pussy | torn_clothes | collarbone | pubic_hair | spread_legs | sweat | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:----------------|:--------------|:--------|:-----------------|:--------------|:--------------------|:---------|:-------------------|:-----------------|:-------------|:-----------|:-------|:---------|:--------------|:-----------------|:-------------|:-----|:-----|:------------|:---------|:------------|:----------------|:-------|:-------------|:----------|:-----------------------------|:-----------|:-------|:-----------------|:-----------|:---------|:----------------|:---------------------|:-------|:---------|:-------------|:----------|:--------|:------|:-------|:----------|:-------------------|:--------|:---------------|:-------------|:-------------|:--------------|:--------| | 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 |  |  |  |  |  | X | X | | X | X | | X | | | X | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | X | X | X | X | | X | | | | | | X | X | X | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 3 | 16 |  |  |  |  |  | X | X | X | X | | | | | | X | | X | | X | X | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | 4 | 7 |  |  |  |  |  | X | | X | | X | | X | | | | X | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/yunaka_fireemblem

2024-01-17T13:09:05+00:00

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

2024-01-17T14:05:34+00:00

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

Dataset of yunaka/ユナカ (Fire Emblem) =================================== This is the dataset of yunaka/ユナカ (Fire Emblem), containing 285 images and their tags. The core tags of this character are 'long\_hair, red\_hair, breasts, red\_eyes, large\_breasts, bangs, hair\_ornament', 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

6c9d64a01ed054a8bdcfb7309e4e5387f3f22464

# Dataset of hinoka/ヒノカ (Fire Emblem) This is the dataset of hinoka/ヒノカ (Fire Emblem), containing 316 images and their tags. The core tags of this character are `red_hair, short_hair, red_eyes, 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 | 316 | 319.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinoka_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 316 | 201.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinoka_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 683 | 380.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinoka_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 316 | 287.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinoka_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 683 | 496.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hinoka_fireemblem/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/hinoka_fireemblem', 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 | 22 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, blush, penis, sex, open_mouth, nipples, pussy, vaginal, cum, navel, completely_nude, uncensored, ass, medium_breasts, thighhighs | | 1 | 13 |  |  |  |  |  | 1girl, huge_breasts, muscular_female, nipples, hetero, solo_focus, thick_thighs, 1boy, ass, sex, completely_nude, antenna_hair, futanari, smile, testicles, white_background, cum, gigantic_breasts, huge_penis | | 2 | 6 |  |  |  |  |  | 1girl, nipples, nude, solo, blush, female_pubic_hair, pussy, small_breasts, navel, simple_background, smile | | 3 | 6 |  |  |  |  |  | 1girl, medium_breasts, nipples, nude, solo, navel, smile | | 4 | 5 |  |  |  |  |  | 1girl, garter_straps, holding_weapon, short_dress, solo, thigh_boots, elbow_gloves, shoulder_armor, smile, zettai_ryouiki, looking_at_viewer, naginata, simple_background, full_body, grey_background, open_mouth, red_footwear, red_thighhighs, spear, white_scarf | | 5 | 12 |  |  |  |  |  | 1girl, solo, naked_towel, blush, looking_at_viewer, ahoge, bare_shoulders, towel_on_head, bucket, cleavage, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | solo_focus | blush | penis | sex | open_mouth | nipples | pussy | vaginal | cum | navel | completely_nude | uncensored | ass | medium_breasts | thighhighs | huge_breasts | muscular_female | thick_thighs | antenna_hair | futanari | smile | testicles | white_background | gigantic_breasts | huge_penis | nude | solo | female_pubic_hair | small_breasts | simple_background | garter_straps | holding_weapon | short_dress | thigh_boots | elbow_gloves | shoulder_armor | zettai_ryouiki | looking_at_viewer | naginata | full_body | grey_background | red_footwear | red_thighhighs | spear | white_scarf | naked_towel | ahoge | bare_shoulders | towel_on_head | bucket | cleavage | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:-------------|:--------|:--------|:------|:-------------|:----------|:--------|:----------|:------|:--------|:------------------|:-------------|:------|:-----------------|:-------------|:---------------|:------------------|:---------------|:---------------|:-----------|:--------|:------------|:-------------------|:-------------------|:-------------|:-------|:-------|:--------------------|:----------------|:--------------------|:----------------|:-----------------|:--------------|:--------------|:---------------|:-----------------|:-----------------|:--------------------|:-----------|:------------|:------------------|:---------------|:-----------------|:--------|:--------------|:--------------|:--------|:-----------------|:----------------|:---------|:-----------| | 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 13 |  |  |  |  |  | X | X | X | X | | | X | | X | | | X | | X | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 |  |  |  |  |  | | X | | | X | | | | X | X | | | X | | | | | | | | | | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 |  |  |  |  |  | | X | | | | | | | X | | | | X | | | | X | | | | | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | | X | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | 5 | 12 |  |  |  |  |  | | X | | | X | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | X | | | | | | | | X | | | | | | | | X | X | X | X | X | X |

CyberHarem/hinoka_fireemblem

2024-01-17T13:12:55+00:00

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

2024-01-17T14:32:48+00:00

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

Dataset of hinoka/ヒノカ (Fire Emblem) =================================== This is the dataset of hinoka/ヒノカ (Fire Emblem), containing 316 images and their tags. The core tags of this character are 'red\_hair, short\_hair, red\_eyes, 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

a6a6b1f6582ff1c5a55240547fcbcd556ac1ed95

# Dataset Card for Evaluation run of Cartinoe5930/iDUS <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [Cartinoe5930/iDUS](https://huggingface.co/Cartinoe5930/iDUS) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_Cartinoe5930__iDUS", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-17T13:14:26.897278](https://huggingface.co/datasets/open-llm-leaderboard/details_Cartinoe5930__iDUS/blob/main/results_2024-01-17T13-14-26.897278.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": 0.24846552462908655, "acc_stderr": 0.030596938174151714, "acc_norm": 0.24984616637570042, "acc_norm_stderr": 0.031417483125595794, "mc1": 0.22276621787025705, "mc1_stderr": 0.014566506961396754, "mc2": 0.48577541497626797, "mc2_stderr": 0.016589496055636796 }, "harness|arc:challenge|25": { "acc": 0.20733788395904437, "acc_stderr": 0.011846905782971352, "acc_norm": 0.2773037542662116, "acc_norm_stderr": 0.013082095839059374 }, "harness|hellaswag|10": { "acc": 0.26020713005377416, "acc_stderr": 0.004378508362084367, "acc_norm": 0.2664807807209719, "acc_norm_stderr": 0.004412149415717922 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.26, "acc_stderr": 0.04408440022768081, "acc_norm": 0.26, "acc_norm_stderr": 0.04408440022768081 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.2, "acc_stderr": 0.034554737023254366, "acc_norm": 0.2, "acc_norm_stderr": 0.034554737023254366 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.32894736842105265, "acc_stderr": 0.03823428969926604, "acc_norm": 0.32894736842105265, "acc_norm_stderr": 0.03823428969926604 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.21509433962264152, "acc_stderr": 0.025288394502891366, "acc_norm": 0.21509433962264152, "acc_norm_stderr": 0.025288394502891366 }, "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": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.20809248554913296, "acc_stderr": 0.030952890217749874, "acc_norm": 0.20809248554913296, "acc_norm_stderr": 0.030952890217749874 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383889, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383889 }, "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": 0.24680851063829787, "acc_stderr": 0.02818544130123409, "acc_norm": 0.24680851063829787, "acc_norm_stderr": 0.02818544130123409 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.21052631578947367, "acc_stderr": 0.038351539543994194, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.038351539543994194 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.1793103448275862, "acc_stderr": 0.031967664333731854, "acc_norm": 0.1793103448275862, "acc_norm_stderr": 0.031967664333731854 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.2671957671957672, "acc_stderr": 0.022789673145776564, "acc_norm": 0.2671957671957672, "acc_norm_stderr": 0.022789673145776564 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.30952380952380953, "acc_stderr": 0.04134913018303316, "acc_norm": 0.30952380952380953, "acc_norm_stderr": 0.04134913018303316 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.2, "acc_stderr": 0.04020151261036846, "acc_norm": 0.2, "acc_norm_stderr": 0.04020151261036846 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.18064516129032257, "acc_stderr": 0.021886178567172548, "acc_norm": 0.18064516129032257, "acc_norm_stderr": 0.021886178567172548 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.15270935960591134, "acc_stderr": 0.02530890453938063, "acc_norm": 0.15270935960591134, "acc_norm_stderr": 0.02530890453938063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.24, "acc_stderr": 0.04292346959909284, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.23030303030303031, "acc_stderr": 0.03287666758603489, "acc_norm": 0.23030303030303031, "acc_norm_stderr": 0.03287666758603489 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.17676767676767677, "acc_stderr": 0.027178752639044915, "acc_norm": 0.17676767676767677, "acc_norm_stderr": 0.027178752639044915 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.19689119170984457, "acc_stderr": 0.028697873971860664, "acc_norm": 0.19689119170984457, "acc_norm_stderr": 0.028697873971860664 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.20256410256410257, "acc_stderr": 0.020377660970371372, "acc_norm": 0.20256410256410257, "acc_norm_stderr": 0.020377660970371372 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.21851851851851853, "acc_stderr": 0.02519575225182379, "acc_norm": 0.21851851851851853, "acc_norm_stderr": 0.02519575225182379 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.21428571428571427, "acc_stderr": 0.02665353159671549, "acc_norm": 0.21428571428571427, "acc_norm_stderr": 0.02665353159671549 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33112582781456956, "acc_stderr": 0.038425817186598696, "acc_norm": 0.33112582781456956, "acc_norm_stderr": 0.038425817186598696 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.20917431192660552, "acc_stderr": 0.01743793717334323, "acc_norm": 0.20917431192660552, "acc_norm_stderr": 0.01743793717334323 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4675925925925926, "acc_stderr": 0.03402801581358966, "acc_norm": 0.4675925925925926, "acc_norm_stderr": 0.03402801581358966 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.2549019607843137, "acc_stderr": 0.030587591351604246, "acc_norm": 0.2549019607843137, "acc_norm_stderr": 0.030587591351604246 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.20253164556962025, "acc_stderr": 0.026160568246601457, "acc_norm": 0.20253164556962025, "acc_norm_stderr": 0.026160568246601457 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.31390134529147984, "acc_stderr": 0.031146796482972465, "acc_norm": 0.31390134529147984, "acc_norm_stderr": 0.031146796482972465 }, "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": 0.2396694214876033, "acc_stderr": 0.03896878985070417, "acc_norm": 0.2396694214876033, "acc_norm_stderr": 0.03896878985070417 }, "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": 0.22085889570552147, "acc_stderr": 0.032591773927421776, "acc_norm": 0.22085889570552147, "acc_norm_stderr": 0.032591773927421776 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.29464285714285715, "acc_stderr": 0.04327040932578729, "acc_norm": 0.29464285714285715, "acc_norm_stderr": 0.04327040932578729 }, "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": 0.2905982905982906, "acc_stderr": 0.02974504857267404, "acc_norm": 0.2905982905982906, "acc_norm_stderr": 0.02974504857267404 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.24, "acc_stderr": 0.04292346959909281, "acc_norm": 0.24, "acc_norm_stderr": 0.04292346959909281 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.28607918263090676, "acc_stderr": 0.016160871405127522, "acc_norm": 0.28607918263090676, "acc_norm_stderr": 0.016160871405127522 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.24855491329479767, "acc_stderr": 0.023267528432100174, "acc_norm": 0.24855491329479767, "acc_norm_stderr": 0.023267528432100174 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.23798882681564246, "acc_stderr": 0.014242630070574915, "acc_norm": 0.23798882681564246, "acc_norm_stderr": 0.014242630070574915 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.22549019607843138, "acc_stderr": 0.02392915551735128, "acc_norm": 0.22549019607843138, "acc_norm_stderr": 0.02392915551735128 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.1864951768488746, "acc_stderr": 0.02212243977248077, "acc_norm": 0.1864951768488746, "acc_norm_stderr": 0.02212243977248077 }, "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": 0.23404255319148937, "acc_stderr": 0.025257861359432417, "acc_norm": 0.23404255319148937, "acc_norm_stderr": 0.025257861359432417 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.24902216427640156, "acc_stderr": 0.01104489226404077, "acc_norm": 0.24902216427640156, "acc_norm_stderr": 0.01104489226404077 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.19117647058823528, "acc_stderr": 0.02388688192244034, "acc_norm": 0.19117647058823528, "acc_norm_stderr": 0.02388688192244034 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.24509803921568626, "acc_stderr": 0.01740181671142765, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.01740181671142765 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.21818181818181817, "acc_stderr": 0.03955932861795833, "acc_norm": 0.21818181818181817, "acc_norm_stderr": 0.03955932861795833 }, "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": 0.24378109452736318, "acc_stderr": 0.03036049015401465, "acc_norm": 0.24378109452736318, "acc_norm_stderr": 0.03036049015401465 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.28, "acc_stderr": 0.04512608598542128, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542128 }, "harness|hendrycksTest-virology|5": { "acc": 0.28313253012048195, "acc_stderr": 0.03507295431370518, "acc_norm": 0.28313253012048195, "acc_norm_stderr": 0.03507295431370518 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.21052631578947367, "acc_stderr": 0.0312678171466318, "acc_norm": 0.21052631578947367, "acc_norm_stderr": 0.0312678171466318 }, "harness|truthfulqa:mc|0": { "mc1": 0.22276621787025705, "mc1_stderr": 0.014566506961396754, "mc2": 0.48577541497626797, "mc2_stderr": 0.016589496055636796 }, "harness|winogrande|5": { "acc": 0.49171270718232046, "acc_stderr": 0.014050555322824194 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is 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open-llm-leaderboard/details_Cartinoe5930__iDUS

2024-01-17T13:16:43+00:00

{"pretty_name": "Evaluation run of Cartinoe5930/iDUS", "dataset_summary": "Dataset automatically created during the evaluation run of model [Cartinoe5930/iDUS](https://huggingface.co/Cartinoe5930/iDUS) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Cartinoe5930__iDUS\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T13:14:26.897278](https://huggingface.co/datasets/open-llm-leaderboard/details_Cartinoe5930__iDUS/blob/main/results_2024-01-17T13-14-26.897278.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\": 0.24846552462908655,\n \"acc_stderr\": 0.030596938174151714,\n \"acc_norm\": 0.24984616637570042,\n \"acc_norm_stderr\": 0.031417483125595794,\n \"mc1\": 0.22276621787025705,\n \"mc1_stderr\": 0.014566506961396754,\n \"mc2\": 0.48577541497626797,\n \"mc2_stderr\": 0.016589496055636796\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.20733788395904437,\n \"acc_stderr\": 0.011846905782971352,\n \"acc_norm\": 0.2773037542662116,\n \"acc_norm_stderr\": 0.013082095839059374\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.26020713005377416,\n \"acc_stderr\": 0.004378508362084367,\n \"acc_norm\": 0.2664807807209719,\n \"acc_norm_stderr\": 0.004412149415717922\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.034554737023254366,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.034554737023254366\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.32894736842105265,\n \"acc_stderr\": 0.03823428969926604,\n \"acc_norm\": 0.32894736842105265,\n \"acc_norm_stderr\": 0.03823428969926604\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.025288394502891366,\n \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.025288394502891366\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\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383889,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383889\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\": 0.24680851063829787,\n \"acc_stderr\": 0.02818544130123409,\n \"acc_norm\": 0.24680851063829787,\n \"acc_norm_stderr\": 0.02818544130123409\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.038351539543994194,\n \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.038351539543994194\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.1793103448275862,\n \"acc_stderr\": 0.031967664333731854,\n \"acc_norm\": 0.1793103448275862,\n \"acc_norm_stderr\": 0.031967664333731854\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2671957671957672,\n \"acc_stderr\": 0.022789673145776564,\n \"acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.022789673145776564\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.18064516129032257,\n \"acc_stderr\": 0.021886178567172548,\n \"acc_norm\": 0.18064516129032257,\n \"acc_norm_stderr\": 0.021886178567172548\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.23030303030303031,\n \"acc_stderr\": 0.03287666758603489,\n \"acc_norm\": 0.23030303030303031,\n \"acc_norm_stderr\": 0.03287666758603489\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.21851851851851853,\n \"acc_stderr\": 0.02519575225182379,\n \"acc_norm\": 0.21851851851851853,\n \"acc_norm_stderr\": 0.02519575225182379\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.02665353159671549,\n \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.02665353159671549\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.20917431192660552,\n \"acc_stderr\": 0.01743793717334323,\n \"acc_norm\": 0.20917431192660552,\n \"acc_norm_stderr\": 0.01743793717334323\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.030587591351604246,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.030587591351604246\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.20253164556962025,\n \"acc_stderr\": 0.026160568246601457,\n \"acc_norm\": 0.20253164556962025,\n \"acc_norm_stderr\": 0.026160568246601457\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n \"acc_norm_stderr\": 0.031146796482972465\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\": 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\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\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n \"acc_stderr\": 0.04327040932578729,\n \"acc_norm\": 0.29464285714285715,\n \"acc_norm_stderr\": 0.04327040932578729\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\": 0.2905982905982906,\n \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.24,\n 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["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T13-14-26.897278.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-17T13-14-26.897278.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_01_17T13_14_26.897278", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T13-14-26.897278.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-01-17T13-14-26.897278.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_01_17T13_14_26.897278", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T13-14-26.897278.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-01-17T13-14-26.897278.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_01_17T13_14_26.897278", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T13-14-26.897278.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-01-17T13-14-26.897278.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_01_17T13_14_26.897278", "path": ["**/details_harness|winogrande|5_2024-01-17T13-14-26.897278.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-01-17T13-14-26.897278.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_01_17T13_14_26.897278", "path": ["results_2024-01-17T13-14-26.897278.parquet"]}, {"split": "latest", "path": ["results_2024-01-17T13-14-26.897278.parquet"]}]}]}

2024-01-17T13:17:05+00:00

TAGS #region-us

# Dataset Card for Evaluation run of Cartinoe5930/iDUS Dataset automatically created during the evaluation run of model Cartinoe5930/iDUS on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-01-17T13:14:26.897278(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

5927da75e3fa805c7748cfbcdd239d167a1c4105

This is a cleaned version of the Quora dataset that's been configured with a train-test-val split. - Train : For training model - Test : For running experiments and comparing different OSS models and closed sourced models - Val : Only to be used at the **end**! Colab Notebook to reproduce : https://colab.research.google.com/drive/1dGjGiqwPV1M7JOLfcPEsSh3SC37urItS?usp=sharing

567-labs/cleaned-quora-dataset-train-test-split

2024-01-17T13:18:52+00:00

{"dataset_info": {"features": [{"name": "questions", "struct": [{"name": "id", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "is_duplicate", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 39231843, "num_examples": 261317}, {"name": "test", "num_bytes": 7005599, "num_examples": 44635}, {"name": "val", "num_bytes": 6704734, "num_examples": 42232}], "download_size": 31031925, "dataset_size": 52942176}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}]}

2024-01-29T14:44:01+00:00

TAGS #region-us

This is a cleaned version of the Quora dataset that's been configured with a train-test-val split. - Train : For training model - Test : For running experiments and comparing different OSS models and closed sourced models - Val : Only to be used at the end! Colab Notebook to reproduce : URL

59a030207046255f521a01dd9219b2f952e5844c

# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-DPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [zhengr/MixTAO-7Bx2-MoE-DPO](https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-17T13:23:29.676681](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-DPO/blob/main/results_2024-01-17T13-23-29.676681.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": 0.6522594762182442, "acc_stderr": 0.0319843897745183, "acc_norm": 0.6520128707870778, "acc_norm_stderr": 0.03264277329988372, "mc1": 0.5385556915544676, "mc1_stderr": 0.017451384104637455, "mc2": 0.6934208246675816, "mc2_stderr": 0.014891018416465928 }, "harness|arc:challenge|25": { "acc": 0.6808873720136519, "acc_stderr": 0.01362169611917331, "acc_norm": 0.7090443686006825, "acc_norm_stderr": 0.013273077865907595 }, "harness|hellaswag|10": { "acc": 0.7027484564827724, "acc_stderr": 0.004561141293448457, "acc_norm": 0.8712407886875124, "acc_norm_stderr": 0.003342487333262275 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6296296296296297, "acc_stderr": 0.041716541613545426, "acc_norm": 0.6296296296296297, "acc_norm_stderr": 0.041716541613545426 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6842105263157895, "acc_stderr": 0.0378272898086547, "acc_norm": 0.6842105263157895, "acc_norm_stderr": 0.0378272898086547 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.65, "acc_stderr": 0.0479372485441102, "acc_norm": 0.65, "acc_norm_stderr": 0.0479372485441102 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7132075471698113, "acc_stderr": 0.027834912527544067, "acc_norm": 0.7132075471698113, "acc_norm_stderr": 0.027834912527544067 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7569444444444444, "acc_stderr": 0.035868792800803406, "acc_norm": 0.7569444444444444, "acc_norm_stderr": 0.035868792800803406 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6589595375722543, "acc_stderr": 0.03614665424180826, "acc_norm": 0.6589595375722543, "acc_norm_stderr": 0.03614665424180826 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4019607843137255, "acc_stderr": 0.048786087144669955, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.048786087144669955 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909284, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5787234042553191, "acc_stderr": 0.03227834510146268, "acc_norm": 0.5787234042553191, "acc_norm_stderr": 0.03227834510146268 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5, "acc_stderr": 0.047036043419179864, "acc_norm": 0.5, "acc_norm_stderr": 0.047036043419179864 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5379310344827586, "acc_stderr": 0.04154659671707548, "acc_norm": 0.5379310344827586, "acc_norm_stderr": 0.04154659671707548 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.41005291005291006, "acc_stderr": 0.025331202438944427, "acc_norm": 0.41005291005291006, "acc_norm_stderr": 0.025331202438944427 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.4603174603174603, "acc_stderr": 0.04458029125470973, "acc_norm": 0.4603174603174603, "acc_norm_stderr": 0.04458029125470973 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.31, "acc_stderr": 0.04648231987117316, "acc_norm": 0.31, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7903225806451613, "acc_stderr": 0.023157879349083522, "acc_norm": 0.7903225806451613, "acc_norm_stderr": 0.023157879349083522 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.7, "acc_stderr": 0.046056618647183814, "acc_norm": 0.7, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7696969696969697, "acc_stderr": 0.03287666758603491, "acc_norm": 0.7696969696969697, "acc_norm_stderr": 0.03287666758603491 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.797979797979798, "acc_stderr": 0.02860620428922987, "acc_norm": 0.797979797979798, "acc_norm_stderr": 0.02860620428922987 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9067357512953368, "acc_stderr": 0.020986854593289715, "acc_norm": 0.9067357512953368, "acc_norm_stderr": 0.020986854593289715 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6666666666666666, "acc_stderr": 0.023901157979402534, "acc_norm": 0.6666666666666666, "acc_norm_stderr": 0.023901157979402534 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.32222222222222224, "acc_stderr": 0.028493465091028593, "acc_norm": 0.32222222222222224, "acc_norm_stderr": 0.028493465091028593 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6890756302521008, "acc_stderr": 0.03006676158297793, "acc_norm": 0.6890756302521008, "acc_norm_stderr": 0.03006676158297793 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.31788079470198677, "acc_stderr": 0.038020397601079024, "acc_norm": 0.31788079470198677, "acc_norm_stderr": 0.038020397601079024 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8550458715596331, "acc_stderr": 0.015094215699700486, "acc_norm": 0.8550458715596331, "acc_norm_stderr": 0.015094215699700486 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.5648148148148148, "acc_stderr": 0.033812000056435254, "acc_norm": 0.5648148148148148, "acc_norm_stderr": 0.033812000056435254 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.8627450980392157, "acc_stderr": 0.024152225962801584, "acc_norm": 0.8627450980392157, "acc_norm_stderr": 0.024152225962801584 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8143459915611815, "acc_stderr": 0.02531049537694486, "acc_norm": 0.8143459915611815, "acc_norm_stderr": 0.02531049537694486 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.672645739910314, "acc_stderr": 0.03149384670994131, "acc_norm": 0.672645739910314, "acc_norm_stderr": 0.03149384670994131 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7862595419847328, "acc_stderr": 0.0359546161177469, "acc_norm": 0.7862595419847328, "acc_norm_stderr": 0.0359546161177469 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.0384985609879409, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.0384985609879409 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7777777777777778, "acc_stderr": 0.0401910747255735, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.0401910747255735 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7730061349693251, "acc_stderr": 0.03291099578615769, "acc_norm": 0.7730061349693251, "acc_norm_stderr": 0.03291099578615769 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.45535714285714285, "acc_stderr": 0.047268355537191, "acc_norm": 0.45535714285714285, "acc_norm_stderr": 0.047268355537191 }, "harness|hendrycksTest-management|5": { "acc": 0.7864077669902912, "acc_stderr": 0.040580420156460344, "acc_norm": 0.7864077669902912, "acc_norm_stderr": 0.040580420156460344 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8760683760683761, "acc_stderr": 0.02158649400128138, "acc_norm": 0.8760683760683761, "acc_norm_stderr": 0.02158649400128138 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.72, "acc_stderr": 0.045126085985421276, "acc_norm": 0.72, "acc_norm_stderr": 0.045126085985421276 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8250319284802043, "acc_stderr": 0.013586619219903335, "acc_norm": 0.8250319284802043, "acc_norm_stderr": 0.013586619219903335 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7167630057803468, "acc_stderr": 0.02425790170532338, "acc_norm": 0.7167630057803468, "acc_norm_stderr": 0.02425790170532338 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.4212290502793296, "acc_stderr": 0.01651367603117959, "acc_norm": 0.4212290502793296, "acc_norm_stderr": 0.01651367603117959 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7058823529411765, "acc_stderr": 0.026090162504279056, "acc_norm": 0.7058823529411765, "acc_norm_stderr": 0.026090162504279056 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7170418006430869, "acc_stderr": 0.025583062489984813, "acc_norm": 0.7170418006430869, "acc_norm_stderr": 0.025583062489984813 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7685185185185185, "acc_stderr": 0.023468429832451156, "acc_norm": 0.7685185185185185, "acc_norm_stderr": 0.023468429832451156 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5070921985815603, "acc_stderr": 0.02982449855912901, "acc_norm": 0.5070921985815603, "acc_norm_stderr": 0.02982449855912901 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4771838331160365, "acc_stderr": 0.012756933382823698, "acc_norm": 0.4771838331160365, "acc_norm_stderr": 0.012756933382823698 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6838235294117647, "acc_stderr": 0.028245687391462927, "acc_norm": 0.6838235294117647, "acc_norm_stderr": 0.028245687391462927 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6683006535947712, "acc_stderr": 0.01904748523936038, "acc_norm": 0.6683006535947712, "acc_norm_stderr": 0.01904748523936038 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6727272727272727, "acc_stderr": 0.0449429086625209, "acc_norm": 0.6727272727272727, "acc_norm_stderr": 0.0449429086625209 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7428571428571429, "acc_stderr": 0.02797982353874455, "acc_norm": 0.7428571428571429, "acc_norm_stderr": 0.02797982353874455 }, "harness|hendrycksTest-sociology|5": { "acc": 0.835820895522388, "acc_stderr": 0.02619392354445412, "acc_norm": 0.835820895522388, "acc_norm_stderr": 0.02619392354445412 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.88, "acc_stderr": 0.03265986323710906, "acc_norm": 0.88, "acc_norm_stderr": 0.03265986323710906 }, "harness|hendrycksTest-virology|5": { "acc": 0.5542168674698795, "acc_stderr": 0.03869543323472101, "acc_norm": 0.5542168674698795, "acc_norm_stderr": 0.03869543323472101 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8187134502923976, "acc_stderr": 0.02954774168764004, "acc_norm": 0.8187134502923976, "acc_norm_stderr": 0.02954774168764004 }, "harness|truthfulqa:mc|0": { "mc1": 0.5385556915544676, "mc1_stderr": 0.017451384104637455, "mc2": 0.6934208246675816, "mc2_stderr": 0.014891018416465928 }, "harness|winogrande|5": { "acc": 0.8121546961325967, "acc_stderr": 0.010977481103435091 }, "harness|gsm8k|5": { "acc": 0.7035633055344959, "acc_stderr": 0.012579398235589534 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-DPO

2024-01-17T13:25:46+00:00

{"pretty_name": "Evaluation run of zhengr/MixTAO-7Bx2-MoE-DPO", "dataset_summary": "Dataset automatically created during the evaluation run of model [zhengr/MixTAO-7Bx2-MoE-DPO](https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-DPO\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T13:23:29.676681](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-DPO/blob/main/results_2024-01-17T13-23-29.676681.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\": 0.6522594762182442,\n \"acc_stderr\": 0.0319843897745183,\n \"acc_norm\": 0.6520128707870778,\n \"acc_norm_stderr\": 0.03264277329988372,\n \"mc1\": 0.5385556915544676,\n \"mc1_stderr\": 0.017451384104637455,\n \"mc2\": 0.6934208246675816,\n \"mc2_stderr\": 0.014891018416465928\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6808873720136519,\n \"acc_stderr\": 0.01362169611917331,\n \"acc_norm\": 0.7090443686006825,\n \"acc_norm_stderr\": 0.013273077865907595\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7027484564827724,\n \"acc_stderr\": 0.004561141293448457,\n \"acc_norm\": 0.8712407886875124,\n \"acc_norm_stderr\": 0.003342487333262275\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544067,\n \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544067\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n \"acc_stderr\": 0.035868792800803406,\n \"acc_norm\": 0.7569444444444444,\n \"acc_norm_stderr\": 0.035868792800803406\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.41005291005291006,\n \"acc_stderr\": 0.025331202438944427,\n \"acc_norm\": 0.41005291005291006,\n \"acc_norm_stderr\": 0.025331202438944427\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.03287666758603491,\n \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.03287666758603491\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289715,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289715\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700486,\n \"acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700486\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5648148148148148,\n \"acc_stderr\": 0.033812000056435254,\n \"acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.033812000056435254\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8627450980392157,\n \"acc_stderr\": 0.024152225962801584,\n \"acc_norm\": 0.8627450980392157,\n \"acc_norm_stderr\": 0.024152225962801584\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.02531049537694486,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.02531049537694486\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.0384985609879409,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.0384985609879409\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n \"acc_stderr\": 0.047268355537191,\n \"acc_norm\": 0.45535714285714285,\n \"acc_norm_stderr\": 0.047268355537191\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n \"acc_stderr\": 0.013586619219903335,\n \"acc_norm\": 0.8250319284802043,\n \"acc_norm_stderr\": 0.013586619219903335\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4212290502793296,\n \"acc_stderr\": 0.01651367603117959,\n \"acc_norm\": 0.4212290502793296,\n \"acc_norm_stderr\": 0.01651367603117959\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279056,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279056\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7685185185185185,\n \"acc_stderr\": 0.023468429832451156,\n \"acc_norm\": 0.7685185185185185,\n \"acc_norm_stderr\": 0.023468429832451156\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4771838331160365,\n \"acc_stderr\": 0.012756933382823698,\n \"acc_norm\": 0.4771838331160365,\n \"acc_norm_stderr\": 0.012756933382823698\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.02619392354445412,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.02619392354445412\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.02954774168764004,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.02954774168764004\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5385556915544676,\n \"mc1_stderr\": 0.017451384104637455,\n \"mc2\": 0.6934208246675816,\n \"mc2_stderr\": 0.014891018416465928\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.010977481103435091\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7035633055344959,\n \"acc_stderr\": 0.012579398235589534\n }\n}\n```", "repo_url": "https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-DPO", "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": "2024_01_17T13_23_29.676681", "path": ["**/details_harness|arc:challenge|25_2024-01-17T13-23-29.676681.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-17T13-23-29.676681.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_17T13_23_29.676681", "path": ["**/details_harness|gsm8k|5_2024-01-17T13-23-29.676681.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-17T13-23-29.676681.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_17T13_23_29.676681", "path": ["**/details_harness|hellaswag|10_2024-01-17T13-23-29.676681.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-17T13-23-29.676681.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_17T13_23_29.676681", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T13-23-29.676681.parquet", 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2024-01-17T13:26:07+00:00

TAGS #region-us

# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-DPO Dataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-DPO on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-01-17T13:23:29.676681(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

5d92806e2e296fc269c18397cb5b45e86b4b58bb

# Dataset of pieri/ピエリ (Fire Emblem) This is the dataset of pieri/ピエリ (Fire Emblem), containing 194 images and their tags. The core tags of this character are `blue_hair, multicolored_hair, hair_over_one_eye, pink_hair, twintails, breasts, two-tone_hair, red_eyes, gradient_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 | 194 | 216.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pieri_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 194 | 127.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pieri_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 427 | 258.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pieri_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 194 | 190.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pieri_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 427 | 357.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pieri_fireemblem/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/pieri_fireemblem', 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 |  |  |  |  |  | 1girl, simple_background, solo, armor, white_background, smile, looking_at_viewer, sword, upper_body | | 1 | 10 |  |  |  |  |  | 1girl, armor, solo, spear, open_mouth | | 2 | 14 |  |  |  |  |  | 1girl, solo, nipples, nude, pussy, smile, blush, looking_at_viewer, uncensored, navel | | 3 | 7 |  |  |  |  |  | blush, nipples, nude, solo_focus, 1boy, 1girl, cum_on_breasts, hetero, smile, cum_on_hair, facial, paizuri, penis, censored, closed_eyes, collarbone, long_hair, open_mouth | | 4 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, solo_focus, open_mouth, blush, pink_eyes, ahegao, completely_nude, medium_breasts, sex_from_behind, simple_background, tongue_out, arm_grab, arm_held_back, navel, standing_sex, vaginal | | 5 | 9 |  |  |  |  |  | 1boy, fellatio, hetero, penis, 1girl, solo_focus, uncensored, nude, blush, english_text, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | simple_background | solo | armor | white_background | smile | looking_at_viewer | sword | upper_body | spear | open_mouth | nipples | nude | pussy | blush | uncensored | navel | solo_focus | 1boy | cum_on_breasts | hetero | cum_on_hair | facial | paizuri | penis | censored | closed_eyes | collarbone | long_hair | pink_eyes | ahegao | completely_nude | medium_breasts | sex_from_behind | tongue_out | arm_grab | arm_held_back | standing_sex | vaginal | fellatio | english_text | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:--------|:-------------------|:--------|:--------------------|:--------|:-------------|:--------|:-------------|:----------|:-------|:--------|:--------|:-------------|:--------|:-------------|:-------|:-----------------|:---------|:--------------|:---------|:----------|:--------|:-----------|:--------------|:-------------|:------------|:------------|:---------|:------------------|:-----------------|:------------------|:-------------|:-----------|:----------------|:---------------|:----------|:-----------|:---------------| | 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 |  |  |  |  |  | X | | X | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 14 |  |  |  |  |  | X | | X | | | X | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 |  |  |  |  |  | X | | | | | X | | | | | X | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 4 | 7 |  |  |  |  |  | X | X | | | | | | | | | X | X | | | X | | X | X | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | 5 | 9 |  |  |  |  |  | X | X | | | | | | | | | | | X | | X | X | | X | X | | X | | | | X | | | | | | | | | | | | | | | X | X |

CyberHarem/pieri_fireemblem

2024-01-17T13:30:49+00:00

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

2024-01-17T14:06:52+00:00

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

Dataset of pieri/ピエリ (Fire Emblem) ================================== This is the dataset of pieri/ピエリ (Fire Emblem), containing 194 images and their tags. The core tags of this character are 'blue\_hair, multicolored\_hair, hair\_over\_one\_eye, pink\_hair, twintails, breasts, two-tone\_hair, red\_eyes, gradient\_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

e781bb38cf9b3106cd9523b300e2235c30123f8b

# Dataset of oboro/オボロ (Fire Emblem) This is the dataset of oboro/オボロ (Fire Emblem), containing 108 images and their tags. The core tags of this character are `long_hair, ponytail, blue_hair, brown_eyes, breasts, braid`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 108 | 91.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 108 | 66.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 235 | 125.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 108 | 86.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 235 | 155.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oboro_fireemblem/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/oboro_fireemblem', 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 |  |  |  |  |  | 1girl, solo, japanese_clothes, armor, naginata, simple_background, smile, white_background, looking_at_viewer, spear | | 1 | 12 |  |  |  |  |  | 1girl, hetero, penis, solo_focus, blush, nipples, sex, vaginal, 1boy, nude, large_breasts, cum_in_pussy, navel, sweat, bar_censor, medium_breasts, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | japanese_clothes | armor | naginata | simple_background | smile | white_background | looking_at_viewer | spear | hetero | penis | solo_focus | blush | nipples | sex | vaginal | 1boy | nude | large_breasts | cum_in_pussy | navel | sweat | bar_censor | medium_breasts | open_mouth | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------------|:--------|:-----------|:--------------------|:--------|:-------------------|:--------------------|:--------|:---------|:--------|:-------------|:--------|:----------|:------|:----------|:-------|:-------|:----------------|:---------------|:--------|:--------|:-------------|:-----------------|:-------------| | 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | 1 | 12 |  |  |  |  |  | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/oboro_fireemblem

2024-01-17T13:31:00+00:00

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

2024-01-17T13:52:06+00:00

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

Dataset of oboro/オボロ (Fire Emblem) ================================== This is the dataset of oboro/オボロ (Fire Emblem), containing 108 images and their tags. The core tags of this character are 'long\_hair, ponytail, blue\_hair, brown\_eyes, breasts, braid', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

0ec15d902aae33d887074375c92772e07d5dc2a0

# Dataset of est/エスト (Fire Emblem) This is the dataset of est/エスト (Fire Emblem), containing 103 images and their tags. The core tags of this character are `short_hair, pink_hair, breasts, red_hair, pink_eyes, headband, red_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 | 103 | 94.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/est_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 103 | 62.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/est_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 208 | 113.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/est_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 103 | 86.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/est_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 208 | 149.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/est_fireemblem/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/est_fireemblem', 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 |  |  |  |  |  | hetero, 1girl, blush, cum_in_pussy, thighhighs, armor, solo_focus, vaginal, elbow_gloves, heart-shaped_pupils, open_mouth, spread_legs, breasts_out, clothed_sex, nipples, overflow, 1boy, 2boys, censored, fingerless_gloves, group_sex, medium_breasts, multiple_penises, torn_clothes | | 1 | 6 |  |  |  |  |  | 1girl, nipples, solo, thighhighs, blush, cross-laced_footwear, looking_at_viewer, nude, open_mouth, smile, thigh_boots, pussy, medium_breasts, spread_legs | | 2 | 6 |  |  |  |  |  | 1girl, elbow_gloves, solo, spear, thighhighs, zettai_ryouiki, dress, smile, thigh_boots, open_mouth, sword, breastplate, fingerless_gloves, full_body, holding, looking_at_viewer, pegasus_knight_uniform_(fire_emblem), shoulder_armor | | 3 | 6 |  |  |  |  |  | 1girl, bangs, belt, elbow_gloves, full_body, open_mouth, sheath, short_dress, shoulder_armor, solo, sword, thigh_boots, thighhighs, holding_weapon, shiny_hair, simple_background, spear, white_background, white_gloves, gold_trim, leg_up, medium_breasts, pelvic_curtain, thighs, white_dress, white_footwear, zettai_ryouiki, high_heels, sleeveless | | 4 | 11 |  |  |  |  |  | rabbit_ears, 1girl, fake_animal_ears, smile, solo, open_mouth, see-through, thighhighs, hair_ornament, choker, easter_egg, white_gloves, dress, flower, full_body, simple_background, looking_at_viewer, official_alternate_costume, shorts, white_background, bangs, holding, puffy_short_sleeves, shiny_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | hetero | 1girl | blush | cum_in_pussy | thighhighs | armor | solo_focus | vaginal | elbow_gloves | heart-shaped_pupils | open_mouth | spread_legs | breasts_out | clothed_sex | nipples | overflow | 1boy | 2boys | censored | fingerless_gloves | group_sex | medium_breasts | multiple_penises | torn_clothes | solo | cross-laced_footwear | looking_at_viewer | nude | smile | thigh_boots | pussy | spear | zettai_ryouiki | dress | sword | breastplate | full_body | holding | pegasus_knight_uniform_(fire_emblem) | shoulder_armor | bangs | belt | sheath | short_dress | holding_weapon | shiny_hair | simple_background | white_background | white_gloves | gold_trim | leg_up | pelvic_curtain | thighs | white_dress | white_footwear | high_heels | sleeveless | rabbit_ears | fake_animal_ears | see-through | hair_ornament | choker | easter_egg | flower | official_alternate_costume | shorts | puffy_short_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------|:--------|:--------|:---------------|:-------------|:--------|:-------------|:----------|:---------------|:----------------------|:-------------|:--------------|:--------------|:--------------|:----------|:-----------|:-------|:--------|:-----------|:--------------------|:------------|:-----------------|:-------------------|:---------------|:-------|:-----------------------|:--------------------|:-------|:--------|:--------------|:--------|:--------|:-----------------|:--------|:--------|:--------------|:------------|:----------|:---------------------------------------|:-----------------|:--------|:-------|:---------|:--------------|:-----------------|:-------------|:--------------------|:-------------------|:---------------|:------------|:---------|:-----------------|:---------|:--------------|:-----------------|:-------------|:-------------|:--------------|:-------------------|:--------------|:----------------|:---------|:-------------|:---------|:-----------------------------|:---------|:----------------------| | 0 | 9 |  |  |  |  |  | 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 | 6 |  |  |  |  |  | | X | X | | X | | | | | | X | X | | | X | | | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 6 |  |  |  |  |  | | X | | | X | | | | X | | X | | | | | | | | | X | | | | | X | | X | | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 |  |  |  |  |  | | X | | | X | | | | X | | X | | | | | | | | | | | X | | | 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 |  |  |  |  |  | | 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/est_fireemblem

2024-01-17T13:31:06+00:00

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

2024-01-17T13:54:20+00:00

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

Dataset of est/エスト (Fire Emblem) ================================ This is the dataset of est/エスト (Fire Emblem), containing 103 images and their tags. The core tags of this character are 'short\_hair, pink\_hair, breasts, red\_hair, pink\_eyes, headband, red\_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

5b55289694668673fc16cad900ae7a2ab2c91f47

# Dataset of sharon/シャロン (Fire Emblem) This is the dataset of sharon/シャロン (Fire Emblem), containing 196 images and their tags. The core tags of this character are `blonde_hair, green_eyes, long_hair, braid, breasts, crown_braid`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 196 | 202.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sharon_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 196 | 132.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sharon_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 415 | 251.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sharon_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 196 | 185.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sharon_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 415 | 326.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sharon_fireemblem/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/sharon_fireemblem', 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 | 14 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, sex, blush, medium_breasts, penis, solo_focus, open_mouth, completely_nude, cum_in_pussy, vaginal, navel, spread_legs, mosaic_censoring, thighhighs | | 1 | 18 |  |  |  |  |  | fake_animal_ears, rabbit_ears, smile, 1girl, solo, simple_background, open_mouth, white_gloves, multicolored_hair, looking_at_viewer, rabbit_tail, pantyhose, blush, playboy_bunny, easter_egg, medium_breasts, pink_hair, cleavage_cutout, one_eye_closed, ponytail | | 2 | 21 |  |  |  |  |  | 1girl, simple_background, solo, smile, armor, gloves, looking_at_viewer, white_background, multicolored_hair, open_mouth, blush, cape, upper_body | | 3 | 16 |  |  |  |  |  | 1girl, armor, cape, solo, spear, looking_at_viewer, simple_background, smile, thighhighs, brown_gloves, holding_polearm, open_mouth, skirt, white_background, gradient_hair, pink_hair, shield, thigh_boots | | 4 | 5 |  |  |  |  |  | 1girl, kimono, obi, open_mouth, solo, floral_print, hair_ornament, long_sleeves, smile, wide_sleeves, bangs, flower, gradient_hair, looking_at_viewer, pink_hair, full_body, fur_trim, gradient_clothes, holding, low-tied_long_hair, sandals, shiny_hair, tabi, transparent_background, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | nipples | sex | blush | medium_breasts | penis | solo_focus | open_mouth | completely_nude | cum_in_pussy | vaginal | navel | spread_legs | mosaic_censoring | thighhighs | fake_animal_ears | rabbit_ears | smile | solo | simple_background | white_gloves | multicolored_hair | looking_at_viewer | rabbit_tail | pantyhose | playboy_bunny | easter_egg | pink_hair | cleavage_cutout | one_eye_closed | ponytail | armor | gloves | white_background | cape | upper_body | spear | brown_gloves | holding_polearm | skirt | gradient_hair | shield | thigh_boots | kimono | obi | floral_print | hair_ornament | long_sleeves | wide_sleeves | bangs | flower | full_body | fur_trim | gradient_clothes | holding | low-tied_long_hair | sandals | shiny_hair | tabi | transparent_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:----------|:------|:--------|:-----------------|:--------|:-------------|:-------------|:------------------|:---------------|:----------|:--------|:--------------|:-------------------|:-------------|:-------------------|:--------------|:--------|:-------|:--------------------|:---------------|:--------------------|:--------------------|:--------------|:------------|:----------------|:-------------|:------------|:------------------|:-----------------|:-----------|:--------|:---------|:-------------------|:-------|:-------------|:--------|:---------------|:------------------|:--------|:----------------|:---------|:--------------|:---------|:------|:---------------|:----------------|:---------------|:---------------|:--------|:---------|:------------|:-----------|:-------------------|:----------|:---------------------|:----------|:-------------|:-------|:-------------------------| | 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 18 |  |  |  |  |  | | X | | | | X | X | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 21 |  |  |  |  |  | | X | | | | X | | | | X | | | | | | | | | | X | X | X | | X | X | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 16 |  |  |  |  |  | | X | | | | | | | | X | | | | | | | X | | | X | X | X | | | X | | | | | X | | | | X | | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | | X | | | | | | | | 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/sharon_fireemblem

2024-01-17T13:31:40+00:00

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

2024-01-17T14:19:58+00:00

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

Dataset of sharon/シャロン (Fire Emblem) ==================================== This is the dataset of sharon/シャロン (Fire Emblem), containing 196 images and their tags. The core tags of this character are 'blonde\_hair, green\_eyes, long\_hair, braid, breasts, crown\_braid', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

f7182dca4f1ae3a0e31b4fb2ab18834eeb4b7d77

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

tmnam20/VietnameseBookCorpus-raw-parquet

2024-01-17T13:35:23+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4928921669, "num_examples": 19287}], "download_size": 2543402734, "dataset_size": 4928921669}}

2024-01-17T13:41:47+00:00

TAGS #region-us

# Dataset Card for "VietnameseBookCorpus-raw-parquet" More Information needed

ad7ff55d87443132aadff208a19b700e676e2021

# Portuguese-Corpus ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://nkluge-correa.github.io/TeenyTinyLlama/ - **Repository:** https://github.com/Nkluge-correa/TeenyTinyLlama - **Paper:** [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640) - **Point of Contact:** [AIRES at PUCRS](mailto:[email protected]) ### Dataset Summary Portuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-generation&language=language:pt&sort=trending). In a tokenized format, the dataset (uncompressed) weighs 50 GB and has approximately 4.1B tokens. This version does not have instructional content. ### Supported Tasks and Leaderboards This dataset can be utilized for tasks involving language modeling. ### Languages Portuguese. ## Dataset Structure ### Data Instances The dataset consists of the following features: - **text:** a string of text in Portuguese. - **metadata:** the source where that string originated. ### Data Fields ```python { "text": "A inteligência artificial (de sigla: IA; do inglês: artificial intelligence, de sigla: AI) é um campo de estudo multidisciplinar que abrange varias áreas do conhecimento.", "metadata": "source: https://huggingface.co/datasets/graelo/wikipedia" } ``` ### Data Splits Available splits are `train`. ```python from datasets import load_dataset dataset = load_dataset("nicholasKluge/Pt-Corpus", split='train') # If you don't want to download the entire dataset, set streaming to `True` dataset = load_dataset("nicholasKluge/Pt-Corpus", split='train', streaming=True) ``` ## Dataset Creation ### Curation Rationale This dataset was developed are part of the [TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese](https://arxiv.org/abs/2401.16640) paper. In this study, we document the development of open-foundation models tailored for use in low-resource settings, their limitations, and their benefits. ### Source Data #### Initial Data Collection and Normalization We utilized some of the filters used in Rae et al. ([2021](https://arxiv.org/abs/2112.11446)), besides using a [fine-tuned BERTimbau](https://huggingface.co/nicholasKluge/ToxicityModelPT) to exclude samples classified above a pre-defined toxicity threshold. #### Who are the source language producers? All text samples are native to Portuguese or translated from other languages to Portuguese (slight contamination of other languages should also be expected). ### Annotations #### Annotation process Portuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the [Hub](https://huggingface.co/datasets?task_categories=task_categories:text-generation&language=language:pt&sort=trending). We utilized some of the filters used in Rae et al. ([2021](https://arxiv.org/abs/2112.11446)), besides using a [fine-tuned BERTimbau](https://huggingface.co/nicholasKluge/ToxicityModelPT) to exclude samples classified above a pre-defined toxicity threshold. #### Who are the annotators? [Nicholas Kluge Corrêa](mailto:[email protected]). ### Personal and Sensitive Information This dataset, sourced from web scraping, may potentially contain personal and sensitive information, alongside offensive, toxic, and disturbing language. ## Considerations for Using the Data ### Social Impact of Dataset The presence of personal and sensitive information within the dataset raises concerns about privacy and data protection, potentially leading to breaches of individuals' confidentiality and security. Furthermore, the inclusion of offensive, toxic, and disturbing language in the dataset poses risks of perpetuating harmful behaviors and attitudes, contributing to the normalization of hate speech and online toxicity. Therefore, careful handling and ethical considerations are essential to mitigate these potential social impacts and promote responsible dataset use. ### Discussion of Biases The inclusion of offensive, toxic, and disturbing language in the dataset poses risks of perpetuating harmful behaviors and attitudes, contributing to the normalization of hate speech and online toxicity. ### Other Known Limitations A significant portion of the data within the dataset has been translated using translation engines, potentially resulting in corrupted samples of both language and code. While useful for quickly converting text between languages, translation engines often struggle with accurately preserving the syntax, semantics, and context of programming languages. As a result, the translated code may contain errors, syntax inconsistencies, or even introduce vulnerabilities, rendering it unreliable or unusable for its intended purpose. ## Additional Information ### Dataset Curators [Nicholas Kluge Corrêa](mailto:[email protected]). ### Licensing Information The following datasets (_only training splits are a part of the corpus_) and respective licenses form the Portuguese-Corpus: - [Wikipedia](https://huggingface.co/datasets/graelo/wikipedia) (License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)) - [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX) (License: [ODC-By](https://opendatacommons.org/licenses/by/1-0/), [cc0-1.0](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information)) - [OSCAR](https://huggingface.co/datasets/eduagarcia/OSCAR-2301-pt_dedup) (License: [cc0-1.0](https://huggingface.co/datasets/oscar-corpus/OSCAR-2301#licensing-information)) - [CCc100](https://huggingface.co/datasets/eduagarcia/cc100-pt) (License: [Common Crawl terms of use](https://commoncrawl.org/terms-of-use/)) - [Roots Wikiquote](https://huggingface.co/datasets/bigscience-data/roots_pt_wikiquote) (License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)) - [Roots Ted Talks](https://huggingface.co/datasets/bigscience-data/roots_pt_ted_talks_iwslt) (License: [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en)) ### Citation Information ```latex @misc{correa24ttllama, title = {TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese}, author = {Corr{\^e}a, Nicholas Kluge and Falk, Sophia and Fatimah, Shiza and Sen, Aniket and De Oliveira, Nythamar}, journal={arXiv preprint arXiv:2401.16640}, year={2024} } ``` ### Contributions If you would like to contribute, contact me at [[email protected]](mailto:[email protected])!

nicholasKluge/Pt-Corpus

2024-01-17T13:38:48+00:00

{"language": ["pt"], "license": "other", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "pretty_name": "Pt-Corpus", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "metadata", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16220765175.988096, "num_examples": 5768246}], "download_size": 11478008666, "dataset_size": 16220765175.988096}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["portuguese", "language-modeling"]}

2024-02-15T18:08:39+00:00

TAGS #task_categories-text-generation #size_categories-1M<n<10M #language-Portuguese #license-other #portuguese #language-modeling #arxiv-2401.16640 #arxiv-2112.11446 #region-us

# Portuguese-Corpus ## Table of Contents - Table of Contents - Dataset Description - Dataset Summary - Supported Tasks and Leaderboards - Languages - Dataset Structure - Data Instances - Data Fields - Data Splits - Dataset Creation - Curation Rationale - Source Data - Annotations - 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 - Citation Information - Contributions ## Dataset Description - Homepage: URL - Repository: URL - Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese - Point of Contact: AIRES at PUCRS ### Dataset Summary Portuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the Hub. In a tokenized format, the dataset (uncompressed) weighs 50 GB and has approximately 4.1B tokens. This version does not have instructional content. ### Supported Tasks and Leaderboards This dataset can be utilized for tasks involving language modeling. ### Languages Portuguese. ## Dataset Structure ### Data Instances The dataset consists of the following features: - text: a string of text in Portuguese. - metadata: the source where that string originated. ### Data Fields ### Data Splits Available splits are 'train'. ## Dataset Creation ### Curation Rationale This dataset was developed are part of the TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese paper. In this study, we document the development of open-foundation models tailored for use in low-resource settings, their limitations, and their benefits. ### Source Data #### Initial Data Collection and Normalization We utilized some of the filters used in Rae et al. (2021), besides using a fine-tuned BERTimbau to exclude samples classified above a pre-defined toxicity threshold. #### Who are the source language producers? All text samples are native to Portuguese or translated from other languages to Portuguese (slight contamination of other languages should also be expected). ### Annotations #### Annotation process Portuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the Hub. We utilized some of the filters used in Rae et al. (2021), besides using a fine-tuned BERTimbau to exclude samples classified above a pre-defined toxicity threshold. #### Who are the annotators? Nicholas Kluge Corrêa. ### Personal and Sensitive Information This dataset, sourced from web scraping, may potentially contain personal and sensitive information, alongside offensive, toxic, and disturbing language. ## Considerations for Using the Data ### Social Impact of Dataset The presence of personal and sensitive information within the dataset raises concerns about privacy and data protection, potentially leading to breaches of individuals' confidentiality and security. Furthermore, the inclusion of offensive, toxic, and disturbing language in the dataset poses risks of perpetuating harmful behaviors and attitudes, contributing to the normalization of hate speech and online toxicity. Therefore, careful handling and ethical considerations are essential to mitigate these potential social impacts and promote responsible dataset use. ### Discussion of Biases The inclusion of offensive, toxic, and disturbing language in the dataset poses risks of perpetuating harmful behaviors and attitudes, contributing to the normalization of hate speech and online toxicity. ### Other Known Limitations A significant portion of the data within the dataset has been translated using translation engines, potentially resulting in corrupted samples of both language and code. While useful for quickly converting text between languages, translation engines often struggle with accurately preserving the syntax, semantics, and context of programming languages. As a result, the translated code may contain errors, syntax inconsistencies, or even introduce vulnerabilities, rendering it unreliable or unusable for its intended purpose. ## Additional Information ### Dataset Curators Nicholas Kluge Corrêa. ### Licensing Information The following datasets (_only training splits are a part of the corpus_) and respective licenses form the Portuguese-Corpus: - Wikipedia (License: CC BY-SA 3.0) - CulturaX (License: ODC-By, cc0-1.0) - OSCAR (License: cc0-1.0) - CCc100 (License: Common Crawl terms of use) - Roots Wikiquote (License: CC BY-SA 3.0) - Roots Ted Talks (License: CC BY-NC-ND 4.0) ### Contributions If you would like to contribute, contact me at nicholas@URL!

081222a5e3aedeb04b84abde27abf98e130fe602

# This is a dataset containing 10,000 posts from Kaggle and 60,000 comments related to those posts in the question-answer topic. ## Data Fields ### kaggle_post 1. 'pseudo', The question authors. 2. 'title', Title of the Post. 3. 'question', The question's body. 4. 'vote', Voting on Kaggle is similar to liking. 5. 'medal', I will share with you the Kaggle medal system, which can be found at https://www.kaggle.com/progression. The system awards medals to users based on their performance. 6. 'nbr_comment', The comment number. 7. 'date', The post date. 8. 'url_post', Link the comment dataset using the post URL. 9. 'url_competition', If the question is related to a competition, include the competition URL. 10. 'rank_competition', The author's rank in the competition. ### kaggle_comment 1. 'pseudo_com', the answer authors. 2. 'answer', The answer's body. 3. 'vote_com', The answer's number of likes 4. 'medal_com', I will share with you the Kaggle medal system, which can be found at https://www.kaggle.com/progression. The system awards medals to users based on their performance. 5. 'date_com', The answer date. 6. 'url_post', Link the comment dataset using the post URL. 7. 'rank_competition', the author's rank in the competition. Data scraping by Mathieu Duverne on august 2023.

Raaxx/Kaggle-post-and-comments-question-answer-topic

2024-01-17T13:42:26+00:00

{"language": ["en"], "task_categories": ["question-answering"]}

2024-01-17T14:27:40+00:00

TAGS #task_categories-question-answering #language-English #region-us

# This is a dataset containing 10,000 posts from Kaggle and 60,000 comments related to those posts in the question-answer topic. ## Data Fields ### kaggle_post 1. 'pseudo', The question authors. 2. 'title', Title of the Post. 3. 'question', The question's body. 4. 'vote', Voting on Kaggle is similar to liking. 5. 'medal', I will share with you the Kaggle medal system, which can be found at URL The system awards medals to users based on their performance. 6. 'nbr_comment', The comment number. 7. 'date', The post date. 8. 'url_post', Link the comment dataset using the post URL. 9. 'url_competition', If the question is related to a competition, include the competition URL. 10. 'rank_competition', The author's rank in the competition. ### kaggle_comment 1. 'pseudo_com', the answer authors. 2. 'answer', The answer's body. 3. 'vote_com', The answer's number of likes 4. 'medal_com', I will share with you the Kaggle medal system, which can be found at URL The system awards medals to users based on their performance. 5. 'date_com', The answer date. 6. 'url_post', Link the comment dataset using the post URL. 7. 'rank_competition', the author's rank in the competition. Data scraping by Mathieu Duverne on august 2023.

31298db19f3d7cabf930104c28afc78aa86de3d3

# Dataset of elaice/イレース (Fire Emblem) This is the dataset of elaice/イレース (Fire Emblem), containing 138 images and their tags. The core tags of this character are `purple_hair, long_hair, purple_eyes, twintails, 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 | 138 | 111.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elaice_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 138 | 77.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elaice_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 258 | 141.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elaice_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 138 | 103.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elaice_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 258 | 175.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elaice_fireemblem/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/elaice_fireemblem', 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 |  |  |  |  |  | 1girl, hetero, multiple_penises, solo_focus, mosaic_censoring, gangbang, nipples, vaginal, blush, cum_in_pussy, medium_breasts, 2boys, 3boys, circlet, facial, fellatio, handjob, testicles | | 1 | 5 |  |  |  |  |  | 1girl, nipples, solo, medium_breasts, open_mouth, blush, completely_nude, navel, artist_name, circlet, food, hair_flower, large_breasts, looking_at_viewer, pussy, signature, simple_background, sitting | | 2 | 8 |  |  |  |  |  | 1girl, cape, circlet, skirt, solo, low-tied_long_hair, book, simple_background, sitting, white_background | | 3 | 7 |  |  |  |  |  | 1girl, circlet, full_body, short_sleeves, simple_background, solo, bangs, capelet, hood_down, low_twintails, white_footwear, miniskirt, shiny_hair, white_background, belt_pouch, closed_mouth, purple_skirt, holding_book, jewelry, knee_boots, looking_at_viewer, magic, open_book | | 4 | 9 |  |  |  |  |  | 1girl, alternate_costume, solo, candy, halloween_costume, holding, long_sleeves, circlet, dress, simple_background, cape, open_mouth, white_background, white_pantyhose, boots, eating, looking_at_viewer, purple_gloves | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | multiple_penises | solo_focus | mosaic_censoring | gangbang | nipples | vaginal | blush | cum_in_pussy | medium_breasts | 2boys | 3boys | circlet | facial | fellatio | handjob | testicles | solo | open_mouth | completely_nude | navel | artist_name | food | hair_flower | large_breasts | looking_at_viewer | pussy | signature | simple_background | sitting | cape | skirt | low-tied_long_hair | book | white_background | full_body | short_sleeves | bangs | capelet | hood_down | low_twintails | white_footwear | miniskirt | shiny_hair | belt_pouch | closed_mouth | purple_skirt | holding_book | jewelry | knee_boots | magic | open_book | alternate_costume | candy | halloween_costume | holding | long_sleeves | dress | white_pantyhose | boots | eating | purple_gloves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------------------|:-------------|:-------------------|:-----------|:----------|:----------|:--------|:---------------|:-----------------|:--------|:--------|:----------|:---------|:-----------|:----------|:------------|:-------|:-------------|:------------------|:--------|:--------------|:-------|:--------------|:----------------|:--------------------|:--------|:------------|:--------------------|:----------|:-------|:--------|:---------------------|:-------|:-------------------|:------------|:----------------|:--------|:----------|:------------|:----------------|:-----------------|:------------|:-------------|:-------------|:---------------|:---------------|:---------------|:----------|:-------------|:--------|:------------|:--------------------|:--------|:--------------------|:----------|:---------------|:--------|:------------------|:--------|:---------|:----------------| | 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 |  |  |  |  |  | X | | | | | | X | | X | | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 |  |  |  |  |  | X | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | X | | | | | X | | | | | | | | X | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 4 | 9 |  |  |  |  |  | X | | | | | | | | | | | | | X | | | | | X | X | | | | | | | X | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |

CyberHarem/elaice_fireemblem

2024-01-17T13:47:37+00:00

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

2024-01-17T14:12:05+00:00

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

Dataset of elaice/イレース (Fire Emblem) ==================================== This is the dataset of elaice/イレース (Fire Emblem), containing 138 images and their tags. The core tags of this character are 'purple\_hair, long\_hair, purple\_eyes, twintails, 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

660a5b20463b76b2c46f6e404e3f216eb881a3cd

# Dataset of nepenee/ネフェニー (Fire Emblem) This is the dataset of nepenee/ネフェニー (Fire Emblem), containing 189 images and their tags. The core tags of this character are `green_hair, long_hair, green_eyes, 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 | 189 | 199.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nepenee_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 189 | 132.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nepenee_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 385 | 240.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nepenee_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 189 | 185.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nepenee_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 385 | 312.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nepenee_fireemblem/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/nepenee_fireemblem', 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 |  |  |  |  |  | 1girl, blue_armor, breastplate, helmet, solo, spear, thighhighs, blue_eyes, boots, skirt, full_body, holding_weapon, shield, simple_background, belt, white_background, detached_sleeves, looking_at_viewer | | 1 | 5 |  |  |  |  |  | 1girl, blue_armor, helmet, solo, breastplate, spear, shield, thighhighs, belt | | 2 | 7 |  |  |  |  |  | 1girl, hetero, solo_focus, vaginal, blush, nipples, rape, armor, helmet, multiple_penises, cum_in_pussy, large_breasts, mosaic_censoring, spread_legs, tears, thighhighs, torn_clothes, 3boys, gangbang, medium_breasts, mmf_threesome, straddling | | 3 | 7 |  |  |  |  |  | 1boy, 1girl, helmet, hetero, day, large_breasts, nipples, open_mouth, blush, cum_in_pussy, penis, solo_focus, vaginal, bar_censor, blue_armor, breasts_out, clothed_sex, overflow, very_long_hair, anus, blue_sky, outdoors | | 4 | 6 |  |  |  |  |  | 1girl, day, looking_at_viewer, outdoors, solo, cloud, large_breasts, navel, black_bikini, blue_sky, blush, cleavage, helmet, ocean | | 5 | 5 |  |  |  |  |  | 1girl, blue_dress, collarbone, helmet, long_sleeves, medium_breasts, solo, veil, wide_sleeves, aqua_eyes, bangs, blue_footwear, full_body, gradient_hair, puffy_sleeves, simple_background, frilled_sleeves, looking_at_viewer, shoes, smile, white_background, arrow_(projectile), bare_shoulders, blue_armor, closed_mouth, detached_sleeves, holding_bow_(weapon), looking_away, standing | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_armor | breastplate | helmet | solo | spear | thighhighs | blue_eyes | boots | skirt | full_body | holding_weapon | shield | simple_background | belt | white_background | detached_sleeves | looking_at_viewer | hetero | solo_focus | vaginal | blush | nipples | rape | armor | multiple_penises | cum_in_pussy | large_breasts | mosaic_censoring | spread_legs | tears | torn_clothes | 3boys | gangbang | medium_breasts | mmf_threesome | straddling | 1boy | day | open_mouth | penis | bar_censor | breasts_out | clothed_sex | overflow | very_long_hair | anus | blue_sky | outdoors | cloud | navel | black_bikini | cleavage | ocean | blue_dress | collarbone | long_sleeves | veil | wide_sleeves | aqua_eyes | bangs | blue_footwear | gradient_hair | puffy_sleeves | frilled_sleeves | shoes | smile | arrow_(projectile) | bare_shoulders | closed_mouth | holding_bow_(weapon) | looking_away | standing | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------|:---------|:-------|:--------|:-------------|:------------|:--------|:--------|:------------|:-----------------|:---------|:--------------------|:-------|:-------------------|:-------------------|:--------------------|:---------|:-------------|:----------|:--------|:----------|:-------|:--------|:-------------------|:---------------|:----------------|:-------------------|:--------------|:--------|:---------------|:--------|:-----------|:-----------------|:----------------|:-------------|:-------|:------|:-------------|:--------|:-------------|:--------------|:--------------|:-----------|:-----------------|:-------|:-----------|:-----------|:--------|:--------|:---------------|:-----------|:--------|:-------------|:-------------|:---------------|:-------|:---------------|:------------|:--------|:----------------|:----------------|:----------------|:------------------|:--------|:--------|:---------------------|:-----------------|:---------------|:-----------------------|:---------------|:-----------| | 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | | | X | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 |  |  |  |  |  | X | X | | X | | | | | | | | | | | | | | | X | X | X | X | X | | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | | | X | | | | X | | | | | | X | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 5 | 5 |  |  |  |  |  | X | X | | X | X | | | | | | X | | | 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/nepenee_fireemblem

2024-01-17T13:47:39+00:00

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

2024-01-17T14:19:14+00:00

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

Dataset of nepenee/ネフェニー (Fire Emblem) ====================================== This is the dataset of nepenee/ネフェニー (Fire Emblem), containing 189 images and their tags. The core tags of this character are 'green\_hair, long\_hair, green\_eyes, 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

11087f376ed45b93d2a93bb9f8efcb4bf78b8c29

# Dataset of kazahana/カザハナ (Fire Emblem) This is the dataset of kazahana/カザハナ (Fire Emblem), containing 127 images and their tags. The core tags of this character are `long_hair, brown_hair, brown_eyes, headband, 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 | 127 | 144.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kazahana_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 127 | 88.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kazahana_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 280 | 175.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kazahana_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 127 | 131.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kazahana_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 280 | 232.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kazahana_fireemblem/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/kazahana_fireemblem', 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 |  |  |  |  |  | 1boy, 1girl, hetero, penis, sex, blush, navel, nipples, nude, spread_legs, vaginal, anal, bar_censor, cum_in_pussy, fingering, large_breasts, open_mouth, pussy_juice, rape, sweat, tears, clenched_teeth, female_ejaculation, interspecies, medium_breasts, saliva, smile, solo_focus, thighhighs | | 1 | 9 |  |  |  |  |  | navel, nipples, 1girl, pussy, blush, medium_breasts, female_pubic_hair, completely_nude, smile, solo, hetero, open_mouth, penis, sex, small_breasts, vaginal | | 2 | 39 |  |  |  |  |  | 1girl, solo, armor, katana, simple_background, smile, japanese_clothes, holding_weapon, open_mouth, thighhighs, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | hetero | penis | sex | blush | navel | nipples | nude | spread_legs | vaginal | anal | bar_censor | cum_in_pussy | fingering | large_breasts | open_mouth | pussy_juice | rape | sweat | tears | clenched_teeth | female_ejaculation | interspecies | medium_breasts | saliva | smile | solo_focus | thighhighs | pussy | female_pubic_hair | completely_nude | solo | small_breasts | armor | katana | simple_background | japanese_clothes | holding_weapon | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:--------|:------|:--------|:--------|:----------|:-------|:--------------|:----------|:-------|:-------------|:---------------|:------------|:----------------|:-------------|:--------------|:-------|:--------|:--------|:-----------------|:---------------------|:---------------|:-----------------|:---------|:--------|:-------------|:-------------|:--------|:--------------------|:------------------|:-------|:----------------|:--------|:---------|:--------------------|:-------------------|:-----------------|:-------------------| | 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 9 |  |  |  |  |  | | X | X | X | X | X | X | X | | | X | | | | | | X | | | | | | | | X | | X | | | X | X | X | X | X | | | | | | | | 2 | 39 |  |  |  |  |  | | X | | | | | | | | | | | | | | | X | | | | | | | | | | X | | X | | | | X | | X | X | X | X | X | X |

CyberHarem/kazahana_fireemblem

2024-01-17T13:47:47+00:00

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

2024-01-17T14:17:22+00:00

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

Dataset of kazahana/カザハナ (Fire Emblem) ====================================== This is the dataset of kazahana/カザハナ (Fire Emblem), containing 127 images and their tags. The core tags of this character are 'long\_hair, brown\_hair, brown\_eyes, headband, 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

e3237a269a0af3c2db102d551e3e22994b62c8d3

# Dataset of veronica/ヴェロニカ (Fire Emblem) This is the dataset of veronica/ヴェロニカ (Fire Emblem), containing 215 images and their tags. The core tags of this character are `red_eyes, grey_hair, long_hair, hair_ornament, crown, 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 | 215 | 242.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/veronica_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 215 | 150.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/veronica_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 467 | 308.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/veronica_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 215 | 218.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/veronica_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 467 | 405.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/veronica_fireemblem/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/veronica_fireemblem', 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 |  |  |  |  |  | 1girl, simple_background, solo, closed_mouth, looking_at_viewer, white_background, upper_body | | 1 | 5 |  |  |  |  |  | 1girl, black_armor, simple_background, solo, closed_mouth, shoulder_armor, upper_body, white_background, looking_at_viewer, cape | | 2 | 16 |  |  |  |  |  | 1girl, black_gloves, cape, shoulder_armor, long_sleeves, solo, black_armor, holding_staff, closed_mouth, simple_background, high_heels | | 3 | 5 |  |  |  |  |  | 1girl, dress, easter_egg, fake_animal_ears, hair_flower, rabbit_ears, simple_background, solo, white_gloves, open_mouth, see-through, wrist_cuffs, rabbit_tail, cleavage_cutout, holding, twitter_username, white_pantyhose | | 4 | 15 |  |  |  |  |  | 1girl, nipples, 1boy, blush, hetero, open_mouth, small_breasts, solo_focus, penis, vaginal, sex, bar_censor, cum_in_pussy, navel, on_back, nude, torn_clothes | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | simple_background | solo | closed_mouth | looking_at_viewer | white_background | upper_body | black_armor | shoulder_armor | cape | black_gloves | long_sleeves | holding_staff | high_heels | dress | easter_egg | fake_animal_ears | hair_flower | rabbit_ears | white_gloves | open_mouth | see-through | wrist_cuffs | rabbit_tail | cleavage_cutout | holding | twitter_username | white_pantyhose | nipples | 1boy | blush | hetero | small_breasts | solo_focus | penis | vaginal | sex | bar_censor | cum_in_pussy | navel | on_back | nude | torn_clothes | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:--------------------|:-------------------|:-------------|:--------------|:-----------------|:-------|:---------------|:---------------|:----------------|:-------------|:--------|:-------------|:-------------------|:--------------|:--------------|:---------------|:-------------|:--------------|:--------------|:--------------|:------------------|:----------|:-------------------|:------------------|:----------|:-------|:--------|:---------|:----------------|:-------------|:--------|:----------|:------|:-------------|:---------------|:--------|:----------|:-------|:---------------| | 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 16 |  |  |  |  |  | X | X | X | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | X | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 4 | 15 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/veronica_fireemblem

2024-01-17T13:47:50+00:00

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

2024-01-17T14:34:35+00:00

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

Dataset of veronica/ヴェロニカ (Fire Emblem) ======================================= This is the dataset of veronica/ヴェロニカ (Fire Emblem), containing 215 images and their tags. The core tags of this character are 'red\_eyes, grey\_hair, long\_hair, hair\_ornament, crown, 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

1261f8fd538d07e6aa37012e91b6f047f1e36ca8

# Dataset of chloe/クロエ (Fire Emblem) This is the dataset of chloe/クロエ (Fire Emblem), containing 177 images and their tags. The core tags of this character are `breasts, long_hair, green_eyes, braid, large_breasts, aqua_hair, bangs, earrings, bow, hair_bow, blue_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 | 177 | 292.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 177 | 149.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 440 | 327.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 177 | 250.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 440 | 499.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chloe_fireemblem/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/chloe_fireemblem', 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 |  |  |  |  |  | 1girl, cleavage, elbow_gloves, looking_at_viewer, shoulder_armor, smile, white_gloves, simple_background, solo, upper_body, jewelry, blush, covered_navel, green_hair, white_background | | 1 | 5 |  |  |  |  |  | 1girl, breastplate, cleavage, elbow_gloves, solo, white_gloves, covered_navel, green_hair, jewelry, looking_at_viewer, open_mouth, shoulder_armor, :d | | 2 | 9 |  |  |  |  |  | 1girl, elbow_gloves, solo, white_gloves, breastplate, cleavage, looking_at_viewer, smile, jewelry, pegasus_knight_uniform_(fire_emblem), shoulder_armor, holding_polearm, spear, covered_navel | | 3 | 9 |  |  |  |  |  | 1girl, cleavage, smile, solo, blush, looking_at_viewer, collarbone, necklace, upper_body, green_dress, green_hair, closed_mouth, holding, short_sleeves, simple_background, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | elbow_gloves | looking_at_viewer | shoulder_armor | smile | white_gloves | simple_background | solo | upper_body | jewelry | blush | covered_navel | green_hair | white_background | breastplate | open_mouth | :d | pegasus_knight_uniform_(fire_emblem) | holding_polearm | spear | collarbone | necklace | green_dress | closed_mouth | holding | short_sleeves | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------------|:--------------------|:-----------------|:--------|:---------------|:--------------------|:-------|:-------------|:----------|:--------|:----------------|:-------------|:-------------------|:--------------|:-------------|:-----|:---------------------------------------|:------------------|:--------|:-------------|:-----------|:--------------|:---------------|:----------|:----------------| | 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | | X | | X | | X | X | | X | X | X | | | | | | | | | | | 2 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | | X | | X | | X | | | X | | | X | X | X | | | | | | | | 3 | 9 |  |  |  |  |  | X | X | | X | | X | | X | X | X | | X | | X | X | | | | | | | X | X | X | X | X | X |

CyberHarem/chloe_fireemblem

2024-01-17T14:03:28+00:00

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

2024-01-17T14:39:30+00:00

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

Dataset of chloe/クロエ (Fire Emblem) ================================== This is the dataset of chloe/クロエ (Fire Emblem), containing 177 images and their tags. The core tags of this character are 'breasts, long\_hair, green\_eyes, braid, large\_breasts, aqua\_hair, bangs, earrings, bow, hair\_bow, blue\_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

bcee32dd3f400311397d1af1111b3b5bbe6db2a6

# Dataset of ferry/フュリー (Fire Emblem) This is the dataset of ferry/フュリー (Fire Emblem), containing 500 images and their tags. The core tags of this character are `long_hair, green_hair, green_eyes, ponytail, breasts, earrings, bangs, large_breasts, very_long_hair, high_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 | 500 | 723.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ferry_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 409.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ferry_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1211 | 856.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ferry_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 643.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ferry_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1211 | 1.19 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ferry_fireemblem/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/ferry_fireemblem', 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 | 28 |  |  |  |  |  | 1girl, mask_on_head, official_alternate_costume, solo, chest_sarashi, tube_top, bandages, looking_at_viewer, smile, jewelry, single_bare_shoulder, cleavage, bandeau, midriff, blue_skirt, navel, single_sleeve, stomach, holding_weapon, blue_shirt, polearm, white_background, bare_shoulders, simple_background, collarbone, short_sleeves, standing, thighs | | 1 | 5 |  |  |  |  |  | 1girl, blue_dress, fingerless_gloves, holding_sword, jewelry, looking_at_viewer, sheath, solo, katana, pelvic_curtain, short_sleeves, thighs, side_slit, black_gloves, boots, smile | | 2 | 7 |  |  |  |  |  | 1girl, black_gloves, boots, simple_background, white_background, blue_dress, fingerless_gloves, holding_sword, pelvic_curtain, full_body, solo, side_slit | | 3 | 10 |  |  |  |  |  | 1girl, dress, jewelry, solo, arrow_(projectile), fingerless_gloves, holding_bow_(weapon), white_background, feathers, simple_background, quiver, smile, fur_trim, hair_ornament, looking_at_viewer, pelvic_curtain, short_sleeves, cape, full_body, knee_boots, thighs, belt, closed_mouth, elbow_gloves, medium_breasts, shoulder_armor, standing | | 4 | 8 |  |  |  |  |  | 1girl, blush, hair_flower, jewelry, official_alternate_costume, solo, bare_shoulders, blue_bikini, cleavage, looking_at_viewer, choker, collarbone, smile, simple_background, closed_mouth, navel, open_mouth, strapless_bikini, white_background | | 5 | 7 |  |  |  |  |  | 1girl, jewelry, looking_at_viewer, ocean, outdoors, solo, beach, blue_bikini, blue_sky, cleavage, cloud, day, hair_flower, navel, official_alternate_costume, bare_shoulders, smile, strapless_bikini, blush, collarbone, thighs, water, choker, wet | | 6 | 9 |  |  |  |  |  | bare_shoulders, bride, necklace, strapless_dress, wedding_dress, 1girl, hair_flower, official_alternate_costume, white_dress, cleavage, looking_at_viewer, solo, bouquet, open_mouth, smile, bridal_veil, detached_sleeves, gloves, medium_breasts | | 7 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, sweat, open_mouth, mosaic_censoring, nipples, cum, erection, jewelry, licking_penis, nude, tongue_out | | 8 | 11 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, penis, sex, open_mouth, nipples, pussy, vaginal, completely_nude, mosaic_censoring, smile, jewelry, spread_legs, sweat, looking_at_viewer, ass, dark-skinned_male, gloves, lying, navel, straddling, thighhighs | | 9 | 6 |  |  |  |  |  | 1girl, elbow_gloves, armpits, arms_up, blush, looking_at_viewer, solo, thighhighs, white_gloves, armor, open_mouth, pole_dancing, stripper_pole, :d, censored, pussy, spread_legs, sweat, thigh_boots | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | mask_on_head | official_alternate_costume | solo | chest_sarashi | tube_top | bandages | looking_at_viewer | smile | jewelry | single_bare_shoulder | cleavage | bandeau | midriff | blue_skirt | navel | single_sleeve | stomach | holding_weapon | blue_shirt | polearm | white_background | bare_shoulders | simple_background | collarbone | short_sleeves | standing | thighs | blue_dress | fingerless_gloves | holding_sword | sheath | katana | pelvic_curtain | side_slit | black_gloves | boots | full_body | dress | arrow_(projectile) | holding_bow_(weapon) | feathers | quiver | fur_trim | hair_ornament | cape | knee_boots | belt | closed_mouth | elbow_gloves | medium_breasts | shoulder_armor | blush | hair_flower | blue_bikini | choker | open_mouth | strapless_bikini | ocean | outdoors | beach | blue_sky | cloud | day | water | wet | bride | necklace | strapless_dress | wedding_dress | white_dress | bouquet | bridal_veil | detached_sleeves | gloves | 1boy | hetero | solo_focus | sweat | mosaic_censoring | nipples | cum | erection | licking_penis | nude | tongue_out | penis | sex | pussy | vaginal | completely_nude | spread_legs | ass | dark-skinned_male | lying | straddling | thighhighs | armpits | arms_up | white_gloves | armor | pole_dancing | stripper_pole | :d | censored | thigh_boots | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------------------------|:-------|:----------------|:-----------|:-----------|:--------------------|:--------|:----------|:-----------------------|:-----------|:----------|:----------|:-------------|:--------|:----------------|:----------|:-----------------|:-------------|:----------|:-------------------|:-----------------|:--------------------|:-------------|:----------------|:-----------|:---------|:-------------|:--------------------|:----------------|:---------|:---------|:-----------------|:------------|:---------------|:--------|:------------|:--------|:---------------------|:-----------------------|:-----------|:---------|:-----------|:----------------|:-------|:-------------|:-------|:---------------|:---------------|:-----------------|:-----------------|:--------|:--------------|:--------------|:---------|:-------------|:-------------------|:--------|:-----------|:--------|:-----------|:--------|:------|:--------|:------|:--------|:-----------|:------------------|:----------------|:--------------|:----------|:--------------|:-------------------|:---------|:-------|:---------|:-------------|:--------|:-------------------|:----------|:------|:-----------|:----------------|:-------|:-------------|:--------|:------|:--------|:----------|:------------------|:--------------|:------|:--------------------|:--------|:-------------|:-------------|:----------|:----------|:---------------|:--------|:---------------|:----------------|:-----|:-----------|:--------------| | 0 | 28 |  |  |  |  |  | X | X | X | X | 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 | 5 |  |  |  |  |  | X | | | X | | | | X | X | X | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | | | | | X | | X | | | | | X | X | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 10 |  |  |  |  |  | X | | | X | | | | X | 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 | 8 |  |  |  |  |  | X | | X | X | | | | X | X | X | | X | | | | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 7 |  |  |  |  |  | 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 | 9 |  |  |  |  |  | X | | X | X | | | | X | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 |  |  |  |  |  | X | | | | | | | | | X | 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CyberHarem/ferry_fireemblem

2024-01-17T14:03:29+00:00

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

2024-01-17T16:04:38+00:00

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

Dataset of ferry/フュリー (Fire Emblem) =================================== This is the dataset of ferry/フュリー (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'long\_hair, green\_hair, green\_eyes, ponytail, breasts, earrings, bangs, large\_breasts, very\_long\_hair, high\_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

dbb934ad2aaaf7b0bf90b7083bfdcd64d3941e8c

# Dataset of aqua (Fire Emblem) This is the dataset of aqua (Fire Emblem), containing 500 images and their tags. The core tags of this character are `blue_hair, long_hair, yellow_eyes, hair_between_eyes, very_long_hair, 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 | 700.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aqua_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 412.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aqua_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1137 | 805.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aqua_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 631.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aqua_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1137 | 1.07 GiB | [Download](https://huggingface.co/datasets/CyberHarem/aqua_fireemblem/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/aqua_fireemblem', 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 | 19 |  |  |  |  |  | 1girl, elbow_gloves, fingerless_gloves, solo, veil, white_dress, white_gloves, water, open_mouth, looking_at_viewer, necklace, pendant, ribbon | | 1 | 7 |  |  |  |  |  | 1girl, dress, elbow_gloves, looking_at_viewer, solo, fingerless_gloves, water, simple_background, smile, white_background, jewelry, mouth_veil, open_mouth, white_gloves | | 2 | 7 |  |  |  |  |  | 1girl, barefoot, elbow_gloves, fingerless_gloves, solo, veil, anklet, looking_at_viewer, white_dress, white_gloves, full_body, simple_background, spear, white_background, holding_weapon, necklace, water | | 3 | 5 |  |  |  |  |  | 1girl, anklet, barefoot, dress, elbow_gloves, looking_at_viewer, solo, veil, dakimakura_(medium), fingerless_gloves, full_body, on_back, blush, ripples, water | | 4 | 9 |  |  |  |  |  | 1girl, nipples, solo, completely_nude, looking_at_viewer, navel, medium_breasts, simple_background, white_background, veil, smile | | 5 | 5 |  |  |  |  |  | 1girl, blush, completely_nude, nipples, solo, collarbone, large_breasts, looking_at_viewer, veil, closed_mouth, medium_breasts, navel, smile, arms_behind_back, curtains, indoors, onsen, partially_submerged, stomach, water, wet | | 6 | 6 |  |  |  |  |  | 1girl, blush, collarbone, completely_nude, looking_at_viewer, navel, solo, stomach, water, wet, bangs, closed_mouth, day, nipples, outdoors, wading, armpits, cowboy_shot, veil, arm_up, blue_sky, groin, large_breasts, medium_breasts, smile, thighs, tree | | 7 | 8 |  |  |  |  |  | 1girl, bangs, crop_top, looking_at_viewer, midriff, navel, necklace, solo, veil, cleavage, collarbone, holding, closed_mouth, medium_breasts, official_alternate_costume, smile, blue_skirt, circlet, fire, see-through, standing, thighlet, bare_shoulders, blush, cowboy_shot, full_body, hand_up, large_breasts, light_blue_hair, pantyhose, torch | | 8 | 9 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, veil, blush, nipples, large_breasts, looking_at_viewer, male_pubic_hair, mosaic_censoring, paizuri, pov, fellatio, breasts_squeezed_together | | 9 | 5 |  |  |  |  |  | 1girl, obi, solo, veil, full_body, hagoita, holding, sandals, simple_background, wide_sleeves, floral_print, hair_ornament, hanetsuki, long_sleeves, looking_at_viewer, open_mouth, blue_kimono, flower, grey_background, hair_tubes, smile, tabi | | 10 | 9 |  |  |  |  |  | 1girl, cum_in_pussy, hetero, multiple_boys, multiple_penises, nipples, rape, mosaic_censoring, solo_focus, cum_on_body, gangbang, large_breasts, vaginal, veil, blush, asymmetrical_legwear, breast_grab, gloves, grabbing, navel, pantyhose, tears, anal, facial, open_mouth, spread_legs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | fingerless_gloves | solo | veil | white_dress | white_gloves | water | open_mouth | looking_at_viewer | necklace | pendant | ribbon | dress | simple_background | smile | white_background | jewelry | mouth_veil | barefoot | anklet | full_body | spear | holding_weapon | dakimakura_(medium) | on_back | blush | ripples | nipples | completely_nude | navel | medium_breasts | collarbone | large_breasts | closed_mouth | arms_behind_back | curtains | indoors | onsen | partially_submerged | stomach | wet | bangs | day | outdoors | wading | armpits | cowboy_shot | arm_up | blue_sky | groin | thighs | tree | crop_top | midriff | cleavage | holding | official_alternate_costume | blue_skirt | circlet | fire | see-through | standing | thighlet | bare_shoulders | hand_up | light_blue_hair | pantyhose | torch | 1boy | hetero | penis | solo_focus | male_pubic_hair | mosaic_censoring | paizuri | pov | fellatio | breasts_squeezed_together | obi | hagoita | sandals | wide_sleeves | floral_print | hair_ornament | hanetsuki | long_sleeves | blue_kimono | flower | grey_background | hair_tubes | tabi | cum_in_pussy | multiple_boys | multiple_penises | rape | cum_on_body | gangbang | vaginal | asymmetrical_legwear | breast_grab | gloves | grabbing | tears | anal | facial | spread_legs | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:--------------------|:-------|:-------|:--------------|:---------------|:--------|:-------------|:--------------------|:-----------|:----------|:---------|:--------|:--------------------|:--------|:-------------------|:----------|:-------------|:-----------|:---------|:------------|:--------|:-----------------|:----------------------|:----------|:--------|:----------|:----------|:------------------|:--------|:-----------------|:-------------|:----------------|:---------------|:-------------------|:-----------|:----------|:--------|:----------------------|:----------|:------|:--------|:------|:-----------|:---------|:----------|:--------------|:---------|:-----------|:--------|:---------|:-------|:-----------|:----------|:-----------|:----------|:-----------------------------|:-------------|:----------|:-------|:--------------|:-----------|:-----------|:-----------------|:----------|:------------------|:------------|:--------|:-------|:---------|:--------|:-------------|:------------------|:-------------------|:----------|:------|:-----------|:----------------------------|:------|:----------|:----------|:---------------|:---------------|:----------------|:------------|:---------------|:--------------|:---------|:------------------|:-------------|:-------|:---------------|:----------------|:-------------------|:-------|:--------------|:-----------|:----------|:-----------------------|:--------------|:---------|:-----------|:--------|:-------|:---------|:--------------| | 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 |  |  |  |  |  | X | X | X | X | | | X | X | X | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | | X | X | | | | X | | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | X | X | X | X | | | X | | X | | | | X | | | | | | X | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 |  |  |  |  |  | X | | | X | X | | | | | X | | | | | X | X | X | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 |  |  |  |  |  | X | | | X | X | | | X | | X | | | | | | X | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 |  |  |  |  |  | X | | | X | X | | | 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 | 8 |  |  |  |  |  | X | | | X | X | | | | | X | X | | | | | X | | | | | | X | | | | | 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 |  |  |  |  |  | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 9 | 5 |  |  |  |  |  | X | | | X | X | | | | X | X | | | | | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 10 | 9 |  |  |  |  |  | X | | | | X | | | | 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/aqua_fireemblem

2024-01-17T14:03:37+00:00

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

2024-01-17T15:47:47+00:00

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

Dataset of aqua (Fire Emblem) ============================= This is the dataset of aqua (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'blue\_hair, long\_hair, yellow\_eyes, hair\_between\_eyes, very\_long\_hair, 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

3d6d648aa9630b243f02a8fed42b1b53c2770eab

# Dataset of citrinne/シトリニカ (Fire Emblem) This is the dataset of citrinne/シトリニカ (Fire Emblem), containing 144 images and their tags. The core tags of this character are `short_hair, blonde_hair, red_eyes, hair_ornament, breasts, bangs, medium_breasts, earrings`, 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 | 144 | 234.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/citrinne_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 144 | 127.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/citrinne_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 364 | 280.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/citrinne_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 144 | 207.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/citrinne_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 364 | 415.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/citrinne_fireemblem/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/citrinne_fireemblem', 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 |  |  |  |  |  | 1girl, cleavage, smile, solo, detached_sleeves, holding_book, looking_at_viewer, blush, brown_dress, necklace, open_mouth | | 1 | 8 |  |  |  |  |  | 1girl, dress, smile, solo, cleavage, upper_body, bare_shoulders, detached_sleeves, looking_at_viewer, necklace, official_alternate_costume | | 2 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, long_sleeves, midriff, closed_mouth, crop_top, smile, stomach, arms_up, collarbone, shirt, simple_background, blush, choker, cleavage, on_back, pants, small_breasts, thighhighs, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | smile | solo | detached_sleeves | holding_book | looking_at_viewer | blush | brown_dress | necklace | open_mouth | dress | upper_body | bare_shoulders | official_alternate_costume | navel | long_sleeves | midriff | closed_mouth | crop_top | stomach | arms_up | collarbone | shirt | simple_background | choker | on_back | pants | small_breasts | thighhighs | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------|:-------|:-------------------|:---------------|:--------------------|:--------|:--------------|:-----------|:-------------|:--------|:-------------|:-----------------|:-----------------------------|:--------|:---------------|:----------|:---------------|:-----------|:----------|:----------|:-------------|:--------|:--------------------|:---------|:----------|:--------|:----------------|:-------------|:-------------------| | 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 1 | 8 |  |  |  |  |  | X | X | X | X | X | | X | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | X | X | X | | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/citrinne_fireemblem

2024-01-17T14:03:42+00:00

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

2024-01-17T14:31:14+00:00

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

Dataset of citrinne/シトリニカ (Fire Emblem) ======================================= This is the dataset of citrinne/シトリニカ (Fire Emblem), containing 144 images and their tags. The core tags of this character are 'short\_hair, blonde\_hair, red\_eyes, hair\_ornament, breasts, bangs, medium\_breasts, earrings', 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

5ca6e369f0e723c9c745452ba9a677b7f1dae159

# Dataset of hortensia/オルテンシア (Fire Emblem) This is the dataset of hortensia/オルテンシア (Fire Emblem), containing 156 images and their tags. The core tags of this character are `pink_hair, bangs, pink_eyes, breasts, hair_rings, multicolored_hair, facial_mark, 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 | 156 | 247.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hortensia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 156 | 136.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hortensia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 362 | 293.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hortensia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 156 | 214.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hortensia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 362 | 433.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hortensia_fireemblem/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/hortensia_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 |  |  |  |  |  | 1girl, heart, looking_at_viewer, open_mouth, smile, solo, one_eye_closed, juliet_sleeves, ;d, cleavage, red_rose, upper_body, white_background, simple_background, blush, streaked_hair, medium_breasts, v_over_eye | | 1 | 6 |  |  |  |  |  | 1girl, juliet_sleeves, looking_at_viewer, red_rose, smile, solo, simple_background, cleavage, heart_tattoo, medium_breasts, open_mouth, upper_body, green_background | | 2 | 6 |  |  |  |  |  | 1girl, hair_bow, looking_at_viewer, smile, solo, choker, earrings, upper_body, heart_hands, long_sleeves, open_mouth, black_gloves, cleavage, polka_dot_bow, purple_eyes, red_jacket, simple_background, streaked_hair | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | heart | looking_at_viewer | open_mouth | smile | solo | one_eye_closed | juliet_sleeves | ;d | cleavage | red_rose | upper_body | white_background | simple_background | blush | streaked_hair | medium_breasts | v_over_eye | heart_tattoo | green_background | hair_bow | choker | earrings | heart_hands | long_sleeves | black_gloves | polka_dot_bow | purple_eyes | red_jacket | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------------|:--------|:-------|:-----------------|:-----------------|:-----|:-----------|:-----------|:-------------|:-------------------|:--------------------|:--------|:----------------|:-----------------|:-------------|:---------------|:-------------------|:-----------|:---------|:-----------|:--------------|:---------------|:---------------|:----------------|:--------------|:-------------| | 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 6 |  |  |  |  |  | X | | X | X | X | X | | X | | X | X | X | | X | | | X | | X | X | | | | | | | | | | | 2 | 6 |  |  |  |  |  | X | | X | X | X | X | | | | X | | X | | X | | X | | | | | X | X | X | X | X | X | X | X | X |

CyberHarem/hortensia_fireemblem

2024-01-17T14:27:05+00:00

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

2024-01-17T14:55:59+00:00

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

Dataset of hortensia/オルテンシア (Fire Emblem) ========================================= This is the dataset of hortensia/オルテンシア (Fire Emblem), containing 156 images and their tags. The core tags of this character are 'pink\_hair, bangs, pink\_eyes, breasts, hair\_rings, multicolored\_hair, facial\_mark, 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

a3f246a51045405acfb831f7496aa6d482feaa8f

# Dataset of orochi/オロチ (Fire Emblem) This is the dataset of orochi/オロチ (Fire Emblem), containing 96 images and their tags. The core tags of this character are `long_hair, breasts, hair_ornament, purple_eyes, purple_hair, large_breasts, earrings`, 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 | 96 | 97.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orochi_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 96 | 63.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orochi_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 227 | 132.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orochi_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 96 | 89.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orochi_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 227 | 174.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/orochi_fireemblem/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/orochi_fireemblem', 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 | 19 |  |  |  |  |  | 1girl, solo, jewelry, midriff, smile, looking_at_viewer, navel, cleavage, simple_background, bare_shoulders, white_background | | 1 | 13 |  |  |  |  |  | 1boy, hetero, 1girl, penis, nipples, solo_focus, blush, jewelry, cum_on_breasts, facial, open_mouth, smile, nude, uncensored | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | jewelry | midriff | smile | looking_at_viewer | navel | cleavage | simple_background | bare_shoulders | white_background | 1boy | hetero | penis | nipples | solo_focus | blush | cum_on_breasts | facial | open_mouth | nude | uncensored | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:----------|:--------|:--------------------|:--------|:-----------|:--------------------|:-----------------|:-------------------|:-------|:---------|:--------|:----------|:-------------|:--------|:-----------------|:---------|:-------------|:-------|:-------------| | 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 1 | 13 |  |  |  |  |  | X | | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/orochi_fireemblem

2024-01-17T14:27:14+00:00

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

2024-01-17T14:48:33+00:00

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

Dataset of orochi/オロチ (Fire Emblem) =================================== This is the dataset of orochi/オロチ (Fire Emblem), containing 96 images and their tags. The core tags of this character are 'long\_hair, breasts, hair\_ornament, purple\_eyes, purple\_hair, large\_breasts, earrings', 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

bf2ac1c0ce5d2f4e75029a73c5c7d33dbd79f9ce

# Dataset of rinka/リンカ (Fire Emblem) This is the dataset of rinka/リンカ (Fire Emblem), containing 142 images and their tags. The core tags of this character are `dark_skin, dark-skinned_female, white_hair, red_eyes, breasts, facial_mark, muscular_female, mask_on_head, multicolored_hair, red_hair, short_hair, large_breasts, two-tone_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 | 142 | 200.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rinka_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 142 | 110.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rinka_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 371 | 237.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rinka_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 142 | 175.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rinka_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 371 | 335.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rinka_fireemblem/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/rinka_fireemblem', 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 | 40 |  |  |  |  |  | 1girl, abs, mask, muscular, solo, navel, bandages, beads, necklace, midriff, looking_at_viewer, simple_background, chest_sarashi, club_(weapon), bare_shoulders, biceps | | 1 | 6 |  |  |  |  |  | 1girl, abs, long_hair, mask, muscular, navel, solo, bandeau, bangs, bare_shoulders, chest_sarashi, fire, looking_at_viewer, midriff, official_alternate_costume, standing, stomach, tube_top, bandages, full_body, simple_background, white_background, clenched_teeth, grin, holding_fan, lantern, pelvic_curtain, thigh_strap, thighs, uchiwa, whisker_markings | | 2 | 5 |  |  |  |  |  | 1girl, abs, mask, muscular, nipples, pussy, solo, completely_nude, female_pubic_hair, obliques, looking_at_viewer, navel, standing, uncensored, ass_visible_through_thighs, biceps, blush, cowboy_shot, facepaint, outdoors | | 3 | 8 |  |  |  |  |  | 1girl, hetero, penis, sex, vaginal, 1boy, abs, blush, cum_in_pussy, mask, muscular, nipples, necklace, open_mouth, solo_focus, faceless_male, girl_on_top, light_areolae, navel, nude, spread_legs, ahegao, bandages, bar_censor, beads, bottomless, straddling, sweat, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | abs | mask | muscular | solo | navel | bandages | beads | necklace | midriff | looking_at_viewer | simple_background | chest_sarashi | club_(weapon) | bare_shoulders | biceps | long_hair | bandeau | bangs | fire | official_alternate_costume | standing | stomach | tube_top | full_body | white_background | clenched_teeth | grin | holding_fan | lantern | pelvic_curtain | thigh_strap | thighs | uchiwa | whisker_markings | nipples | pussy | completely_nude | female_pubic_hair | obliques | uncensored | ass_visible_through_thighs | blush | cowboy_shot | facepaint | outdoors | hetero | penis | sex | vaginal | 1boy | cum_in_pussy | open_mouth | solo_focus | faceless_male | girl_on_top | light_areolae | nude | spread_legs | ahegao | bar_censor | bottomless | straddling | sweat | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------|:-------|:-----------|:-------|:--------|:-----------|:--------|:-----------|:----------|:--------------------|:--------------------|:----------------|:----------------|:-----------------|:---------|:------------|:----------|:--------|:-------|:-----------------------------|:-----------|:----------|:-----------|:------------|:-------------------|:-----------------|:-------|:--------------|:----------|:-----------------|:--------------|:---------|:---------|:-------------------|:----------|:--------|:------------------|:--------------------|:-----------|:-------------|:-----------------------------|:--------|:--------------|:------------|:-----------|:---------|:--------|:------|:----------|:-------|:---------------|:-------------|:-------------|:----------------|:--------------|:----------------|:-------|:--------------|:---------|:-------------|:-------------|:-------------|:--------|:-------------| | 0 | 40 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | | | 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 |  |  |  |  |  | X | X | X | X | X | X | | | | | X | | | | | X | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 3 | 8 |  |  |  |  |  | X | X | X | X | | X | 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/rinka_fireemblem

2024-01-17T14:27:18+00:00

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

2024-01-17T15:01:39+00:00

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

Dataset of rinka/リンカ (Fire Emblem) ================================== This is the dataset of rinka/リンカ (Fire Emblem), containing 142 images and their tags. The core tags of this character are 'dark\_skin, dark-skinned\_female, white\_hair, red\_eyes, breasts, facial\_mark, muscular\_female, mask\_on\_head, multicolored\_hair, red\_hair, short\_hair, large\_breasts, two-tone\_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

1f12faa2123474ed9714302c780952a1bb050876

# Dataset of kinu (Fire Emblem) This is the dataset of kinu (Fire Emblem), containing 262 images and their tags. The core tags of this character are `animal_ears, blonde_hair, fox_ears, yellow_eyes, fox_tail, multicolored_hair, tail, short_hair, streaked_hair, hair_ornament, breasts, brown_hair, two-tone_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 | 262 | 282.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 262 | 167.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 603 | 338.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 262 | 251.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 603 | 466.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinu_fireemblem/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/kinu_fireemblem', 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 | 14 |  |  |  |  |  | 1girl, fur_trim, solo, japanese_clothes, fingerless_gloves, white_gloves, open_mouth, simple_background, smile, white_background | | 1 | 6 |  |  |  |  |  | 1girl, bangs, full_body, fur_trim, japanese_clothes, long_sleeves, sandals, shiny_hair, simple_background, solo, tabi, white_background, wide_sleeves, fingerless_gloves, smile, thigh_strap, looking_at_viewer | | 2 | 26 |  |  |  |  |  | 1girl, nipples, solo, blush, navel, censored, completely_nude, animal_ear_fluff, looking_at_viewer, open_mouth, smile, medium_breasts, spread_legs, fox_girl, pussy_juice, spread_pussy, anus | | 3 | 8 |  |  |  |  |  | 1boy, 1girl, blush, cum_in_pussy, hetero, nipples, solo_focus, vaginal, open_mouth, animal_ear_fluff, bar_censor, medium_breasts, penis, spread_legs, female_pubic_hair, japanese_clothes, navel, overflow, bottomless, breasts_out, clothed_sex, cowgirl_position, fang, fingerless_gloves, heart-shaped_pupils, indoors, looking_at_viewer, smile, white_gloves | | 4 | 5 |  |  |  |  |  | 1boy, 1girl, hetero, open_mouth, blush, nipples, sex_from_behind, solo_focus, doggystyle, large_breasts, nude, ahegao, closed_eyes, cum_on_body, saliva, sheet_grab, tears, tongue_out | | 5 | 6 |  |  |  |  |  | 1girl, solo, open_mouth, smile, looking_at_viewer, navel, hair_flower, large_breasts, red_bikini, side-tie_bikini_bottom | | 6 | 6 |  |  |  |  |  | 1boy, 1girl, blush, fellatio, hetero, penis, solo_focus, heart, animal_ear_fluff, cum_in_mouth, large_breasts, uncensored | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fur_trim | solo | japanese_clothes | fingerless_gloves | white_gloves | open_mouth | simple_background | smile | white_background | bangs | full_body | long_sleeves | sandals | shiny_hair | tabi | wide_sleeves | thigh_strap | looking_at_viewer | nipples | blush | navel | censored | completely_nude | animal_ear_fluff | medium_breasts | spread_legs | fox_girl | pussy_juice | spread_pussy | anus | 1boy | cum_in_pussy | hetero | solo_focus | vaginal | bar_censor | penis | female_pubic_hair | overflow | bottomless | breasts_out | clothed_sex | cowgirl_position | fang | heart-shaped_pupils | indoors | sex_from_behind | doggystyle | large_breasts | nude | ahegao | closed_eyes | cum_on_body | saliva | sheet_grab | tears | tongue_out | hair_flower | red_bikini | side-tie_bikini_bottom | fellatio | heart | cum_in_mouth | uncensored | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:-------|:-------------------|:--------------------|:---------------|:-------------|:--------------------|:--------|:-------------------|:--------|:------------|:---------------|:----------|:-------------|:-------|:---------------|:--------------|:--------------------|:----------|:--------|:--------|:-----------|:------------------|:-------------------|:-----------------|:--------------|:-----------|:--------------|:---------------|:-------|:-------|:---------------|:---------|:-------------|:----------|:-------------|:--------|:--------------------|:-----------|:-------------|:--------------|:--------------|:-------------------|:-------|:----------------------|:----------|:------------------|:-------------|:----------------|:-------|:---------|:--------------|:--------------|:---------|:-------------|:--------|:-------------|:--------------|:-------------|:-------------------------|:-----------|:--------|:---------------|:-------------| | 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 |  |  |  |  |  | X | X | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 26 |  |  |  |  |  | X | | X | | | | X | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 8 |  |  |  |  |  | X | | | X | X | X | X | | 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 |  |  |  |  |  | X | | | | | | X | | | | | | | | | | | | | X | X | | | | | | | | | | | X | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | 5 | 6 |  |  |  |  |  | X | | X | | | | X | | X | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | | | | | | 6 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | X | | X | X | | | X | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X |

CyberHarem/kinu_fireemblem

2024-01-17T14:27:20+00:00

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

2024-01-17T15:21:54+00:00

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

Dataset of kinu (Fire Emblem) ============================= This is the dataset of kinu (Fire Emblem), containing 262 images and their tags. The core tags of this character are 'animal\_ears, blonde\_hair, fox\_ears, yellow\_eyes, fox\_tail, multicolored\_hair, tail, short\_hair, streaked\_hair, hair\_ornament, breasts, brown\_hair, two-tone\_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

f1c5b0bd67845178c90fa1b3c5299b207cc7f1aa

# Dataset Card for "SemEval_traindata_emotions" Как был получен ```python from datasets import load_dataset import datasets from torchvision.io import read_video import json import torch import os from torch.utils.data import Dataset, DataLoader import tqdm dataset_path = "./SemEval-2024_Task3/training_data/Subtask_2_train.json" dataset = json.loads(open(dataset_path).read()) print(len(dataset)) all_conversations = [] for item in dataset: all_conversations.extend(item["conversation"]) print(len(all_conversations)) all_data = datasets.Dataset.from_list(all_conversations) all_data = all_data.train_test_split( test_size=0.08, seed=42, ) all_data.push_to_hub( "dim/SemEval_training_data_emotions", token=open("./hf_token").read(), ) ```

dim/SemEval_training_data_emotions

2024-01-17T14:36:07+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "utterance_ID", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "speaker", "dtype": "string"}, {"name": "emotion", "dtype": "string"}, {"name": "video_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1198989.1453851238, "num_examples": 12529}, {"name": "test", "num_bytes": 104309.85461487627, "num_examples": 1090}], "download_size": 614184, "dataset_size": 1303299.0}}

2024-01-17T14:41:17+00:00

TAGS #region-us

# Dataset Card for "SemEval_traindata_emotions" Как был получен

cfdd09d098472e44bf09334ef47cd7e5e406ec3e

# Dataset of aversa/インバース (Fire Emblem) This is the dataset of aversa/インバース (Fire Emblem), containing 62 images and their tags. The core tags of this character are `long_hair, breasts, white_hair, facial_mark, dark-skinned_female, dark_skin, large_breasts, red_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 | 62 | 75.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aversa_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 62 | 43.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aversa_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 131 | 83.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aversa_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 62 | 66.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aversa_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 131 | 120.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/aversa_fireemblem/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/aversa_fireemblem', 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 | 37 |  |  |  |  |  | 1girl, solo, cleavage, looking_at_viewer, thighhighs, smile, nail_polish, simple_background, bridal_gauntlets, navel, black_nails, book, dress, jewelry, long_fingernails, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cleavage | looking_at_viewer | thighhighs | smile | nail_polish | simple_background | bridal_gauntlets | navel | black_nails | book | dress | jewelry | long_fingernails | white_background | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:--------------------|:-------------|:--------|:--------------|:--------------------|:-------------------|:--------|:--------------|:-------|:--------|:----------|:-------------------|:-------------------| | 0 | 37 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/aversa_fireemblem

2024-01-17T14:36:57+00:00

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

2024-01-17T14:48:04+00:00

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

Dataset of aversa/インバース (Fire Emblem) ===================================== This is the dataset of aversa/インバース (Fire Emblem), containing 62 images and their tags. The core tags of this character are 'long\_hair, breasts, white\_hair, facial\_mark, dark-skinned\_female, dark\_skin, large\_breasts, red\_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

52c34f921753108dd3a21d8b1155972ed6e81bc5

# Dataset of nono (Fire Emblem) This is the dataset of nono (Fire Emblem), containing 405 images and their tags. The core tags of this character are `long_hair, pointy_ears, purple_eyes, green_hair, ahoge, ponytail, breasts, blonde_hair, small_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 | 405 | 446.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nono_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 405 | 265.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nono_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 891 | 531.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nono_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 405 | 398.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nono_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 891 | 727.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nono_fireemblem/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/nono_fireemblem', 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 | 61 |  |  |  |  |  | 1girl, cape, solo, gloves, smile, circlet, navel, open_mouth, midriff, looking_at_viewer, shorts, belt, garter_straps, boots, simple_background, flat_chest, pink_bow, pink_thighhighs | | 1 | 25 |  |  |  |  |  | 1girl, solo, witch_hat, circlet, open_mouth, shorts, smile, halloween_costume, navel, midriff, sleeves_past_wrists, bow, looking_at_viewer, simple_background, belt, wide_sleeves, alternate_costume, broom_riding, boots, full_body | | 2 | 9 |  |  |  |  |  | 1girl, blush, cum_in_pussy, hetero, navel, penis, solo_focus, 1boy, circlet, gloves, nipples, sex, vaginal, open_mouth, spread_legs, thighhighs, mosaic_censoring, smile, boots, cape, tears | | 3 | 7 |  |  |  |  |  | 1boy, 1girl, blush, circlet, fellatio, hetero, penis, solo_focus, cum_in_mouth, heart, one_eye_closed, pov, loli, mosaic_censoring, simple_background, witch_hat | | 4 | 5 |  |  |  |  |  | 1boy, 1girl, circlet, hetero, paizuri, penis, solo_focus, blush, ejaculation, nipples, gloves, open_mouth, uncensored, cum_on_breasts, heart, huge_breasts, jewelry, nude, simple_background, smile, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cape | solo | gloves | smile | circlet | navel | open_mouth | midriff | looking_at_viewer | shorts | belt | garter_straps | boots | simple_background | flat_chest | pink_bow | pink_thighhighs | witch_hat | halloween_costume | sleeves_past_wrists | bow | wide_sleeves | alternate_costume | broom_riding | full_body | blush | cum_in_pussy | hetero | penis | solo_focus | 1boy | nipples | sex | vaginal | spread_legs | thighhighs | mosaic_censoring | tears | fellatio | cum_in_mouth | heart | one_eye_closed | pov | loli | paizuri | ejaculation | uncensored | cum_on_breasts | huge_breasts | jewelry | nude | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------|:---------|:--------|:----------|:--------|:-------------|:----------|:--------------------|:---------|:-------|:----------------|:--------|:--------------------|:-------------|:-----------|:------------------|:------------|:--------------------|:----------------------|:------|:---------------|:--------------------|:---------------|:------------|:--------|:---------------|:---------|:--------|:-------------|:-------|:----------|:------|:----------|:--------------|:-------------|:-------------------|:--------|:-----------|:---------------|:--------|:-----------------|:------|:-------|:----------|:--------------|:-------------|:-----------------|:---------------|:----------|:-------|:-------------| | 0 | 61 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 25 |  |  |  |  |  | X | | X | | X | X | X | X | X | X | X | X | | X | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 |  |  |  |  |  | X | X | | X | X | X | X | X | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | 3 | 7 |  |  |  |  |  | X | | | | | X | | | | | | | | | X | | | | X | | | | | | | | X | | X | X | X | X | | | | | | X | | X | X | X | X | X | X | | | | | | | | | | 4 | 5 |  |  |  |  |  | X | | | X | X | X | | X | | | | | | | X | | | | | | | | | | | | X | | X | X | X | X | X | | | | | | | | | X | | | | X | X | X | X | X | X | X | X |

CyberHarem/nono_fireemblem

2024-01-17T14:37:05+00:00

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

2024-01-17T15:55:43+00:00

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

Dataset of nono (Fire Emblem) ============================= This is the dataset of nono (Fire Emblem), containing 405 images and their tags. The core tags of this character are 'long\_hair, pointy\_ears, purple\_eyes, green\_hair, ahoge, ponytail, breasts, blonde\_hair, small\_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

3dc42886bdb38338e6cc03b5093c28b5b5694d0b

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

tmnam20/Vietnamese-Book-Corpus

2024-01-17T14:38:52+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3716689262, "num_examples": 16407}], "download_size": 1923451913, "dataset_size": 3716689262}}

2024-01-17T14:44:27+00:00

TAGS #region-us

# Dataset Card for "Vietnamese-Book-Corpus" More Information needed

4c65870d6d8e0576a555ad8ed26bc5ae02c86896

# Dataset of selena (Fire Emblem) This is the dataset of selena (Fire Emblem), containing 358 images and their tags. The core tags of this character are `long_hair, red_hair, twintails, red_eyes, breasts, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 358 | 374.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selena_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 358 | 233.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selena_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 779 | 449.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selena_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 358 | 340.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selena_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 779 | 598.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/selena_fireemblem/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/selena_fireemblem', 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 |  |  |  |  |  | 1girl, armor, boots, fingerless_gloves, full_body, sword, simple_background, solo, bent_over, detached_sleeves, leaning_forward, looking_at_viewer, pants, sheathed, bare_shoulders, belt, white_background, bangs, closed_mouth | | 1 | 9 |  |  |  |  |  | 1girl, nipples, blush, looking_at_viewer, navel, completely_nude, solo, large_breasts, collarbone, medium_breasts, pussy, bangs, hair_between_eyes, hair_ribbon | | 2 | 8 |  |  |  |  |  | 1girl, barefoot, nipples, solo, completely_nude, full_body, medium_breasts, navel, standing, looking_at_viewer, outdoors, profile, running | | 3 | 5 |  |  |  |  |  | 1girl, nipples, nude, solo, navel, small_breasts, medium_breasts, simple_background, uncensored, white_background, blush, brown_eyes, crossed_arms, pussy_juice, ribbon | | 4 | 22 |  |  |  |  |  | 1girl, blush, hetero, 1boy, penis, solo_focus, sex, nude, vaginal, nipples, open_mouth, uncensored, navel, gloves, medium_breasts, cowgirl_position, cum_in_pussy, pov | | 5 | 5 |  |  |  |  |  | 2girls, open_mouth, yuri, blush, tongue_out, nipples, 1boy, blue_hair, completely_nude, cunnilingus, heart-shaped_pupils, large_breasts, licking, saliva, sweat | | 6 | 30 |  |  |  |  |  | fake_animal_ears, rabbit_ears, 1girl, pantyhose, playboy_bunny, solo, leotard, cleavage, looking_at_viewer, choker, blush, medium_breasts, alternate_costume, bare_shoulders, simple_background, white_gloves, easter_egg, hair_ornament | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | armor | boots | fingerless_gloves | full_body | sword | simple_background | solo | bent_over | detached_sleeves | leaning_forward | looking_at_viewer | pants | sheathed | bare_shoulders | belt | white_background | bangs | closed_mouth | nipples | blush | navel | completely_nude | large_breasts | collarbone | medium_breasts | pussy | hair_between_eyes | hair_ribbon | barefoot | standing | outdoors | profile | running | nude | small_breasts | uncensored | brown_eyes | crossed_arms | pussy_juice | ribbon | hetero | 1boy | penis | solo_focus | sex | vaginal | open_mouth | gloves | cowgirl_position | cum_in_pussy | pov | 2girls | yuri | tongue_out | blue_hair | cunnilingus | heart-shaped_pupils | licking | saliva | sweat | fake_animal_ears | rabbit_ears | pantyhose | playboy_bunny | leotard | cleavage | choker | alternate_costume | white_gloves | easter_egg | hair_ornament | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------|:--------------------|:------------|:--------|:--------------------|:-------|:------------|:-------------------|:------------------|:--------------------|:--------|:-----------|:-----------------|:-------|:-------------------|:--------|:---------------|:----------|:--------|:--------|:------------------|:----------------|:-------------|:-----------------|:--------|:--------------------|:--------------|:-----------|:-----------|:-----------|:----------|:----------|:-------|:----------------|:-------------|:-------------|:---------------|:--------------|:---------|:---------|:-------|:--------|:-------------|:------|:----------|:-------------|:---------|:-------------------|:---------------|:------|:---------|:-------|:-------------|:------------|:--------------|:----------------------|:----------|:---------|:--------|:-------------------|:--------------|:------------|:----------------|:----------|:-----------|:---------|:--------------------|:---------------|:-------------|:----------------| | 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 9 |  |  |  |  |  | X | | | | | | | X | | | | X | | | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 |  |  |  |  |  | X | | | | X | | | X | | | | X | | | | | | | | X | | X | X | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | | | | | | X | X | | | | | | | | | X | | | X | X | X | | | | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 22 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | X | X | X | | | | X | | | | | | | | | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 5 | 5 |  |  |  |  |  | | | | | | | | | | | | | | | | | | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 6 | 30 |  |  |  |  |  | X | | | | | | X | X | | | | X | | | X | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/selena_fireemblem

2024-01-17T14:39:14+00:00

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

2024-01-17T16:08:48+00:00

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

Dataset of selena (Fire Emblem) =============================== This is the dataset of selena (Fire Emblem), containing 358 images and their tags. The core tags of this character are 'long\_hair, red\_hair, twintails, red\_eyes, breasts, very\_long\_hair', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

c3f2fc2b8d17bcac06155506c17744e609653a22

# Autocast This is the Autocast dataset from the paper "[Forecasting Future World Events with Neural Networks](http://arxiv.org/abs/2206.15474)" by [Andy Zou](https://andyzoujm.github.io/), [Tristan Xiao](https://www.linkedin.com/in/tristan-xiao/), [Ryan Jia](https://www.linkedin.com/in/ryanjia/), [Joe Kwon](joekwon.io), [Mantas Mazeika](https://www.linkedin.com/in/mmazeika/), [Richard Li](https://www.linkedin.com/in/lirichard23/), [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/), [Owain Evans](https://owainevans.github.io/), and [Dan Hendrycks](https://danhendrycks.com/). The original dataset files are: - `autocast_questions.json` - `autocast_competition_test_set.json` - `negated_tf_questions.json` We have also processed the dataset to filter out source links with: - URLs returning non-200 HTTP status codes - URLs from sites that are difficult to scrape like twitter, bloomberg - Links with less than 1000 words are removed. Only samples with a minimum of 5 working URLs are retained. The maximum number of working source links is 20. The refined dataset files are: - `autocast_questions_filtered.json` - a JSON subset of the initial autocast dataset. - `autocast_questions_filtered.pkl` - a pickle file mapping URLs to the scraped data. - `retrieved_docs.pkl` - this contains all texts that were retrieved. <img align="center" src="assets/splash.png" width="750"> # Forecasting Future World Events with Neural Networks ## Introduction Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits. ## Autocast Dataset The original [Autocast dataset can be downloaded here](https://people.eecs.berkeley.edu/~hendrycks/autocast.tar.gz). For more details on how to use the Autocast dataset and news articles, please refer to our short demonstration in `usage.ipynb`. Each question has the following fields: ```json { "id": "unique identifier (str)", "question": "question body (str)", "background": "question context/details (str)", "qtype": "question type (str)", "status": "question status (str)", "choices": "choices or possible ranges (List or Dict)", "answer": "question resolution (str or float)", "crowd": "human crowd forecasts over time (List)", "publish_time": "publish timestamp (str)", "close_time": "close timestamp (str)", "prediction_count": "number of crowd predictions (int)", "forecaster_count": "number of crowd forecasters (int)", "tags": "question category (List)", "source_links": "source links from comments (List)" } ``` The original authors obtained permission from [Metaculus](https://www.metaculus.com/) to host the dataset on GitHub for research purposes only. ## IntervalQA Dataset Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), the original authors also curate IntervalQA, a dataset of numerical questions and metrics for calibration. [Download the IntervalQA dataset here](https://people.eecs.berkeley.edu/~hendrycks/intervalqa.tar.gz). ## Citation If you find this useful in your research, please consider citing the original authors: @article{zouforecasting2022, title={Forecasting Future World Events with Neural Networks}, author={Andy Zou and Tristan Xiao and Ryan Jia and Joe Kwon and Mantas Mazeika and Richard Li and Dawn Song and Jacob Steinhardt and Owain Evans and Dan Hendrycks}, journal={NeurIPS}, year={2022} }

valory/autocast

2024-01-17T14:39:53+00:00

{}

2024-02-05T20:39:07+00:00

TAGS #arxiv-2206.15474 #region-us

# Autocast This is the Autocast dataset from the paper "Forecasting Future World Events with Neural Networks" by Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks. The original dataset files are: - 'autocast_questions.json' - 'autocast_competition_test_set.json' - 'negated_tf_questions.json' We have also processed the dataset to filter out source links with: - URLs returning non-200 HTTP status codes - URLs from sites that are difficult to scrape like twitter, bloomberg - Links with less than 1000 words are removed. Only samples with a minimum of 5 working URLs are retained. The maximum number of working source links is 20. The refined dataset files are: - 'autocast_questions_filtered.json' - a JSON subset of the initial autocast dataset. - 'autocast_questions_filtered.pkl' - a pickle file mapping URLs to the scraped data. - 'retrieved_docs.pkl' - this contains all texts that were retrieved. <img align="center" src="assets/URL" width="750"> # Forecasting Future World Events with Neural Networks ## Introduction Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Given advances in language modeling, can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying news corpus. Questions are taken from forecasting tournaments, ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. In sum, Autocast poses a novel challenge for large language models and improved performance could bring large practical benefits. ## Autocast Dataset The original Autocast dataset can be downloaded here. For more details on how to use the Autocast dataset and news articles, please refer to our short demonstration in 'URL'. Each question has the following fields: The original authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only. ## IntervalQA Dataset Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), the original authors also curate IntervalQA, a dataset of numerical questions and metrics for calibration. Download the IntervalQA dataset here. If you find this useful in your research, please consider citing the original authors: @article{zouforecasting2022, title={Forecasting Future World Events with Neural Networks}, author={Andy Zou and Tristan Xiao and Ryan Jia and Joe Kwon and Mantas Mazeika and Richard Li and Dawn Song and Jacob Steinhardt and Owain Evans and Dan Hendrycks}, journal={NeurIPS}, year={2022} }

78a77a8650d8ed1412c6bb7255bfb7e36dba7fab

# Dataset of cellica (Fire Emblem) This is the dataset of cellica (Fire Emblem), containing 500 images and their tags. The core tags of this character are `long_hair, red_hair, red_eyes, breasts, earrings, bangs, hairband`, 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 | 571.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cellica_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 359.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cellica_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1080 | 696.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cellica_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 518.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cellica_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1080 | 918.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cellica_fireemblem/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/cellica_fireemblem', 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 | 6 |  |  |  |  |  | 1girl, cape, fingerless_gloves, jewelry, looking_at_viewer, simple_background, smile, solo, tiara, armor, bare_shoulders, detached_collar, black_gloves, white_dress | | 1 | 7 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, simple_background, solo, tiara, upper_body, armor, detached_collar, jewelry, smile, white_dress, closed_mouth, cape, cleavage, medium_breasts, white_background | | 2 | 14 |  |  |  |  |  | 1girl, cape, dress, jewelry, solo, armor, fingerless_gloves, holding_sword, simple_background, smile, tiara, black_thighhighs, looking_at_viewer, zettai_ryouiki, detached_collar, white_background, bare_shoulders, boots, full_body, black_gloves, cowboy_shot | | 3 | 5 |  |  |  |  |  | 1girl, navel, nipples, smile, solo, completely_nude, large_breasts, looking_at_viewer, medium_breasts, pussy, collarbone, outdoors, standing, thighs, water, blush, day, jewelry, nature, wading, yellow_eyes | | 4 | 8 |  |  |  |  |  | 1girl, jewelry, solo_focus, thighhighs, hetero, open_mouth, tiara, 1boy, blush, breasts_out, clothed_sex, cowgirl_position, cum_in_pussy, girl_on_top, nipples, penis, vaginal, armor, black_gloves, fingerless_gloves, large_breasts, spread_legs, cape, detached_collar, medium_breasts, sweat, bar_censor, dress_lift, dress_pull, looking_at_viewer, white_dress | | 5 | 7 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, bar_censor, fellatio, nipples, jewelry, nude, blush, large_breasts, medium_breasts, testicles | | 6 | 12 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, blush, solo_focus, sex, large_breasts, open_mouth, penis, vaginal, cum_in_pussy, navel, bar_censor, completely_nude, tiara, jewelry, sweat | | 7 | 5 |  |  |  |  |  | 1girl, barefoot, large_breasts, nipples, solo, arms_behind_back, blush, bondage, feet, looking_at_viewer, navel, rope, toes, completely_nude, pussy_juice, restrained, shibari, smile, spread_legs, sweat, thighs, clitoris, closed_mouth, jewelry, mosaic_censoring, squatting, uncensored | | 8 | 7 |  |  |  |  |  | 1girl, blush, hetero, nipples, thighhighs, mmf_threesome, multiple_penises, anal, dark-skinned_male, nude, open_mouth, vaginal, 2boys, ass, blunt_bangs, double_penetration, interracial, jewelry, large_breasts, tongue_out, ahegao, cum, faceless_male, gloves, medium_breasts, mosaic_censoring, pussy, solo_focus, sweat, tiara | | 9 | 5 |  |  |  |  |  | 1girl, navel, smile, beach, cleavage, cloud, hair_flower, looking_at_viewer, solo, white_bikini, alternate_costume, blue_sky, day, jewelry, medium_breasts, ocean, open_mouth, outdoors, collarbone, sitting, thighs, water | | 10 | 6 |  |  |  |  |  | 1girl, bondage, gagged, rope, arms_behind_back, blush, solo, improvised_gag, jewelry, black_thighhighs, cleavage, large_breasts, medium_breasts, navel, orange_hair, panties, shibari | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cape | fingerless_gloves | jewelry | looking_at_viewer | simple_background | smile | solo | tiara | armor | bare_shoulders | detached_collar | black_gloves | white_dress | upper_body | closed_mouth | cleavage | medium_breasts | white_background | dress | holding_sword | black_thighhighs | zettai_ryouiki | boots | full_body | cowboy_shot | navel | nipples | completely_nude | large_breasts | pussy | collarbone | outdoors | standing | thighs | water | blush | day | nature | wading | yellow_eyes | solo_focus | thighhighs | hetero | open_mouth | 1boy | breasts_out | clothed_sex | cowgirl_position | cum_in_pussy | girl_on_top | penis | vaginal | spread_legs | sweat | bar_censor | dress_lift | dress_pull | fellatio | nude | testicles | sex | barefoot | arms_behind_back | bondage | feet | rope | toes | pussy_juice | restrained | shibari | clitoris | mosaic_censoring | squatting | uncensored | mmf_threesome | multiple_penises | anal | dark-skinned_male | 2boys | ass | blunt_bangs | double_penetration | interracial | tongue_out | ahegao | cum | faceless_male | gloves | beach | cloud | hair_flower | white_bikini | alternate_costume | blue_sky | ocean | sitting | gagged | improvised_gag | orange_hair | panties | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:--------------------|:----------|:--------------------|:--------------------|:--------|:-------|:--------|:--------|:-----------------|:------------------|:---------------|:--------------|:-------------|:---------------|:-----------|:-----------------|:-------------------|:--------|:----------------|:-------------------|:-----------------|:--------|:------------|:--------------|:--------|:----------|:------------------|:----------------|:--------|:-------------|:-----------|:-----------|:---------|:--------|:--------|:------|:---------|:---------|:--------------|:-------------|:-------------|:---------|:-------------|:-------|:--------------|:--------------|:-------------------|:---------------|:--------------|:--------|:----------|:--------------|:--------|:-------------|:-------------|:-------------|:-----------|:-------|:------------|:------|:-----------|:-------------------|:----------|:-------|:-------|:-------|:--------------|:-------------|:----------|:-----------|:-------------------|:------------|:-------------|:----------------|:-------------------|:-------|:--------------------|:--------|:------|:--------------|:---------------------|:--------------|:-------------|:---------|:------|:----------------|:---------|:--------|:--------|:--------------|:---------------|:--------------------|:-----------|:--------|:----------|:---------|:-----------------|:--------------|:----------| | 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 7 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | | | X | X | | X | X | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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CyberHarem/cellica_fireemblem

2024-01-17T14:48:30+00:00

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

2024-01-17T16:36:58+00:00

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

Dataset of cellica (Fire Emblem) ================================ This is the dataset of cellica (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'long\_hair, red\_hair, red\_eyes, breasts, earrings, bangs, hairband', 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

bd72953c0d3a2aafff3a3945bdcf9a5b58d5b141

# Dataset of elice/エリス (Fire Emblem) This is the dataset of elice/エリス (Fire Emblem), containing 500 images and their tags. The core tags of this character are `blue_hair, blue_eyes, long_hair, hair_between_eyes, breasts, 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 | 652.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elice_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 370.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elice_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1132 | 754.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elice_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 574.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/elice_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1132 | 1.05 GiB | [Download](https://huggingface.co/datasets/CyberHarem/elice_fireemblem/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/elice_fireemblem', 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 | 6 |  |  |  |  |  | 1girl, cape, falchion_(fire_emblem), fingerless_gloves, solo, sword, tiara, smile, armor, blush, looking_at_viewer | | 1 | 12 |  |  |  |  |  | 1girl, cape, falchion_(fire_emblem), solo, tiara, armor, fingerless_gloves, holding_sword, looking_at_viewer, simple_background, belt | | 2 | 5 |  |  |  |  |  | 1girl, blue_gloves, falchion_(fire_emblem), fingerless_gloves, holding_sword, long_sleeves, looking_at_viewer, solo, tiara, white_background, blue_cape, closed_mouth, red_cape, simple_background, blue_footwear, brown_belt, shoulder_armor, sweater, thigh_boots | | 3 | 7 |  |  |  |  |  | 1girl, bare_shoulders, hair_flower, looking_at_viewer, official_alternate_costume, solo, white_dress, sleeveless_dress, smile, blush, cleavage, closed_mouth, collarbone, symbol-shaped_pupils, armlet, small_breasts, upper_body, white_flower | | 4 | 5 |  |  |  |  |  | 1girl, crop_top, looking_at_viewer, midriff, navel, short_shorts, solo, tiara, bare_shoulders, blush, official_alternate_costume, small_breasts, open_mouth, simple_background, sleeveless, thighs, white_background, :d, arm_up, armpits, belt, bikini, blue_shorts, innertube | | 5 | 7 |  |  |  |  |  | 1girl, day, navel, smile, solo, tiara, blue_bikini, crop_top, looking_at_viewer, midriff, outdoors, armpits, bare_shoulders, cloud, ocean, short_shorts, small_breasts, water, alternate_costume, arm_up, beach, belt, blue_sky, blush, closed_mouth, cowboy_shot, innertube, sleeveless, thighs, tree, wet | | 6 | 7 |  |  |  |  |  | 1girl, large_breasts, smile, solo, alternate_breast_size, looking_at_viewer, navel, tiara, blue_bikini, patreon_username | | 7 | 19 |  |  |  |  |  | 1girl, looking_at_viewer, solo, alternate_costume, orange_shorts, short_shorts, tiara, waitress, beer_mug, smile, cleavage, employee_uniform, blush, medium_breasts, open_mouth, white_tank_top, holding_plate, chicken_(food), holding_cup, navel, tray | | 8 | 12 |  |  |  |  |  | 1girl, official_alternate_costume, playboy_bunny, rabbit_ears, smile, solo, fake_animal_ears, looking_at_viewer, white_pantyhose, leotard, rabbit_tail, simple_background, cleavage, open_mouth, blush, small_breasts, easter_egg, frilled_choker, puffy_short_sleeves, white_background, white_gloves | | 9 | 8 |  |  |  |  |  | 1girl, tiara, uncensored, blush, hetero, nipples, penis, pussy, 1boy, looking_at_viewer, navel, sex, solo_focus, spread_legs, large_breasts, open_mouth, vaginal, completely_nude, cum, lying, alternate_breast_size, clitoris, pov, sweat, tongue_out | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cape | falchion_(fire_emblem) | fingerless_gloves | solo | sword | tiara | smile | armor | blush | looking_at_viewer | holding_sword | simple_background | belt | blue_gloves | long_sleeves | white_background | blue_cape | closed_mouth | red_cape | blue_footwear | brown_belt | shoulder_armor | sweater | thigh_boots | bare_shoulders | hair_flower | official_alternate_costume | white_dress | sleeveless_dress | cleavage | collarbone | symbol-shaped_pupils | armlet | small_breasts | upper_body | white_flower | crop_top | midriff | navel | short_shorts | open_mouth | sleeveless | thighs | :d | arm_up | armpits | bikini | blue_shorts | innertube | day | blue_bikini | outdoors | cloud | ocean | water | alternate_costume | beach | blue_sky | cowboy_shot | tree | wet | large_breasts | alternate_breast_size | patreon_username | orange_shorts | waitress | beer_mug | employee_uniform | medium_breasts | white_tank_top | holding_plate | chicken_(food) | holding_cup | tray | playboy_bunny | rabbit_ears | fake_animal_ears | white_pantyhose | leotard | rabbit_tail | easter_egg | frilled_choker | puffy_short_sleeves | white_gloves | uncensored | hetero | nipples | penis | pussy | 1boy | sex | solo_focus | spread_legs | vaginal | completely_nude | cum | lying | clitoris | pov | sweat | tongue_out | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------------------|:--------------------|:-------|:--------|:--------|:--------|:--------|:--------|:--------------------|:----------------|:--------------------|:-------|:--------------|:---------------|:-------------------|:------------|:---------------|:-----------|:----------------|:-------------|:-----------------|:----------|:--------------|:-----------------|:--------------|:-----------------------------|:--------------|:-------------------|:-----------|:-------------|:-----------------------|:---------|:----------------|:-------------|:---------------|:-----------|:----------|:--------|:---------------|:-------------|:-------------|:---------|:-----|:---------|:----------|:---------|:--------------|:------------|:------|:--------------|:-----------|:--------|:--------|:--------|:--------------------|:--------|:-----------|:--------------|:-------|:------|:----------------|:------------------------|:-------------------|:----------------|:-----------|:-----------|:-------------------|:-----------------|:-----------------|:----------------|:-----------------|:--------------|:-------|:----------------|:--------------|:-------------------|:------------------|:----------|:--------------|:-------------|:-----------------|:----------------------|:---------------|:-------------|:---------|:----------|:--------|:--------|:-------|:------|:-------------|:--------------|:----------|:------------------|:------|:--------|:-----------|:------|:--------|:-------------| | 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 |  |  |  |  |  | X | X | X | X | X | | X | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 |  |  |  |  |  | X | | X | X | X | | X | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 7 |  |  |  |  |  | X | | | | X | | | X | | X | X | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | 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 | 7 |  |  |  |  |  | X | | | | X | | X | X | | X | X | | | X | | | | | 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 | 7 |  |  |  |  |  | X | | | | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 19 |  |  |  |  |  | X | | | | X | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 12 |  |  |  |  |  | X | | | | X | | | X | | X | X | | X | | | | X | | | | | | | | | | | X | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | 9 | 8 |  |  |  |  |  | X | | | | | | 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/elice_fireemblem

2024-01-17T14:49:18+00:00

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

2024-01-17T16:40:48+00:00

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

Dataset of elice/エリス (Fire Emblem) ================================== This is the dataset of elice/エリス (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'blue\_hair, blue\_eyes, long\_hair, hair\_between\_eyes, breasts, 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

73bc6119696b545fe2f9e802d5d5e40e12610e40

# Dataset of rea (Fire Emblem) This is the dataset of rea (Fire Emblem), containing 500 images and their tags. The core tags of this character are `long_hair, green_hair, green_eyes, breasts, hair_ornament, large_breasts, hair_flower, 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 | 680.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rea_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 392.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rea_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1143 | 788.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rea_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 600.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rea_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1143 | 1.06 GiB | [Download](https://huggingface.co/datasets/CyberHarem/rea_fireemblem/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/rea_fireemblem', 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 | 6 |  |  |  |  |  | 1girl, closed_mouth, long_sleeves, simple_background, solo, flower, white_dress, bare_shoulders, smile | | 1 | 10 |  |  |  |  |  | 1girl, flower, solo, tiara, crown, closed_mouth, simple_background, smile, upper_body, white_background, portrait | | 2 | 9 |  |  |  |  |  | 1girl, hair_ribbon, pointy_ears, ribbon_braid, side_braid, solo, tiara, twin_braids, closed_mouth, smile, simple_background, looking_at_viewer, upper_body | | 3 | 8 |  |  |  |  |  | 1girl, barefoot, hair_ribbon, pointy_ears, ribbon_braid, solo, tiara, twin_braids, very_long_hair, blue_dress, anklet, floating_hair, full_body, armpits, side_braid, smile, sparkle, open_mouth | | 4 | 6 |  |  |  |  |  | 1girl, fur_trim, gift_box, hair_ribbon, pointy_ears, ribbon_braid, solo, tiara, twin_braids, christmas_ornaments, smile, dress, holding, open_mouth, sack, side_braid | | 5 | 11 |  |  |  |  |  | 1girl, cleavage, closed_mouth, flower, smile, solo, white_bikini, looking_at_viewer, navel, simple_background, white_background | | 6 | 5 |  |  |  |  |  | 1girl, blue_sky, cleavage, closed_mouth, day, flower, navel, outdoors, white_bikini, official_alternate_costume, smile, beach, solo_focus, water, 1boy, cloud, holding_hands, ocean | | 7 | 5 |  |  |  |  |  | 1girl, bare_shoulders, beach, blue_sky, blush, cleavage, closed_mouth, collarbone, day, looking_at_viewer, navel, ocean, outdoors, parted_bangs, solo, stomach, thighs, alternate_costume, cowboy_shot, sunlight, black_bikini, cloud, earrings, forehead, sand, thigh_gap, skindentation, smile, umbrella, very_long_hair, water | | 8 | 6 |  |  |  |  |  | 2girls, cleavage, closed_mouth, flower, navel, thighs, white_bikini, holding, jewelry, legs, sandals, simple_background, smile, full_body, looking_at_viewer, solo_focus, toes, bare_shoulders, circlet, grey_background, official_alternate_costume | | 9 | 5 |  |  |  |  |  | 1girl, circlet, collarbone, looking_at_viewer, navel, parted_bangs, solo, thighs, white_panties, white_shirt, blush, crop_top, flower, smile, cleavage, parted_lips, bare_shoulders, legs, long_sleeves, lying, off-shoulder_shirt, on_bed, tassel | | 10 | 12 |  |  |  |  |  | 1girl, bare_shoulders, solo, cleavage, flower, parted_bangs, white_dress, blush, collarbone, looking_at_viewer, smile, circlet, thighs, sitting | | 11 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blush, crop_top, long_sleeves, midriff, solo, circlet, closed_mouth, collarbone, flower, green_pants, high-waist_pants, looking_at_viewer, navel, parted_bangs, thighs, tight_pants, white_shirt, alternate_costume, cleavage, contemporary, off-shoulder_shirt, smile, yoga_pants, dated, hand_on_hip, simple_background, tassel | | 12 | 22 |  |  |  |  |  | witch_hat, 1girl, solo, halloween_costume, official_alternate_costume, looking_at_viewer, smile, very_long_hair, long_sleeves, blue_dress, simple_background, wide_sleeves, collarbone, holding, blush, closed_mouth, hat_flower, long_dress | | 13 | 10 |  |  |  |  |  | blush, completely_nude, 1girl, nipples, open_mouth, penis, uncensored, hetero, pussy, sex, vaginal, 1boy, solo_focus, pointy_ears, sweat, anus, artist_name, ass, english_text, flower, navel, spread_legs | | 14 | 10 |  |  |  |  |  | 1girl, hetero, blush, flower, solo_focus, fellatio, mosaic_censoring, 1boy, cum, looking_at_viewer, nipples, pubic_hair, gangbang, handjob, huge_breasts, multiple_boys, multiple_penises | | 15 | 5 |  |  |  |  |  | 1boy, 1girl, bar_censor, blush, flower, hetero, penis, alternate_hair_color, breast_sucking, gloved_handjob, huge_breasts, nursing_handjob, smile, tiara, ejaculation, nipples, short_hair, breastfeeding, closed_eyes, crown, grabbing, lactation, open_mouth | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | long_sleeves | simple_background | solo | flower | white_dress | bare_shoulders | smile | tiara | crown | upper_body | white_background | portrait | hair_ribbon | pointy_ears | ribbon_braid | side_braid | twin_braids | looking_at_viewer | barefoot | very_long_hair | blue_dress | anklet | floating_hair | full_body | armpits | sparkle | open_mouth | fur_trim | gift_box | christmas_ornaments | dress | holding | sack | cleavage | white_bikini | navel | blue_sky | day | outdoors | official_alternate_costume | beach | solo_focus | water | 1boy | cloud | holding_hands | ocean | blush | collarbone | parted_bangs | stomach | thighs | alternate_costume | cowboy_shot | sunlight | black_bikini | earrings | forehead | sand | thigh_gap | skindentation | umbrella | 2girls | jewelry | legs | sandals | toes | circlet | grey_background | white_panties | white_shirt | crop_top | parted_lips | lying | off-shoulder_shirt | on_bed | tassel | sitting | midriff | green_pants | high-waist_pants | tight_pants | contemporary | yoga_pants | dated | hand_on_hip | witch_hat | halloween_costume | wide_sleeves | hat_flower | long_dress | completely_nude | nipples | penis | uncensored | hetero | pussy | sex | vaginal | sweat | anus | artist_name | ass | english_text | spread_legs | fellatio | mosaic_censoring | cum | pubic_hair | gangbang | handjob | huge_breasts | multiple_boys | multiple_penises | bar_censor | alternate_hair_color | breast_sucking | gloved_handjob | nursing_handjob | ejaculation | short_hair | breastfeeding | closed_eyes | grabbing | lactation | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:---------------|:--------------------|:-------|:---------|:--------------|:-----------------|:--------|:--------|:--------|:-------------|:-------------------|:-----------|:--------------|:--------------|:---------------|:-------------|:--------------|:--------------------|:-----------|:-----------------|:-------------|:---------|:----------------|:------------|:----------|:----------|:-------------|:-----------|:-----------|:----------------------|:--------|:----------|:-------|:-----------|:---------------|:--------|:-----------|:------|:-----------|:-----------------------------|:--------|:-------------|:--------|:-------|:--------|:----------------|:--------|:--------|:-------------|:---------------|:----------|:---------|:--------------------|:--------------|:-----------|:---------------|:-----------|:-----------|:-------|:------------|:----------------|:-----------|:---------|:----------|:-------|:----------|:-------|:----------|:------------------|:----------------|:--------------|:-----------|:--------------|:--------|:---------------------|:---------|:---------|:----------|:----------|:--------------|:-------------------|:--------------|:---------------|:-------------|:--------|:--------------|:------------|:--------------------|:---------------|:-------------|:-------------|:------------------|:----------|:--------|:-------------|:---------|:--------|:------|:----------|:--------|:-------|:--------------|:------|:---------------|:--------------|:-----------|:-------------------|:------|:-------------|:-----------|:----------|:---------------|:----------------|:-------------------|:-------------|:-----------------------|:-----------------|:-----------------|:------------------|:--------------|:-------------|:----------------|:--------------|:-----------|:------------| | 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 |  |  |  |  |  | X | X | | X | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 |  |  |  |  |  | X | X | | X | X | | | | X | X | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 8 |  |  |  |  |  | X | | | | X | | | | X | X | | | | | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 6 |  |  |  |  |  | X | | | | X | | | | X | X | | | | | X | X | X | X | X | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 11 |  |  |  |  |  | X | X | | X | X | X | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 5 |  |  |  |  |  | X | X | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | 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| | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 12 |  |  |  |  |  | X | | | | X | X | X | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 11 | 5 |  |  |  |  |  | X | X | X | X | X | 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 | 22 |  |  |  |  |  | X | X | X | X | X | | | | X | | | | | | | | | | | X | | X | X | | | | | | | | | | | X | | | | | | | | X | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 13 | 10 |  |  |  |  |  | X | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 14 | 10 |  |  |  |  |  | X | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 15 | 5 |  |  |  |  |  | X | | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/rea_fireemblem

2024-01-17T14:49:27+00:00

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

2024-01-17T16:41:44+00:00

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

Dataset of rea (Fire Emblem) ============================ This is the dataset of rea (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'long\_hair, green\_hair, green\_eyes, breasts, hair\_ornament, large\_breasts, hair\_flower, 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

31e9070c6ec3d8f295659a01f003f2a54fcb3cf2

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

mii-llm/cult-dpo

2024-01-17T14:59:01+00:00

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "system", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2153488, "num_examples": 987}], "download_size": 1208959, "dataset_size": 2153488}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-01-17T14:59:06+00:00

TAGS #region-us

# Dataset Card for "cult-dpo" More Information needed

b27d8e99aa573dae1a9f656ed85c4989a0078114

# Pseudostreaming Malaya-Speech STT Original dataset at https://github.com/mesolitica/malaysian-dataset/tree/master/speech-to-text-semisupervised/pseudolabel-malaya-speech-stt We use https://huggingface.co/mesolitica/conformer-medium-mixed to generate pseudostreaming dataset, source code at https://github.com/mesolitica/malaysian-dataset/tree/master/speech-to-text-semisupervised/pseudostreaming-malaya-speech-stt Total 8667.802379812754 hours. data format from [processed.jsonl](processed.jsonl), ```json [ { "text": "pernahkah", "start": 0.2802439024390244, "end": 0.9005226480836237, "audio_filename": "processed-audio/0-321061-0.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda", "start": 0.2802439024390244, "end": 1.1407317073170733, "audio_filename": "processed-audio/0-321061-1.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda menga", "start": 0.2802439024390244, "end": 1.5410801393728224, "audio_filename": "processed-audio/0-321061-2.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengala", "start": 0.2802439024390244, "end": 1.741254355400697, "audio_filename": "processed-audio/0-321061-3.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami", "start": 0.2802439024390244, "end": 1.8613588850174216, "audio_filename": "processed-audio/0-321061-4.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situ", "start": 0.2802439024390244, "end": 2.061533101045296, "audio_filename": "processed-audio/0-321061-5.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situa", "start": 0.2802439024390244, "end": 2.3017421602787453, "audio_filename": "processed-audio/0-321061-6.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi", "start": 0.2802439024390244, "end": 2.3818118466898954, "audio_filename": "processed-audio/0-321061-7.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di", "start": 0.2802439024390244, "end": 2.541951219512195, "audio_filename": "processed-audio/0-321061-8.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana", "start": 0.2802439024390244, "end": 2.702090592334495, "audio_filename": "processed-audio/0-321061-9.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana su", "start": 0.2802439024390244, "end": 2.9823344947735193, "audio_filename": "processed-audio/0-321061-10.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana sub", "start": 0.2802439024390244, "end": 3.102439024390244, "audio_filename": "processed-audio/0-321061-11.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subj", "start": 0.2802439024390244, "end": 3.182508710801394, "audio_filename": "processed-audio/0-321061-12.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subje", "start": 0.2802439024390244, "end": 3.3026132404181183, "audio_filename": "processed-audio/0-321061-13.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek", "start": 0.2802439024390244, "end": 3.3426480836236934, "audio_filename": "processed-audio/0-321061-14.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek ter", "start": 0.2802439024390244, "end": 3.462752613240418, "audio_filename": "processed-audio/0-321061-15.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terke", "start": 0.2802439024390244, "end": 3.622891986062718, "audio_filename": "processed-audio/0-321061-16.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkelu", "start": 0.2802439024390244, "end": 3.7830313588850175, "audio_filename": "processed-audio/0-321061-17.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar", "start": 0.2802439024390244, "end": 3.863101045296167, "audio_filename": "processed-audio/0-321061-18.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada", "start": 0.2802439024390244, "end": 3.9832055749128923, "audio_filename": "processed-audio/0-321061-19.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada bi", "start": 0.2802439024390244, "end": 4.463623693379791, "audio_filename": "processed-audio/0-321061-20.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada bing", "start": 0.2802439024390244, "end": 4.62376306620209, "audio_filename": "processed-audio/0-321061-21.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada bingka", "start": 0.2802439024390244, "end": 4.663797909407666, "audio_filename": "processed-audio/0-321061-22.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada bingkai", "start": 0.2802439024390244, "end": 4.7438675958188155, "audio_filename": "processed-audio/0-321061-23.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada bingkai gambar", "start": 0.2802439024390244, "end": 4.863972125435541, "audio_filename": "processed-audio/0-321061-24.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" }, { "text": "pernahkah anda mengalami situasi di mana subjek terkeluar daripada bingkai gambar", "start": 0.2802439024390244, "end": 4.863972125435541, "audio_filename": "processed-audio/0-321061-25.mp3", "original_audio_filename": "output-audio/0-10033-5.mp3" } ] ``` ## how-to ```bash git clone https://huggingface.co/datasets/mesolitica/pseudostreaming-malaya-speech-stt cd pseudostreaming-malaya-speech-stt wget https://www.7-zip.org/a/7z2301-linux-x64.tar.xz tar -xf 7z2301-linux-x64.tar.xz ./7zz x processed-audio.7z.001 -y -mmt40 ```

mesolitica/pseudostreaming-malaya-speech-stt

2024-01-17T14:59:47+00:00

{"language": ["ms"], "license": "mit", "task_categories": ["automatic-speech-recognition"]}

2024-02-12T07:57:36+00:00

TAGS #task_categories-automatic-speech-recognition #language-Malay (macrolanguage) #license-mit #region-us

# Pseudostreaming Malaya-Speech STT Original dataset at URL We use URL to generate pseudostreaming dataset, source code at URL Total 8667.802379812754 hours. data format from URL, ## how-to

25f3fb413936f65622851b69b1a128fa5f7832d8

# Dataset of anna (Fire Emblem) This is the dataset of anna (Fire Emblem), containing 353 images and their tags. The core tags of this character are `red_hair, ponytail, breasts, red_eyes, long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 353 | 399.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anna_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 353 | 225.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anna_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 841 | 474.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anna_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 353 | 351.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anna_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 841 | 663.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/anna_fireemblem/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/anna_fireemblem', 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 | 17 |  |  |  |  |  | 1girl, solo, looking_at_viewer, smile, simple_background, white_background, blush, cape, gloves, one_eye_closed, open_mouth, upper_body | | 1 | 15 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, penis, blush, solo_focus, smile, open_mouth, cowgirl_position, cum_on_body, girl_on_top, mosaic_censoring, navel, sex, vaginal, completely_nude, pov, uncensored | | 2 | 9 |  |  |  |  |  | 1girl, nipples, solo, uncensored, completely_nude, erection, huge_penis, large_penis, blush, large_breasts, navel, futanari_masturbation, open_mouth, veins, artist_name, ejaculation, large_testicles | | 3 | 10 |  |  |  |  |  | 1girl, bare_shoulders, hair_flower, white_dress, smile, solo, looking_at_viewer, simple_background, bangs, detached_sleeves, bride, full_body, holding, jewelry, official_alternate_costume, wedding_dress, choker, rose, white_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | smile | simple_background | white_background | blush | cape | gloves | one_eye_closed | open_mouth | upper_body | 1boy | hetero | nipples | penis | solo_focus | cowgirl_position | cum_on_body | girl_on_top | mosaic_censoring | navel | sex | vaginal | completely_nude | pov | uncensored | erection | huge_penis | large_penis | large_breasts | futanari_masturbation | veins | artist_name | ejaculation | large_testicles | bare_shoulders | hair_flower | white_dress | bangs | detached_sleeves | bride | full_body | holding | jewelry | official_alternate_costume | wedding_dress | choker | rose | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:--------------------|:-------------------|:--------|:-------|:---------|:-----------------|:-------------|:-------------|:-------|:---------|:----------|:--------|:-------------|:-------------------|:--------------|:--------------|:-------------------|:--------|:------|:----------|:------------------|:------|:-------------|:-----------|:-------------|:--------------|:----------------|:------------------------|:--------|:--------------|:--------------|:------------------|:-----------------|:--------------|:--------------|:--------|:-------------------|:--------|:------------|:----------|:----------|:-----------------------------|:----------------|:---------|:-------| | 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 15 |  |  |  |  |  | X | | | X | | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | 2 | 9 |  |  |  |  |  | X | X | | | | | X | | | | X | | | | X | | | | | | | X | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 3 | 10 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/anna_fireemblem

2024-01-17T15:03:26+00:00

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

2024-01-17T16:41:03+00:00

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

Dataset of anna (Fire Emblem) ============================= This is the dataset of anna (Fire Emblem), containing 353 images and their tags. The core tags of this character are 'red\_hair, ponytail, breasts, red\_eyes, long\_hair', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

8a39f50394f20aba10ad59c0ac1fbae246176025

# Dataset of furen (Fire Emblem) This is the dataset of furen (Fire Emblem), containing 466 images and their tags. The core tags of this character are `green_hair, long_hair, green_eyes, hair_ornament, drill_hair, 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 | 466 | 497.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 466 | 304.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 977 | 605.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 466 | 451.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 977 | 832.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/furen_fireemblem/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/furen_fireemblem', 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 |  |  |  |  |  | 1girl, closed_mouth, full_body, garreg_mach_monastery_uniform, long_sleeves, solo, black_footwear, simple_background, smile, white_background, knee_boots, pantyhose, black_dress | | 1 | 11 |  |  |  |  |  | 1girl, closed_mouth, garreg_mach_monastery_uniform, smile, solo, long_sleeves, upper_body, simple_background, hairclip, white_background | | 2 | 27 |  |  |  |  |  | 1girl, garreg_mach_monastery_uniform, solo, long_sleeves, open_mouth, upper_body, simple_background, white_background | | 3 | 5 |  |  |  |  |  | 1girl, bell, cat_tail, dress, solo, alternate_costume, long_sleeves, tail_ornament, white_gloves, cat_ears, open_mouth, halloween_costume, holding, paw_gloves, paw_pose, paw_print, smile | | 4 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, mosaic_censoring, solo_focus, looking_at_viewer, hairclip, open_mouth, pov, cum, handjob, licking_penis, tongue_out | | 5 | 12 |  |  |  |  |  | 1girl, 1boy, hetero, open_mouth, vaginal, blush, penis, sex, breasts, solo_focus, cum_in_pussy, nipples, censored, spread_legs, completely_nude, sweat | | 6 | 9 |  |  |  |  |  | 1girl, nipples, completely_nude, navel, solo, blush, pussy, looking_at_viewer, closed_mouth, small_breasts | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | full_body | garreg_mach_monastery_uniform | long_sleeves | solo | black_footwear | simple_background | smile | white_background | knee_boots | pantyhose | black_dress | upper_body | hairclip | open_mouth | bell | cat_tail | dress | alternate_costume | tail_ornament | white_gloves | cat_ears | halloween_costume | holding | paw_gloves | paw_pose | paw_print | 1boy | blush | hetero | mosaic_censoring | solo_focus | looking_at_viewer | pov | cum | handjob | licking_penis | tongue_out | vaginal | penis | sex | breasts | cum_in_pussy | nipples | censored | spread_legs | completely_nude | sweat | navel | pussy | small_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:------------|:--------------------------------|:---------------|:-------|:-----------------|:--------------------|:--------|:-------------------|:-------------|:------------|:--------------|:-------------|:-----------|:-------------|:-------|:-----------|:--------|:--------------------|:----------------|:---------------|:-----------|:--------------------|:----------|:-------------|:-----------|:------------|:-------|:--------|:---------|:-------------------|:-------------|:--------------------|:------|:------|:----------|:----------------|:-------------|:----------|:--------|:------|:----------|:---------------|:----------|:-----------|:--------------|:------------------|:--------|:--------|:--------|:----------------| | 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 |  |  |  |  |  | X | X | | X | X | X | | X | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 27 |  |  |  |  |  | X | | | X | X | X | | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | | | | X | X | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | X | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 5 | 12 |  |  |  |  |  | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | 6 | 9 |  |  |  |  |  | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | X | | | X | | X | X | X |

CyberHarem/furen_fireemblem

2024-01-17T15:03:28+00:00

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

2024-01-17T16:31:57+00:00

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

Dataset of furen (Fire Emblem) ============================== This is the dataset of furen (Fire Emblem), containing 466 images and their tags. The core tags of this character are 'green\_hair, long\_hair, green\_eyes, hair\_ornament, drill\_hair, 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

d1dc27221cf2f1e8ac2360b46a5dd6482ca74b35

# Dataset of tiamo (Fire Emblem) This is the dataset of tiamo (Fire Emblem), containing 449 images and their tags. The core tags of this character are `long_hair, red_hair, red_eyes, breasts, hair_ornament, hair_between_eyes, very_long_hair`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 449 | 532.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tiamo_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 449 | 324.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tiamo_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1004 | 622.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tiamo_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 449 | 482.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tiamo_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1004 | 835.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tiamo_fireemblem/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/tiamo_fireemblem', 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 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, nipples, smile, solo, large_breasts, completely_nude, female_pubic_hair, pussy | | 1 | 6 |  |  |  |  |  | 1girl, blush, navel, nipples, solo, thighhighs, looking_at_viewer, pussy, elbow_gloves, medium_breasts, nude, small_breasts, smile | | 2 | 11 |  |  |  |  |  | 1boy, 1girl, blush, completely_nude, hetero, nipples, sex, solo_focus, vaginal, mosaic_censoring, navel, open_mouth, penis, pussy, spread_legs, small_breasts, medium_breasts, sweat, missionary, on_back, pov, looking_at_viewer, pillow | | 3 | 5 |  |  |  |  |  | 1boy, 1girl, anus, blush, completely_nude, hetero, looking_at_viewer, looking_back, mosaic_censoring, open_mouth, penis, pussy, solo_focus, vaginal, medium_breasts, nipples, sex_from_behind, ass_grab, girl_on_top, reverse_cowgirl_position, indoors, spread_legs, sweat, wing_hair_ornament | | 4 | 10 |  |  |  |  |  | 1girl, garter_straps, gauntlets, solo, thighhighs, thigh_boots, spear, breastplate, looking_at_viewer, belt | | 5 | 6 |  |  |  |  |  | 1girl, garter_straps, holding_weapon, solo, thigh_boots, thighhighs, breastplate, feathers, gloves, looking_at_viewer, red_dress, short_dress, smile, spear, wing_hair_ornament, gauntlets, shoulder_armor, zettai_ryouiki | | 6 | 9 |  |  |  |  |  | 1girl, gauntlets, smile, solo, looking_at_viewer, polearm, breastplate, holding_weapon, simple_background, white_background | | 7 | 5 |  |  |  |  |  | 1girl, full_body, holding_bow_(weapon), solo, wedding_dress, white_dress, high_heels, looking_at_viewer, bride, gloves, simple_background, smile, bare_shoulders, bridal_gauntlets, grey_background, holding_arrow, one_eye_closed, open_mouth, white_background | | 8 | 9 |  |  |  |  |  | wedding_dress, 1girl, bare_shoulders, blush, looking_at_viewer, smile, solo, white_dress, bride, pearl_necklace | | 9 | 9 |  |  |  |  |  | 1girl, solo, bare_shoulders, holding_weapon, looking_at_viewer, navel, red_bikini, fingerless_gloves, collarbone, fish, smile, bangs, bikini_skirt, cleavage, full_body, simple_background, spear, blush, sandals, small_breasts, toeless_footwear | | 10 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, red_bikini, navel, smile, blush, sky, small_breasts, upper_body, collarbone, bare_shoulders, cloud, day, bangs, open_mouth, outdoors, wing_hair_ornament | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | navel | nipples | smile | solo | large_breasts | completely_nude | female_pubic_hair | pussy | thighhighs | elbow_gloves | medium_breasts | nude | small_breasts | 1boy | hetero | sex | solo_focus | vaginal | mosaic_censoring | open_mouth | penis | spread_legs | sweat | missionary | on_back | pov | pillow | anus | looking_back | sex_from_behind | ass_grab | girl_on_top | reverse_cowgirl_position | indoors | wing_hair_ornament | garter_straps | gauntlets | thigh_boots | spear | breastplate | belt | holding_weapon | feathers | gloves | red_dress | short_dress | shoulder_armor | zettai_ryouiki | polearm | simple_background | white_background | full_body | holding_bow_(weapon) | wedding_dress | white_dress | high_heels | bride | bare_shoulders | bridal_gauntlets | grey_background | holding_arrow | one_eye_closed | pearl_necklace | red_bikini | fingerless_gloves | collarbone | fish | bangs | bikini_skirt | cleavage | sandals | toeless_footwear | sky | upper_body | cloud | day | outdoors | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------------|:--------|:----------|:--------|:-------|:----------------|:------------------|:--------------------|:--------|:-------------|:---------------|:-----------------|:-------|:----------------|:-------|:---------|:------|:-------------|:----------|:-------------------|:-------------|:--------|:--------------|:--------|:-------------|:----------|:------|:---------|:-------|:---------------|:------------------|:-----------|:--------------|:---------------------------|:----------|:---------------------|:----------------|:------------|:--------------|:--------|:--------------|:-------|:-----------------|:-----------|:---------|:------------|:--------------|:-----------------|:-----------------|:----------|:--------------------|:-------------------|:------------|:-----------------------|:----------------|:--------------|:-------------|:--------|:-----------------|:-------------------|:------------------|:----------------|:-----------------|:-----------------|:-------------|:--------------------|:-------------|:-------|:--------|:---------------|:-----------|:----------|:-------------------|:------|:-------------|:--------|:------|:-----------| | 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 11 |  |  |  |  |  | X | X | X | X | X | | | | X | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | 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 | 10 |  |  |  |  |  | X | | X | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 |  |  |  |  |  | X | | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 9 |  |  |  |  |  | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 5 |  |  |  |  |  | X | | X | | | X | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | 8 | 9 |  |  |  |  |  | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | X | | | | | X | | | | | | | | | | | | | | | | 9 | 9 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | X | | X | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | 10 | 14 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | X | | X | | | | | X | X | X | X | X |

CyberHarem/tiamo_fireemblem

2024-01-17T15:03:39+00:00

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

2024-01-17T16:43:50+00:00

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

Dataset of tiamo (Fire Emblem) ============================== This is the dataset of tiamo (Fire Emblem), containing 449 images and their tags. The core tags of this character are 'long\_hair, red\_hair, red\_eyes, breasts, hair\_ornament, hair\_between\_eyes, very\_long\_hair', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

9c87532c584b68abb25361927af33cf16dbb010a

# LVIS ### Dataset Summary This dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. - https://www.lvisdataset.org/ ### Loading This code returns train, validation and test generators. ```python from datasets import load_dataset dataset = load_dataset("winvoker/lvis") ``` Objects is a dictionary which contains annotation information like bbox, class. ``` DatasetDict({ train: Dataset({ features: ['id', 'image', 'height', 'width', 'objects'], num_rows: 100170 }) validation: Dataset({ features: ['id', 'image', 'height', 'width', 'objects'], num_rows: 4809 }) test: Dataset({ features: ['id', 'image', 'height', 'width', 'objects'], num_rows: 19822 }) }) ``` ### Access Generators ```python train = dataset["train"] validation = dataset["validation"] test = dataset["test"] ``` An example row is as follows. ```json { 'id': 0, 'image': '000000437561.jpg', 'height': 480, 'width': 640, 'objects': { 'bboxes': [[[392, 271, 14, 3]], 'classes': [117], 'segmentation': [[376, 272, 375, 270, 372, 269, 371, 269, 373, 269, 373]] } } ```

anvilarth/lvis

2024-01-17T15:10:32+00:00

{"language": ["en"], "license": "apache-2.0"}

2024-01-17T15:20:57+00:00

TAGS #language-English #license-apache-2.0 #region-us

# LVIS ### Dataset Summary This dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. - URL ### Loading This code returns train, validation and test generators. Objects is a dictionary which contains annotation information like bbox, class. ### Access Generators An example row is as follows.

b20883557e0f1e05be6480dfa1e7be3bcbdb18db

# Dataset Card for Evaluation run of KnutJaegersberg/Deita-Qwen-1_8B <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-Qwen-1_8B](https://huggingface.co/KnutJaegersberg/Deita-Qwen-1_8B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__Deita-Qwen-1_8B", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-17T15:12:59.171599](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-Qwen-1_8B/blob/main/results_2024-01-17T15-12-59.171599.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": 0.4513695685616451, "acc_stderr": 0.03473174413713779, "acc_norm": 0.4572369573723266, "acc_norm_stderr": 0.035511285827617124, "mc1": 0.2521419828641371, "mc1_stderr": 0.015201522246299979, "mc2": 0.4002214148044727, "mc2_stderr": 0.014908452990717655 }, "harness|arc:challenge|25": { "acc": 0.32081911262798635, "acc_stderr": 0.013640943091946522, "acc_norm": 0.3651877133105802, "acc_norm_stderr": 0.014070265519268802 }, "harness|hellaswag|10": { "acc": 0.4574785899223262, "acc_stderr": 0.004971704917267752, "acc_norm": 0.6062537343158734, "acc_norm_stderr": 0.004875812021461993 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.42962962962962964, "acc_stderr": 0.04276349494376599, "acc_norm": 0.42962962962962964, "acc_norm_stderr": 0.04276349494376599 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5131578947368421, "acc_stderr": 0.04067533136309173, "acc_norm": 0.5131578947368421, "acc_norm_stderr": 0.04067533136309173 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.44, "acc_stderr": 0.04988876515698589, "acc_norm": 0.44, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.4830188679245283, "acc_stderr": 0.030755120364119898, "acc_norm": 0.4830188679245283, "acc_norm_stderr": 0.030755120364119898 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.375, "acc_stderr": 0.04048439222695598, "acc_norm": 0.375, "acc_norm_stderr": 0.04048439222695598 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.4, "acc_stderr": 0.049236596391733084, "acc_norm": 0.4, "acc_norm_stderr": 0.049236596391733084 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.28, "acc_stderr": 0.04512608598542127, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542127 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4393063583815029, "acc_stderr": 0.03784271932887467, "acc_norm": 0.4393063583815029, "acc_norm_stderr": 0.03784271932887467 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3431372549019608, "acc_stderr": 0.04724007352383889, "acc_norm": 0.3431372549019608, "acc_norm_stderr": 0.04724007352383889 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.3829787234042553, "acc_stderr": 0.03177821250236922, "acc_norm": 0.3829787234042553, "acc_norm_stderr": 0.03177821250236922 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2982456140350877, "acc_stderr": 0.04303684033537315, "acc_norm": 0.2982456140350877, "acc_norm_stderr": 0.04303684033537315 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.42758620689655175, "acc_stderr": 0.041227371113703316, "acc_norm": 0.42758620689655175, "acc_norm_stderr": 0.041227371113703316 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3148148148148148, "acc_stderr": 0.023919984164047732, "acc_norm": 0.3148148148148148, "acc_norm_stderr": 0.023919984164047732 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.35714285714285715, "acc_stderr": 0.04285714285714281, "acc_norm": 0.35714285714285715, "acc_norm_stderr": 0.04285714285714281 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.5225806451612903, "acc_stderr": 0.02841498501970786, "acc_norm": 0.5225806451612903, "acc_norm_stderr": 0.02841498501970786 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4088669950738916, "acc_stderr": 0.034590588158832314, "acc_norm": 0.4088669950738916, "acc_norm_stderr": 0.034590588158832314 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.45, "acc_stderr": 0.049999999999999996, "acc_norm": 0.45, "acc_norm_stderr": 0.049999999999999996 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5333333333333333, "acc_stderr": 0.038956580652718446, "acc_norm": 0.5333333333333333, "acc_norm_stderr": 0.038956580652718446 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5303030303030303, "acc_stderr": 0.03555804051763929, "acc_norm": 0.5303030303030303, "acc_norm_stderr": 0.03555804051763929 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6010362694300518, "acc_stderr": 0.03533999094065696, "acc_norm": 0.6010362694300518, "acc_norm_stderr": 0.03533999094065696 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.43333333333333335, "acc_stderr": 0.02512465352588513, "acc_norm": 0.43333333333333335, "acc_norm_stderr": 0.02512465352588513 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.25555555555555554, "acc_stderr": 0.02659393910184405, "acc_norm": 0.25555555555555554, "acc_norm_stderr": 0.02659393910184405 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4495798319327731, "acc_stderr": 0.03231293497137707, "acc_norm": 0.4495798319327731, "acc_norm_stderr": 0.03231293497137707 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.33774834437086093, "acc_stderr": 0.0386155754625517, "acc_norm": 0.33774834437086093, "acc_norm_stderr": 0.0386155754625517 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5614678899082569, "acc_stderr": 0.021274713073954572, "acc_norm": 0.5614678899082569, "acc_norm_stderr": 0.021274713073954572 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4398148148148148, "acc_stderr": 0.03385177976044811, "acc_norm": 0.4398148148148148, "acc_norm_stderr": 0.03385177976044811 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.5147058823529411, "acc_stderr": 0.03507793834791324, "acc_norm": 0.5147058823529411, "acc_norm_stderr": 0.03507793834791324 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.6033755274261603, "acc_stderr": 0.03184399873811224, "acc_norm": 0.6033755274261603, "acc_norm_stderr": 0.03184399873811224 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.4977578475336323, "acc_stderr": 0.033557465352232634, "acc_norm": 0.4977578475336323, "acc_norm_stderr": 0.033557465352232634 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5114503816793893, "acc_stderr": 0.043841400240780176, "acc_norm": 0.5114503816793893, "acc_norm_stderr": 0.043841400240780176 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6033057851239669, "acc_stderr": 0.044658697805310094, "acc_norm": 0.6033057851239669, "acc_norm_stderr": 0.044658697805310094 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5277777777777778, "acc_stderr": 0.048262172941398944, "acc_norm": 0.5277777777777778, "acc_norm_stderr": 0.048262172941398944 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.44171779141104295, "acc_stderr": 0.03901591825836184, "acc_norm": 0.44171779141104295, "acc_norm_stderr": 0.03901591825836184 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.26785714285714285, "acc_stderr": 0.04203277291467762, "acc_norm": 0.26785714285714285, "acc_norm_stderr": 0.04203277291467762 }, "harness|hendrycksTest-management|5": { "acc": 0.6699029126213593, "acc_stderr": 0.046561471100123514, "acc_norm": 0.6699029126213593, "acc_norm_stderr": 0.046561471100123514 }, "harness|hendrycksTest-marketing|5": { "acc": 0.6709401709401709, "acc_stderr": 0.03078232157768817, "acc_norm": 0.6709401709401709, "acc_norm_stderr": 0.03078232157768817 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620332, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620332 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5504469987228607, "acc_stderr": 0.017788725283507337, "acc_norm": 0.5504469987228607, "acc_norm_stderr": 0.017788725283507337 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.48265895953757226, "acc_stderr": 0.026902900458666647, "acc_norm": 0.48265895953757226, "acc_norm_stderr": 0.026902900458666647 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24804469273743016, "acc_stderr": 0.01444415780826144, "acc_norm": 0.24804469273743016, "acc_norm_stderr": 0.01444415780826144 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5620915032679739, "acc_stderr": 0.02840830202033269, "acc_norm": 0.5620915032679739, "acc_norm_stderr": 0.02840830202033269 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.5048231511254019, "acc_stderr": 0.028396770444111298, "acc_norm": 0.5048231511254019, "acc_norm_stderr": 0.028396770444111298 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.45987654320987653, "acc_stderr": 0.02773102275353928, "acc_norm": 0.45987654320987653, "acc_norm_stderr": 0.02773102275353928 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.37943262411347517, "acc_stderr": 0.028947338851614105, "acc_norm": 0.37943262411347517, "acc_norm_stderr": 0.028947338851614105 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3324641460234681, "acc_stderr": 0.012032022332260507, "acc_norm": 0.3324641460234681, "acc_norm_stderr": 0.012032022332260507 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.4007352941176471, "acc_stderr": 0.029768263528933102, "acc_norm": 0.4007352941176471, "acc_norm_stderr": 0.029768263528933102 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.4019607843137255, "acc_stderr": 0.019835176484375376, "acc_norm": 0.4019607843137255, "acc_norm_stderr": 0.019835176484375376 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5363636363636364, "acc_stderr": 0.04776449162396197, "acc_norm": 0.5363636363636364, "acc_norm_stderr": 0.04776449162396197 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.5387755102040817, "acc_stderr": 0.031912820526692774, "acc_norm": 0.5387755102040817, "acc_norm_stderr": 0.031912820526692774 }, "harness|hendrycksTest-sociology|5": { "acc": 0.582089552238806, "acc_stderr": 0.034875586404620636, "acc_norm": 0.582089552238806, "acc_norm_stderr": 0.034875586404620636 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-virology|5": { "acc": 0.40963855421686746, "acc_stderr": 0.03828401115079023, "acc_norm": 0.40963855421686746, "acc_norm_stderr": 0.03828401115079023 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.5497076023391813, "acc_stderr": 0.038158273659132366, "acc_norm": 0.5497076023391813, "acc_norm_stderr": 0.038158273659132366 }, "harness|truthfulqa:mc|0": { "mc1": 0.2521419828641371, "mc1_stderr": 0.015201522246299979, "mc2": 0.4002214148044727, "mc2_stderr": 0.014908452990717655 }, "harness|winogrande|5": { "acc": 0.5935280189423836, "acc_stderr": 0.013804448697753376 }, "harness|gsm8k|5": { "acc": 0.1561789234268385, "acc_stderr": 0.00999950936975745 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section 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the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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open-llm-leaderboard/details_KnutJaegersberg__Deita-Qwen-1_8B

2024-01-17T15:15:09+00:00

{"pretty_name": "Evaluation run of KnutJaegersberg/Deita-Qwen-1_8B", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/Deita-Qwen-1_8B](https://huggingface.co/KnutJaegersberg/Deita-Qwen-1_8B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__Deita-Qwen-1_8B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T15:12:59.171599](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__Deita-Qwen-1_8B/blob/main/results_2024-01-17T15-12-59.171599.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\": 0.4513695685616451,\n \"acc_stderr\": 0.03473174413713779,\n \"acc_norm\": 0.4572369573723266,\n \"acc_norm_stderr\": 0.035511285827617124,\n \"mc1\": 0.2521419828641371,\n \"mc1_stderr\": 0.015201522246299979,\n \"mc2\": 0.4002214148044727,\n \"mc2_stderr\": 0.014908452990717655\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.32081911262798635,\n \"acc_stderr\": 0.013640943091946522,\n \"acc_norm\": 0.3651877133105802,\n \"acc_norm_stderr\": 0.014070265519268802\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4574785899223262,\n \"acc_stderr\": 0.004971704917267752,\n \"acc_norm\": 0.6062537343158734,\n \"acc_norm_stderr\": 0.004875812021461993\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.42962962962962964,\n \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309173,\n \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309173\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.4830188679245283,\n \"acc_stderr\": 0.030755120364119898,\n \"acc_norm\": 0.4830188679245283,\n \"acc_norm_stderr\": 0.030755120364119898\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04048439222695598,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04048439222695598\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4393063583815029,\n \"acc_stderr\": 0.03784271932887467,\n \"acc_norm\": 0.4393063583815029,\n \"acc_norm_stderr\": 0.03784271932887467\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383889,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383889\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3829787234042553,\n \"acc_stderr\": 0.03177821250236922,\n \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.03177821250236922\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.041227371113703316,\n \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.041227371113703316\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.023919984164047732,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.023919984164047732\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5225806451612903,\n \"acc_stderr\": 0.02841498501970786,\n \"acc_norm\": 0.5225806451612903,\n \"acc_norm_stderr\": 0.02841498501970786\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4088669950738916,\n \"acc_stderr\": 0.034590588158832314,\n \"acc_norm\": 0.4088669950738916,\n \"acc_norm_stderr\": 0.034590588158832314\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.038956580652718446,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.038956580652718446\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5303030303030303,\n \"acc_stderr\": 0.03555804051763929,\n \"acc_norm\": 0.5303030303030303,\n \"acc_norm_stderr\": 0.03555804051763929\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6010362694300518,\n \"acc_stderr\": 0.03533999094065696,\n \"acc_norm\": 0.6010362694300518,\n \"acc_norm_stderr\": 0.03533999094065696\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.43333333333333335,\n \"acc_stderr\": 0.02512465352588513,\n \"acc_norm\": 0.43333333333333335,\n \"acc_norm_stderr\": 0.02512465352588513\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25555555555555554,\n \"acc_stderr\": 0.02659393910184405,\n \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.02659393910184405\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.4495798319327731,\n \"acc_stderr\": 0.03231293497137707,\n \"acc_norm\": 0.4495798319327731,\n \"acc_norm_stderr\": 0.03231293497137707\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.0386155754625517,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.0386155754625517\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5614678899082569,\n \"acc_stderr\": 0.021274713073954572,\n \"acc_norm\": 0.5614678899082569,\n \"acc_norm_stderr\": 0.021274713073954572\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4398148148148148,\n \"acc_stderr\": 0.03385177976044811,\n \"acc_norm\": 0.4398148148148148,\n \"acc_norm_stderr\": 0.03385177976044811\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03507793834791324,\n \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03507793834791324\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6033755274261603,\n \"acc_stderr\": 0.03184399873811224,\n \"acc_norm\": 0.6033755274261603,\n \"acc_norm_stderr\": 0.03184399873811224\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.4977578475336323,\n \"acc_stderr\": 0.033557465352232634,\n \"acc_norm\": 0.4977578475336323,\n \"acc_norm_stderr\": 0.033557465352232634\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5114503816793893,\n \"acc_stderr\": 0.043841400240780176,\n \"acc_norm\": 0.5114503816793893,\n \"acc_norm_stderr\": 0.043841400240780176\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6033057851239669,\n \"acc_stderr\": 0.044658697805310094,\n \"acc_norm\": 0.6033057851239669,\n \"acc_norm_stderr\": 0.044658697805310094\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5277777777777778,\n \"acc_stderr\": 0.048262172941398944,\n \"acc_norm\": 0.5277777777777778,\n \"acc_norm_stderr\": 0.048262172941398944\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.44171779141104295,\n \"acc_stderr\": 0.03901591825836184,\n \"acc_norm\": 0.44171779141104295,\n \"acc_norm_stderr\": 0.03901591825836184\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n \"acc_stderr\": 0.04203277291467762,\n \"acc_norm\": 0.26785714285714285,\n \"acc_norm_stderr\": 0.04203277291467762\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.046561471100123514,\n \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.046561471100123514\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6709401709401709,\n \"acc_stderr\": 0.03078232157768817,\n \"acc_norm\": 0.6709401709401709,\n \"acc_norm_stderr\": 0.03078232157768817\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5504469987228607,\n \"acc_stderr\": 0.017788725283507337,\n \"acc_norm\": 0.5504469987228607,\n \"acc_norm_stderr\": 0.017788725283507337\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.48265895953757226,\n \"acc_stderr\": 0.026902900458666647,\n \"acc_norm\": 0.48265895953757226,\n \"acc_norm_stderr\": 0.026902900458666647\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24804469273743016,\n \"acc_stderr\": 0.01444415780826144,\n \"acc_norm\": 0.24804469273743016,\n \"acc_norm_stderr\": 0.01444415780826144\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5620915032679739,\n \"acc_stderr\": 0.02840830202033269,\n \"acc_norm\": 0.5620915032679739,\n \"acc_norm_stderr\": 0.02840830202033269\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5048231511254019,\n \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.5048231511254019,\n \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.45987654320987653,\n \"acc_stderr\": 0.02773102275353928,\n \"acc_norm\": 0.45987654320987653,\n \"acc_norm_stderr\": 0.02773102275353928\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.37943262411347517,\n \"acc_stderr\": 0.028947338851614105,\n \"acc_norm\": 0.37943262411347517,\n \"acc_norm_stderr\": 0.028947338851614105\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3324641460234681,\n \"acc_stderr\": 0.012032022332260507,\n \"acc_norm\": 0.3324641460234681,\n \"acc_norm_stderr\": 0.012032022332260507\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.029768263528933102,\n \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.029768263528933102\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 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2024-01-17T15:15:37+00:00

TAGS #region-us

# Dataset Card for Evaluation run of KnutJaegersberg/Deita-Qwen-1_8B Dataset automatically created during the evaluation run of model KnutJaegersberg/Deita-Qwen-1_8B on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-01-17T15:12:59.171599(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

329cc063c1518678f55887a723a527ef1a35e8cc

``` CCI-Data SkyPile-150B TeleChat-PTD WebText-cn WuDaoCorpus2.0 wangan yayi2_pretrain_data ``` 整合+minhash去重了一波，最终得到550B中文预训练语料

genggui001/gg_zh_v1_550B

2024-01-17T15:17:57+00:00

{"language": ["zh"], "size_categories": ["100B<n<1T"], "task_categories": ["text-generation"]}

2024-01-20T10:47:46+00:00

TAGS #task_categories-text-generation #size_categories-100B<n<1T #language-Chinese #region-us

整合+minhash去重了一波，最终得到550B中文预训练语料

9305db165c1536cae36a0ee7115de7e2cd7fb671

# Dataset of micaiah (Fire Emblem) This is the dataset of micaiah (Fire Emblem), containing 500 images and their tags. The core tags of this character are `long_hair, yellow_eyes, bangs, grey_hair, ribbon, hair_ribbon, half_updo, breasts, white_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 | 769.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/micaiah_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 397.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/micaiah_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1237 | 854.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/micaiah_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 661.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/micaiah_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1237 | 1.22 GiB | [Download](https://huggingface.co/datasets/CyberHarem/micaiah_fireemblem/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/micaiah_fireemblem', 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 | 30 |  |  |  |  |  | official_alternate_costume, red_bikini, 1girl, solo, bare_shoulders, cleavage, hair_flower, navel, bikini_skirt, blue_scarf, looking_at_viewer, red_gloves, collarbone, open_mouth, bird, blush, groin, :d, front-tie_bikini_top, towel, cowboy_shot, simple_background, miniskirt, outdoors, sky, fingerless_gloves, water | | 1 | 6 |  |  |  |  |  | 1girl, blush, nipples, solo, collarbone, groin, looking_at_viewer, navel, pussy, smile, simple_background, ass_visible_through_thighs, completely_nude, white_background | | 2 | 7 |  |  |  |  |  | 1girl, bangle, bare_shoulders, belt, black_gloves, black_pantyhose, blue_scarf, elbow_gloves, fingerless_gloves, side_slit, simple_background, sleeveless_dress, solo, bird, boots, smile, white_background | | 3 | 5 |  |  |  |  |  | 1girl, bangle, bare_shoulders, black_gloves, black_pantyhose, blue_scarf, cowboy_shot, elbow_gloves, fingerless_gloves, side_slit, simple_background, sleeveless_dress, solo, white_background, belt, looking_at_viewer, smile, blush, hand_on_own_chest | | 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_gloves, blue_scarf, elbow_gloves, fingerless_gloves, simple_background, sleeveless_dress, solo, upper_body, bangle, smile, white_background, bird_on_hand | | 5 | 6 |  |  |  |  |  | 1girl, bare_shoulders, blue_cape, simple_background, sleeveless_dress, solo, turtleneck_dress, bangle, looking_at_viewer, smile, elbow_gloves, fingerless_gloves, black_pantyhose | | 6 | 21 |  |  |  |  |  | 1girl, bare_shoulders, solo, jewelry, looking_at_viewer, sleeveless_dress, smile, official_alternate_costume, simple_background, turtleneck_dress, upper_body, white_background, white_dress, flower, wedding_dress, blush, bouquet, holding, open_mouth, white_gloves | | 7 | 6 |  |  |  |  |  | 1girl, bangle, bare_shoulders, black_dress, black_gloves, bridal_gauntlets, circlet, official_alternate_costume, side_slit, sleeveless_dress, solo, turtleneck_dress, smile, earrings, elbow_gloves, fur-trimmed_coat, looking_at_viewer, red_cape, cowboy_shot, full_body, red_coat, simple_background | | 8 | 9 |  |  |  |  |  | 1girl, bare_shoulders, elbow_gloves, gradient_clothes, official_alternate_costume, shiny_clothes, solo, black_gloves, short_dress, looking_at_viewer, simple_background, sleeveless_dress, torn_cape, bird, hair_bow, bangle, black_dress, grey_background, pantyhose, shiny_hair, skirt, black_ribbon, smile, thigh_boots, turtleneck | | 9 | 5 |  |  |  |  |  | 1girl, bare_shoulders, circlet, long_sleeves, red_cape, solo, official_alternate_costume, simple_background, white_background, bangle, bridal_gauntlets, detached_sleeves, full_body, open_mouth, sandals, smile, turtleneck_dress, white_dress, magic, sleeveless_dress | | 10 | 5 |  |  |  |  |  | 1boy, 1girl, blue_scarf, blush, hetero, mosaic_censoring, penis, solo_focus, bare_shoulders, cum_in_mouth, fellatio, from_side, sleeveless_dress, upper_body, brick_wall, gloves, heart, nipples, nude, pink_background, profile, simple_background, smile, tears | | 11 | 30 |  |  |  |  |  | 1girl, blush, nipples, 1boy, hetero, sex, solo_focus, open_mouth, vaginal, navel, penis, sweat, spread_legs, collarbone, pov, smile, large_breasts, looking_at_viewer, completely_nude, mosaic_censoring, cum_in_pussy, cowgirl_position, bed_sheet, birthmark, on_back | | 12 | 16 |  |  |  |  |  | 1girl, yukata, butterfly_print, official_alternate_costume, solo, blush, obi, looking_at_viewer, smile, wide_sleeves, simple_background, upper_body, holding, open_mouth, twitter_username, white_background | | 13 | 7 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, solo, cosplay, alternate_costume, blue_cape, bodystocking, covered_navel, simple_background, skin_tight, smile, bracelet, white_background, bridal_gauntlets, full_body, open_book, open_mouth | | 14 | 9 |  |  |  |  |  | 1girl, cleavage, crop_top, looking_at_viewer, midriff, navel, short_shorts, smile, tied_shirt, alternate_costume, blush, checkered_shirt, collarbone, denim_shorts, short_sleeves, solo, beer_mug, front-tie_top, holding_cup, large_breasts, blue_shorts, no_gloves, plaid, twitter_username, cowboy_shot | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | official_alternate_costume | red_bikini | 1girl | solo | bare_shoulders | cleavage | hair_flower | navel | bikini_skirt | blue_scarf | looking_at_viewer | red_gloves | collarbone | open_mouth | bird | blush | groin | :d | front-tie_bikini_top | towel | cowboy_shot | simple_background | miniskirt | outdoors | sky | fingerless_gloves | water | nipples | pussy | smile | ass_visible_through_thighs | completely_nude | white_background | bangle | belt | black_gloves | black_pantyhose | elbow_gloves | side_slit | sleeveless_dress | boots | hand_on_own_chest | upper_body | bird_on_hand | blue_cape | turtleneck_dress | jewelry | white_dress | flower | wedding_dress | bouquet | holding | white_gloves | black_dress | bridal_gauntlets | circlet | earrings | fur-trimmed_coat | red_cape | full_body | red_coat | gradient_clothes | shiny_clothes | short_dress | torn_cape | hair_bow | grey_background | pantyhose | shiny_hair | skirt | black_ribbon | thigh_boots | turtleneck | long_sleeves | detached_sleeves | sandals | magic | 1boy | hetero | mosaic_censoring | penis | solo_focus | cum_in_mouth | fellatio | from_side | brick_wall | gloves | heart | nude | pink_background | profile | tears | sex | vaginal | sweat | spread_legs | pov | large_breasts | cum_in_pussy | cowgirl_position | bed_sheet | birthmark | on_back | yukata | butterfly_print | obi | wide_sleeves | twitter_username | cosplay | alternate_costume | bodystocking | covered_navel | skin_tight | bracelet | open_book | crop_top | midriff | short_shorts | tied_shirt | checkered_shirt | denim_shorts | short_sleeves | beer_mug | front-tie_top | holding_cup | blue_shorts | no_gloves | plaid | |----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------|:-------------|:--------|:-------|:-----------------|:-----------|:--------------|:--------|:---------------|:-------------|:--------------------|:-------------|:-------------|:-------------|:-------|:--------|:--------|:-----|:-----------------------|:--------|:--------------|:--------------------|:------------|:-----------|:------|:--------------------|:--------|:----------|:--------|:--------|:-----------------------------|:------------------|:-------------------|:---------|:-------|:---------------|:------------------|:---------------|:------------|:-------------------|:--------|:--------------------|:-------------|:---------------|:------------|:-------------------|:----------|:--------------|:---------|:----------------|:----------|:----------|:---------------|:--------------|:-------------------|:----------|:-----------|:-------------------|:-----------|:------------|:-----------|:-------------------|:----------------|:--------------|:------------|:-----------|:------------------|:------------|:-------------|:--------|:---------------|:--------------|:-------------|:---------------|:-------------------|:----------|:--------|:-------|:---------|:-------------------|:--------|:-------------|:---------------|:-----------|:------------|:-------------|:---------|:--------|:-------|:------------------|:----------|:--------|:------|:----------|:--------|:--------------|:------|:----------------|:---------------|:-------------------|:------------|:------------|:----------|:---------|:------------------|:------|:---------------|:-------------------|:----------|:--------------------|:---------------|:----------------|:-------------|:-----------|:------------|:-----------|:----------|:---------------|:-------------|:------------------|:---------------|:----------------|:-----------|:----------------|:--------------|:--------------|:------------|:--------| | 0 | 30 |  |  |  |  |  | X | X | X | 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 | 6 |  |  |  |  |  | | | X | X | | | | X | | | X | | X | | | X | X | | | | | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | | | X | X | X | | | | | X | | | | | X | | | | | | | X | | | | X | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | | | X | X | X | | | | | X | X | | | | | X | | | | | X | X | | | | X | | | | X | | | X | X | X | X | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | | | X | X | X | | | | | X | | | | | | | | | | | | X | | | | X | | | | X | | | X | X | | X | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 6 |  |  |  |  |  | | | X | X | X | | | | | | X | | | | | | | | | | | X | | | | X | | | | X | | | | X | | | X | X | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 21 |  |  |  |  |  | X | | X | X | X | | | | | | X | | | X | | X | | | | | | X | | | | | | | | X | | | X | | | | | | | X | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 7 | 6 |  |  |  |  |  | X | | X | X | X | | | | | | X | | | | | | | | | | X | X | | | | | | | | X | | | | X | | X | | X | X | X | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8 | 9 |  |  |  |  |  | X | | 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 |  |  |  |  |  | X | | X | X | X | | | | | | | | | X | | | | | | | | X | | | | | | | | X | | | X | X | | | | | | X | | | | | | X | | X | | | | | | | X | X | | | X | X | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 10 | 5 |  |  |  |  |  | | | 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 | 30 |  |  |  |  |  | | | X | | | | | 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 | 16 |  |  |  |  |  | X | | X | X | | | | | | | X | | | X | | X | | | | | | X | | | | | | | | X | | | X | | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 13 | 7 |  |  |  |  |  | | | X | X | | X | | | | | X | | | X | | | | | | | | X | | | | | | | | X | | | X | | | | | | | | | | | | X | | | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 14 | 9 |  |  |  |  |  | | | X | 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/micaiah_fireemblem

2024-01-17T15:27:38+00:00

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

2024-01-17T19:29:23+00:00

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

Dataset of micaiah (Fire Emblem) ================================ This is the dataset of micaiah (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'long\_hair, yellow\_eyes, bangs, grey\_hair, ribbon, hair\_ribbon, half\_updo, breasts, white\_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

16ccf52e3e19a35948a5b62d846edef44dc57f2f

# Dataset of ninian (Fire Emblem) This is the dataset of ninian (Fire Emblem), containing 388 images and their tags. The core tags of this character are `long_hair, blue_hair, red_eyes, hair_ornament, 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 | 388 | 458.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ninian_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 388 | 280.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ninian_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 811 | 533.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ninian_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 388 | 414.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ninian_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 811 | 717.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ninian_fireemblem/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/ninian_fireemblem', 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 | 6 |  |  |  |  |  | 1girl, bare_shoulders, dress, looking_at_viewer, simple_background, smile, solo, cape, full_body, white_background | | 1 | 11 |  |  |  |  |  | 1girl, bare_shoulders, solo, looking_at_viewer, smile, cape, blue_dress, blush | | 2 | 5 |  |  |  |  |  | 1girl, bare_shoulders, dress, looking_at_viewer, solo, white_background, simple_background, smile | | 3 | 43 |  |  |  |  |  | 1girl, bare_shoulders, bride, wedding_dress, smile, solo, white_dress, bridal_veil, flower, looking_at_viewer, bouquet, gloves, strapless_dress | | 4 | 8 |  |  |  |  |  | 1girl, bangs, bare_shoulders, full_body, long_dress, solo, floating_object, medium_breasts, open_mouth, shiny_hair, gradient_clothes, stone, turtleneck, white_background, aqua_hair, blue_dress, looking_at_viewer, snowflakes, cape, dark_aura, glowing_eyes, simple_background, transparent_background | | 5 | 8 |  |  |  |  |  | 1boy, 1girl, hetero, blush, penis, sex, solo_focus, nipples, open_mouth, vaginal, completely_nude, spread_legs, cum_in_pussy, large_breasts, lying, missionary, mosaic_censoring | | 6 | 15 |  |  |  |  |  | hetero, multiple_boys, multiple_penises, 1girl, nipples, solo_focus, vaginal, large_breasts, gangbang, cum_in_pussy, mosaic_censoring, double_handjob, torn_clothes, blush, bukkake, dress, facial, fellatio, nude, rape, straddling | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | dress | looking_at_viewer | simple_background | smile | solo | cape | full_body | white_background | blue_dress | blush | bride | wedding_dress | white_dress | bridal_veil | flower | bouquet | gloves | strapless_dress | bangs | long_dress | floating_object | medium_breasts | open_mouth | shiny_hair | gradient_clothes | stone | turtleneck | aqua_hair | snowflakes | dark_aura | glowing_eyes | transparent_background | 1boy | hetero | penis | sex | solo_focus | nipples | vaginal | completely_nude | spread_legs | cum_in_pussy | large_breasts | lying | missionary | mosaic_censoring | multiple_boys | multiple_penises | gangbang | double_handjob | torn_clothes | bukkake | facial | fellatio | nude | rape | straddling | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------|:--------------------|:--------------------|:--------|:-------|:-------|:------------|:-------------------|:-------------|:--------|:--------|:----------------|:--------------|:--------------|:---------|:----------|:---------|:------------------|:--------|:-------------|:------------------|:-----------------|:-------------|:-------------|:-------------------|:--------|:-------------|:------------|:-------------|:------------|:---------------|:-------------------------|:-------|:---------|:--------|:------|:-------------|:----------|:----------|:------------------|:--------------|:---------------|:----------------|:--------|:-------------|:-------------------|:----------------|:-------------------|:-----------|:-----------------|:---------------|:----------|:---------|:-----------|:-------|:-------|:-------------| | 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 11 |  |  |  |  |  | X | X | | X | | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 43 |  |  |  |  |  | X | X | | X | | X | X | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 8 |  |  |  |  |  | X | X | | X | X | | X | X | X | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 8 |  |  |  |  |  | X | | | | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | 6 | 15 |  |  |  |  |  | X | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/ninian_fireemblem

2024-01-17T15:28:22+00:00

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

2024-01-17T16:46:48+00:00

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

Dataset of ninian (Fire Emblem) =============================== This is the dataset of ninian (Fire Emblem), containing 388 images and their tags. The core tags of this character are 'long\_hair, blue\_hair, red\_eyes, hair\_ornament, 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

6c4e43f0aca28eb96f6ab0501f37541903771d55

The dataset is available at: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/vision-and-language/visual-turing-challenge/  ``` @INPROCEEDINGS{malinowski2014nips, author = {Malinowski, Mateusz and Fritz, Mario}, title = {A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input}, booktitle = {Advances in Neural Information Processing Systems 27}, editor = {Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger}, pages = {1682--1690}, year = {2014}, publisher = {Curran Associates, Inc.}, url = {http://papers.nips.cc/paper/5411-a-multi-world-approach-to-question-answering-about-real-world-scenes-based-on-uncertain-input.pdf} } ```

Andyrasika/VQA-Dataset

2024-01-17T15:37:46+00:00

{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "pretty_name": "VQA ", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "image_id", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 700662, "num_examples": 9974}, {"name": "test", "num_bytes": 174412, "num_examples": 2494}], "download_size": 299109, "dataset_size": 875074}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "tags": ["VQA"]}

2024-01-17T15:45:12+00:00

TAGS #size_categories-100K<n<1M #language-English #license-mit #VQA #region-us

The dataset is available at: URL !image/png

4967422cc91b92359f45e002a999bcb4ff865988

# Dataset of sothis (Fire Emblem) This is the dataset of sothis (Fire Emblem), containing 433 images and their tags. The core tags of this character are `green_hair, long_hair, braid, green_eyes, twin_braids, ribbon_braid, pointy_ears, ribbon, hair_ornament, hair_ribbon, side_braid`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 433 | 548.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sothis_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 433 | 325.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sothis_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 939 | 654.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sothis_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 433 | 489.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sothis_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 939 | 900.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sothis_fireemblem/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/sothis_fireemblem', 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 |  |  |  |  |  | 1girl, closed_mouth, simple_background, solo, tiara, upper_body, smile, white_background, looking_at_viewer | | 1 | 25 |  |  |  |  |  | 1girl, dress, solo, tiara, barefoot, closed_mouth, full_body, smile, anklet, very_long_hair, simple_background, looking_at_viewer | | 2 | 7 |  |  |  |  |  | 1girl, closed_mouth, sitting, solo, tiara, dress, smile, very_long_hair, throne | | 3 | 5 |  |  |  |  |  | 1girl, christmas_ornaments, fur_trim, simple_background, smile, solo, tiara, closed_mouth, dress, full_body, white_background, very_long_hair | | 4 | 9 |  |  |  |  |  | 1girl, fur_trim, gift_box, tiara, christmas_ornaments, smile, solo, dress, closed_mouth, holding, open_mouth | | 5 | 8 |  |  |  |  |  | 1girl, bangs, cleavage, cosplay, official_alternate_costume, solo, tiara, medium_hair, clothing_cutout, hair_between_eyes, large_breasts, looking_at_viewer, blue_dress, blush, bare_shoulders, closed_mouth, upper_body | | 6 | 9 |  |  |  |  |  | 2girls, tiara, dress, simple_background, white_background, open_mouth, smile, closed_mouth | | 7 | 12 |  |  |  |  |  | halloween_costume, witch_hat, smile, 1girl, holding, striped, black_dress, black_headwear, lollipop, looking_at_viewer, official_alternate_costume, open_mouth, puffy_short_sleeves, broom, 1boy, solo | | 8 | 18 |  |  |  |  |  | 1girl, hetero, nipples, penis, solo_focus, pussy, sex, 1boy, vaginal, small_breasts, tiara, uncensored, completely_nude, cum, navel, spread_legs | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_mouth | simple_background | solo | tiara | upper_body | smile | white_background | looking_at_viewer | dress | barefoot | full_body | anklet | very_long_hair | sitting | throne | christmas_ornaments | fur_trim | gift_box | holding | open_mouth | bangs | cleavage | cosplay | official_alternate_costume | medium_hair | clothing_cutout | hair_between_eyes | large_breasts | blue_dress | blush | bare_shoulders | 2girls | halloween_costume | witch_hat | striped | black_dress | black_headwear | lollipop | puffy_short_sleeves | broom | 1boy | hetero | nipples | penis | solo_focus | pussy | sex | vaginal | small_breasts | uncensored | completely_nude | cum | navel | spread_legs | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:--------|:-------------|:--------|:-------------------|:--------------------|:--------|:-----------|:------------|:---------|:-----------------|:----------|:---------|:----------------------|:-----------|:-----------|:----------|:-------------|:--------|:-----------|:----------|:-----------------------------|:--------------|:------------------|:--------------------|:----------------|:-------------|:--------|:-----------------|:---------|:--------------------|:------------|:----------|:--------------|:-----------------|:-----------|:----------------------|:--------|:-------|:---------|:----------|:--------|:-------------|:--------|:------|:----------|:----------------|:-------------|:------------------|:------|:--------|:--------------| | 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 25 |  |  |  |  |  | X | X | X | X | X | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | X | | X | X | | X | | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 5 |  |  |  |  |  | X | X | X | X | X | | X | X | | X | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 9 |  |  |  |  |  | X | X | | X | X | | X | | | X | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 8 |  |  |  |  |  | X | X | | X | X | X | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 9 |  |  |  |  |  | | X | X | | X | | X | X | | X | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | 7 | 12 |  |  |  |  |  | X | | | X | | | X | | X | | | | | | | | | | | X | X | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 8 | 18 |  |  |  |  |  | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/sothis_fireemblem

2024-01-17T16:03:46+00:00

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

2024-01-17T17:37:09+00:00

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

Dataset of sothis (Fire Emblem) =============================== This is the dataset of sothis (Fire Emblem), containing 433 images and their tags. The core tags of this character are 'green\_hair, long\_hair, braid, green\_eyes, twin\_braids, ribbon\_braid, pointy\_ears, ribbon, hair\_ornament, hair\_ribbon, side\_braid', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

bbaae6932323ff00d0245588fc080cccc0e9f6bd

# Dataset of liz (Fire Emblem) This is the dataset of liz (Fire Emblem), containing 321 images and their tags. The core tags of this character are `blonde_hair, twintails, blue_eyes, 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 | 321 | 354.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liz_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 321 | 225.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liz_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 668 | 448.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liz_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 321 | 323.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liz_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 668 | 604.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/liz_fireemblem/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/liz_fireemblem', 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 |  |  |  |  |  | 1girl, dress, smile, solo, corset, looking_at_viewer, open_mouth, simple_background, white_background, apron, long_hair, long_sleeves, puffy_sleeves | | 1 | 10 |  |  |  |  |  | 1girl, dress, solo, staff, smile, apron, boots, holding, full_body, corset, white_background | | 2 | 7 |  |  |  |  |  | 1girl, open_mouth, smile, solo, dress, hair_flower, holding, upper_body, looking_at_viewer, simple_background, white_background, basket, official_alternate_costume | | 3 | 9 |  |  |  |  |  | 1girl, christmas, dress, santa_hat, smile, solo, santa_costume, bell, looking_at_viewer, open_mouth, holding | | 4 | 5 |  |  |  |  |  | 1girl, blush, completely_nude, looking_at_viewer, navel, nipples, arms_behind_back, collarbone, parted_bangs, pussy, smile, solo, closed_mouth, medium_breasts, simple_background, white_background, barefoot, censored, full_body, grey_eyes, groin, long_hair, small_breasts, standing | | 5 | 29 |  |  |  |  |  | blush, hetero, 1boy, 1girl, nipples, sex, penis, solo_focus, medium_breasts, vaginal, open_mouth, spread_legs, sweat, looking_at_viewer, completely_nude, cum_in_pussy, mosaic_censoring, navel | | 6 | 6 |  |  |  |  |  | 1girl, ahoge, circlet, grey_eyes, medium_breasts, bodystocking, cape, long_hair, turtleneck, 1boy, bangs, bridal_gauntlets, covered_navel, holding, panties, smile, thighhighs | | 7 | 5 |  |  |  |  |  | 1girl, arms_behind_back, blush, looking_at_viewer, medium_breasts, shibari, solo, underwear_only, crying_with_eyes_open, gagged, green_eyes, open_mouth, white_panties, crotch_rope, frills, full_body, kneeling, navel, short_hair, white_bra, white_thighhighs, yellow_panties | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | dress | smile | solo | corset | looking_at_viewer | open_mouth | simple_background | white_background | apron | long_hair | long_sleeves | puffy_sleeves | staff | boots | holding | full_body | hair_flower | upper_body | basket | official_alternate_costume | christmas | santa_hat | santa_costume | bell | blush | completely_nude | navel | nipples | arms_behind_back | collarbone | parted_bangs | pussy | closed_mouth | medium_breasts | barefoot | censored | grey_eyes | groin | small_breasts | standing | hetero | 1boy | sex | penis | solo_focus | vaginal | spread_legs | sweat | cum_in_pussy | mosaic_censoring | ahoge | circlet | bodystocking | cape | turtleneck | bangs | bridal_gauntlets | covered_navel | panties | thighhighs | shibari | underwear_only | crying_with_eyes_open | gagged | green_eyes | white_panties | crotch_rope | frills | kneeling | short_hair | white_bra | white_thighhighs | yellow_panties | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------|:-------|:---------|:--------------------|:-------------|:--------------------|:-------------------|:--------|:------------|:---------------|:----------------|:--------|:--------|:----------|:------------|:--------------|:-------------|:---------|:-----------------------------|:------------|:------------|:----------------|:-------|:--------|:------------------|:--------|:----------|:-------------------|:-------------|:---------------|:--------|:---------------|:-----------------|:-----------|:-----------|:------------|:--------|:----------------|:-----------|:---------|:-------|:------|:--------|:-------------|:----------|:--------------|:--------|:---------------|:-------------------|:--------|:----------|:---------------|:-------|:-------------|:--------|:-------------------|:----------------|:----------|:-------------|:----------|:-----------------|:------------------------|:---------|:-------------|:----------------|:--------------|:---------|:-----------|:-------------|:------------|:-------------------|:-----------------| | 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 10 |  |  |  |  |  | X | X | X | X | X | | | | X | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 7 |  |  |  |  |  | X | X | X | X | | X | X | X | X | | | | | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 9 |  |  |  |  |  | X | X | X | X | | X | X | | | | | | | | | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 5 |  |  |  |  |  | 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 | 29 |  |  |  |  |  | X | | | | | X | X | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 |  |  |  |  |  | X | | X | | | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | X | | | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 7 | 5 |  |  |  |  |  | X | | | X | | X | X | | | | | | | | | | X | | | | | | | | | X | | X | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |

CyberHarem/liz_fireemblem

2024-01-17T16:03:51+00:00

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

2024-01-17T17:21:00+00:00

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

Dataset of liz (Fire Emblem) ============================ This is the dataset of liz (Fire Emblem), containing 321 images and their tags. The core tags of this character are 'blonde\_hair, twintails, blue\_eyes, 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

23edfecef664d265e49aa44edd847b8604264a0d

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

tmnam20/Vietnamese-Books-dedup

2024-01-17T16:19:00+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3546619845, "num_examples": 14485736}], "download_size": 1922215933, "dataset_size": 3546619845}}

2024-01-17T16:23:27+00:00

TAGS #region-us

# Dataset Card for "Vietnamese-Books-dedup" More Information needed

277164dc7b4b4676062adcecd3be90af8ea4a950

# Dataset of setsuna (Fire Emblem) This is the dataset of setsuna (Fire Emblem), containing 71 images and their tags. The core tags of this character are `hair_over_one_eye, short_hair, blue_hair, blue_eyes, hairband`, 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 | 71 | 51.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/setsuna_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 71 | 36.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/setsuna_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 110 | 58.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/setsuna_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 71 | 47.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/setsuna_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 110 | 77.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/setsuna_fireemblem/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/setsuna_fireemblem', 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 | 6 |  |  |  |  |  | 1girl, arrow_(projectile), gloves, solo, quiver, simple_background, holding_bow_(weapon), white_background | | 1 | 6 |  |  |  |  |  | 1girl, simple_background, solo, upper_body, white_background | | 2 | 5 |  |  |  |  |  | 1girl, fingerless_gloves, solo, upper_body, looking_at_viewer | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | arrow_(projectile) | gloves | solo | quiver | simple_background | holding_bow_(weapon) | white_background | upper_body | fingerless_gloves | looking_at_viewer | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:---------|:-------|:---------|:--------------------|:-----------------------|:-------------------|:-------------|:--------------------|:--------------------| | 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | 1 | 6 |  |  |  |  |  | X | | | X | | X | | X | X | | | | 2 | 5 |  |  |  |  |  | X | | | X | | | | | X | X | X |

CyberHarem/setsuna_fireemblem

2024-01-17T16:34:37+00:00

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

2024-01-17T16:46:20+00:00

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

Dataset of setsuna (Fire Emblem) ================================ This is the dataset of setsuna (Fire Emblem), containing 71 images and their tags. The core tags of this character are 'hair\_over\_one\_eye, short\_hair, blue\_hair, blue\_eyes, hairband', 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

ba1996e296c790d4b9ae7c3dff0eaae60d26f903

# Dataset of nino (Fire Emblem) This is the dataset of nino (Fire Emblem), containing 342 images and their tags. The core tags of this character are `green_hair, short_hair, hairband, blue_eyes, purple_hairband`, 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 | 342 | 373.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nino_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 342 | 225.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nino_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 672 | 429.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nino_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 342 | 335.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nino_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 672 | 589.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nino_fireemblem/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/nino_fireemblem', 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 | 38 |  |  |  |  |  | 1girl, hetero, 1boy, blush, sex, open_mouth, solo_focus, penis, vaginal, nipples, mosaic_censoring, nude, cum_in_pussy, medium_breasts, sweat | | 1 | 12 |  |  |  |  |  | 1girl, nipples, solo, blush, looking_at_viewer, small_breasts, completely_nude, navel, open_mouth, simple_background | | 2 | 11 |  |  |  |  |  | 1girl, cape, solo, looking_at_viewer, simple_background, upper_body, open_mouth, white_background, long_sleeves, smile, blush | | 3 | 10 |  |  |  |  |  | 1girl, cape, open_mouth, solo, skirt, belt, looking_at_viewer, :d, boots | | 4 | 17 |  |  |  |  |  | 1girl, cape, solo, holding_book, long_sleeves, belt, open_mouth, smile, white_skirt, looking_at_viewer, simple_background, blush, boots | | 5 | 5 |  |  |  |  |  | 1girl, sitting, skirt, smile, solo, blush, boots, cape, looking_at_viewer, long_sleeves, pouch | | 6 | 6 |  |  |  |  |  | 1girl, bangs, black_dress, hood_down, long_sleeves, solo, belt_pouch, feather_trim, open_mouth, shiny_hair, short_dress, simple_background, blush, looking_at_viewer, :d, boots, full_body, hooded_cape, leg_up, white_background | | 7 | 6 |  |  |  |  |  | 1boy, 1girl, cape, couple, hetero, hug, red_hair, smile, blush, bandages, closed_eyes, gloves, white_background | | 8 | 13 |  |  |  |  |  | 1girl, fur_trim, long_sleeves, smile, open_mouth, santa_costume, santa_hat, solo, belt, red_dress, boots, gift_box, holding, looking_at_viewer, pouch, red_headwear, sack, white_background, blush, brown_gloves, christmas_ornaments, christmas_tree, pom_pom_(clothes), red_footwear, bangs, bell, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | 1boy | blush | sex | open_mouth | solo_focus | penis | vaginal | nipples | mosaic_censoring | nude | cum_in_pussy | medium_breasts | sweat | solo | looking_at_viewer | small_breasts | completely_nude | navel | simple_background | cape | upper_body | white_background | long_sleeves | smile | skirt | belt | :d | boots | holding_book | white_skirt | sitting | pouch | bangs | black_dress | hood_down | belt_pouch | feather_trim | shiny_hair | short_dress | full_body | hooded_cape | leg_up | couple | hug | red_hair | bandages | closed_eyes | gloves | fur_trim | santa_costume | santa_hat | red_dress | gift_box | holding | red_headwear | sack | brown_gloves | christmas_ornaments | christmas_tree | pom_pom_(clothes) | red_footwear | bell | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------|:--------|:------|:-------------|:-------------|:--------|:----------|:----------|:-------------------|:-------|:---------------|:-----------------|:--------|:-------|:--------------------|:----------------|:------------------|:--------|:--------------------|:-------|:-------------|:-------------------|:---------------|:--------|:--------|:-------|:-----|:--------|:---------------|:--------------|:----------|:--------|:--------|:--------------|:------------|:-------------|:---------------|:-------------|:--------------|:------------|:--------------|:---------|:---------|:------|:-----------|:-----------|:--------------|:---------|:-----------|:----------------|:------------|:------------|:-----------|:----------|:---------------|:-------|:---------------|:----------------------|:-----------------|:--------------------|:---------------|:-------| | 0 | 38 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 12 |  |  |  |  |  | X | | | X | | X | | | | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 11 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | X | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 10 |  |  |  |  |  | X | | | | | X | | | | | | | | | | X | X | | | | | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 4 | 17 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | X | X | | | | X | X | | | X | X | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 5 | 5 |  |  |  |  |  | X | | | X | | | | | | | | | | | | X | X | | | | | X | | | X | X | X | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 6 | 6 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | X | X | | | | X | | | X | X | | | | X | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | 7 | 6 |  |  |  |  |  | X | X | X | X | | | | | | | | | | | | | | | | | | X | | X | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | 8 | 13 |  |  |  |  |  | X | | | X | | X | | | | | | | | | | 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/nino_fireemblem

2024-01-17T16:35:16+00:00

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

2024-01-17T17:40:15+00:00

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

Dataset of nino (Fire Emblem) ============================= This is the dataset of nino (Fire Emblem), containing 342 images and their tags. The core tags of this character are 'green\_hair, short\_hair, hairband, blue\_eyes, purple\_hairband', 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

4dbd10f56c4e3e318ae504ef9973f7d24345939d

# Dataset Card for "sample_105000_rows" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kraitans21/sample_105000_rows

2024-01-17T16:43:35+00:00

{"dataset_info": {"features": [{"name": "source_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "updated_date", "dtype": "string"}, {"name": "created_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 560214821.7, "num_examples": 100000}, {"name": "eval", "num_bytes": 28010741.085, "num_examples": 5000}], "download_size": 241109440, "dataset_size": 588225562.7850001}}

2024-01-17T16:45:44+00:00

TAGS #region-us

# Dataset Card for "sample_105000_rows" More Information needed

edb870c4babc8a5819d5859aca9f52c40669b67a

# Dataset Card for Evaluation run of kaitchup/Maixtchup-4x7b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [kaitchup/Maixtchup-4x7b](https://huggingface.co/kaitchup/Maixtchup-4x7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_kaitchup__Maixtchup-4x7b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-01-17T16:47:01.392242](https://huggingface.co/datasets/open-llm-leaderboard/details_kaitchup__Maixtchup-4x7b/blob/main/results_2024-01-17T16-47-01.392242.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": 0.6144719599933052, "acc_stderr": 0.03303924482918558, "acc_norm": 0.6168692677516201, "acc_norm_stderr": 0.03370135211774917, "mc1": 0.4039167686658507, "mc1_stderr": 0.017177276822584284, "mc2": 0.5612826178367374, "mc2_stderr": 0.015986434965174608 }, "harness|arc:challenge|25": { "acc": 0.590443686006826, "acc_stderr": 0.014370358632472439, "acc_norm": 0.6254266211604096, "acc_norm_stderr": 0.014144193471893454 }, "harness|hellaswag|10": { "acc": 0.6525592511451902, "acc_stderr": 0.004751840646730854, "acc_norm": 0.8382792272455686, "acc_norm_stderr": 0.003674419799353668 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.6074074074074074, "acc_stderr": 0.04218506215368879, "acc_norm": 0.6074074074074074, "acc_norm_stderr": 0.04218506215368879 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6776315789473685, "acc_stderr": 0.03803510248351585, "acc_norm": 0.6776315789473685, "acc_norm_stderr": 0.03803510248351585 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.56, "acc_stderr": 0.04988876515698589, "acc_norm": 0.56, "acc_norm_stderr": 0.04988876515698589 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6679245283018868, "acc_stderr": 0.028985455652334395, "acc_norm": 0.6679245283018868, "acc_norm_stderr": 0.028985455652334395 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7361111111111112, "acc_stderr": 0.03685651095897532, "acc_norm": 0.7361111111111112, "acc_norm_stderr": 0.03685651095897532 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.55, "acc_stderr": 0.05, "acc_norm": 0.55, "acc_norm_stderr": 0.05 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6127167630057804, "acc_stderr": 0.03714325906302065, "acc_norm": 0.6127167630057804, "acc_norm_stderr": 0.03714325906302065 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.3627450980392157, "acc_stderr": 0.047840607041056527, "acc_norm": 0.3627450980392157, "acc_norm_stderr": 0.047840607041056527 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.76, "acc_stderr": 0.04292346959909284, "acc_norm": 0.76, "acc_norm_stderr": 0.04292346959909284 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5234042553191489, "acc_stderr": 0.03265019475033582, "acc_norm": 0.5234042553191489, "acc_norm_stderr": 0.03265019475033582 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.046854730419077895, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.046854730419077895 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5310344827586206, "acc_stderr": 0.04158632762097828, "acc_norm": 0.5310344827586206, "acc_norm_stderr": 0.04158632762097828 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3968253968253968, "acc_stderr": 0.025197101074246483, "acc_norm": 0.3968253968253968, "acc_norm_stderr": 0.025197101074246483 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949098, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949098 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6064516129032258, "acc_stderr": 0.027791878753132274, "acc_norm": 0.6064516129032258, "acc_norm_stderr": 0.027791878753132274 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4975369458128079, "acc_stderr": 0.03517945038691063, "acc_norm": 0.4975369458128079, "acc_norm_stderr": 0.03517945038691063 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.66, "acc_stderr": 0.04760952285695237, "acc_norm": 0.66, "acc_norm_stderr": 0.04760952285695237 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7636363636363637, "acc_stderr": 0.03317505930009181, "acc_norm": 0.7636363636363637, "acc_norm_stderr": 0.03317505930009181 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7777777777777778, "acc_stderr": 0.02962022787479049, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.02962022787479049 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.844559585492228, "acc_stderr": 0.02614848346915332, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.02614848346915332 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5871794871794872, "acc_stderr": 0.024962683564331796, "acc_norm": 0.5871794871794872, "acc_norm_stderr": 0.024962683564331796 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34074074074074073, "acc_stderr": 0.028897748741131143, "acc_norm": 0.34074074074074073, "acc_norm_stderr": 0.028897748741131143 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6218487394957983, "acc_stderr": 0.03149930577784906, "acc_norm": 0.6218487394957983, "acc_norm_stderr": 0.03149930577784906 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.36423841059602646, "acc_stderr": 0.03929111781242742, "acc_norm": 0.36423841059602646, "acc_norm_stderr": 0.03929111781242742 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8, "acc_stderr": 0.01714985851425095, "acc_norm": 0.8, "acc_norm_stderr": 0.01714985851425095 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.47685185185185186, "acc_stderr": 0.034063153607115065, "acc_norm": 0.47685185185185186, "acc_norm_stderr": 0.034063153607115065 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145635, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145635 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7848101265822784, "acc_stderr": 0.026750826994676166, "acc_norm": 0.7848101265822784, "acc_norm_stderr": 0.026750826994676166 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6412556053811659, "acc_stderr": 0.032190792004199956, "acc_norm": 0.6412556053811659, "acc_norm_stderr": 0.032190792004199956 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.732824427480916, "acc_stderr": 0.03880848301082393, "acc_norm": 0.732824427480916, "acc_norm_stderr": 0.03880848301082393 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8099173553719008, "acc_stderr": 0.03581796951709282, "acc_norm": 0.8099173553719008, "acc_norm_stderr": 0.03581796951709282 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7222222222222222, "acc_stderr": 0.04330043749650743, "acc_norm": 0.7222222222222222, "acc_norm_stderr": 0.04330043749650743 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7116564417177914, "acc_stderr": 0.035590395316173425, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.035590395316173425 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8504273504273504, "acc_stderr": 0.023365051491753715, "acc_norm": 0.8504273504273504, "acc_norm_stderr": 0.023365051491753715 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.71, "acc_stderr": 0.04560480215720684, "acc_norm": 0.71, "acc_norm_stderr": 0.04560480215720684 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.80970625798212, "acc_stderr": 0.014036945850381396, "acc_norm": 0.80970625798212, "acc_norm_stderr": 0.014036945850381396 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.7052023121387283, "acc_stderr": 0.024547617794803828, "acc_norm": 0.7052023121387283, "acc_norm_stderr": 0.024547617794803828 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.3776536312849162, "acc_stderr": 0.01621414875213663, "acc_norm": 0.3776536312849162, "acc_norm_stderr": 0.01621414875213663 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6928104575163399, "acc_stderr": 0.026415601914388992, "acc_norm": 0.6928104575163399, "acc_norm_stderr": 0.026415601914388992 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.707395498392283, "acc_stderr": 0.025839898334877983, "acc_norm": 0.707395498392283, "acc_norm_stderr": 0.025839898334877983 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.691358024691358, "acc_stderr": 0.025702640260603742, "acc_norm": 0.691358024691358, "acc_norm_stderr": 0.025702640260603742 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.46808510638297873, "acc_stderr": 0.029766675075873862, "acc_norm": 0.46808510638297873, "acc_norm_stderr": 0.029766675075873862 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.44328552803129073, "acc_stderr": 0.012687818419599924, "acc_norm": 0.44328552803129073, "acc_norm_stderr": 0.012687818419599924 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6507352941176471, "acc_stderr": 0.02895975519682487, "acc_norm": 0.6507352941176471, "acc_norm_stderr": 0.02895975519682487 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6650326797385621, "acc_stderr": 0.019094228167000325, "acc_norm": 0.6650326797385621, "acc_norm_stderr": 0.019094228167000325 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6363636363636364, "acc_stderr": 0.046075820907199756, "acc_norm": 0.6363636363636364, "acc_norm_stderr": 0.046075820907199756 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7346938775510204, "acc_stderr": 0.0282638899437846, "acc_norm": 0.7346938775510204, "acc_norm_stderr": 0.0282638899437846 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5572139303482587, "acc_stderr": 0.03512310964123937, "acc_norm": 0.5572139303482587, "acc_norm_stderr": 0.03512310964123937 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.82, "acc_stderr": 0.03861229196653694, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653694 }, "harness|hendrycksTest-virology|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8538011695906432, "acc_stderr": 0.027097290118070806, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.027097290118070806 }, "harness|truthfulqa:mc|0": { "mc1": 0.4039167686658507, "mc1_stderr": 0.017177276822584284, "mc2": 0.5612826178367374, "mc2_stderr": 0.015986434965174608 }, "harness|winogrande|5": { "acc": 0.7600631412786109, "acc_stderr": 0.01200207862948574 }, "harness|gsm8k|5": { "acc": 0.5481425322213799, "acc_stderr": 0.013708494995677651 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

open-llm-leaderboard/details_kaitchup__Maixtchup-4x7b

2024-01-17T16:49:16+00:00

{"pretty_name": "Evaluation run of kaitchup/Maixtchup-4x7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [kaitchup/Maixtchup-4x7b](https://huggingface.co/kaitchup/Maixtchup-4x7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_kaitchup__Maixtchup-4x7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-01-17T16:47:01.392242](https://huggingface.co/datasets/open-llm-leaderboard/details_kaitchup__Maixtchup-4x7b/blob/main/results_2024-01-17T16-47-01.392242.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\": 0.6144719599933052,\n \"acc_stderr\": 0.03303924482918558,\n \"acc_norm\": 0.6168692677516201,\n \"acc_norm_stderr\": 0.03370135211774917,\n \"mc1\": 0.4039167686658507,\n \"mc1_stderr\": 0.017177276822584284,\n \"mc2\": 0.5612826178367374,\n \"mc2_stderr\": 0.015986434965174608\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.590443686006826,\n \"acc_stderr\": 0.014370358632472439,\n \"acc_norm\": 0.6254266211604096,\n \"acc_norm_stderr\": 0.014144193471893454\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6525592511451902,\n \"acc_stderr\": 0.004751840646730854,\n \"acc_norm\": 0.8382792272455686,\n \"acc_norm_stderr\": 0.003674419799353668\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.028985455652334395,\n \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.028985455652334395\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.047840607041056527,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.047840607041056527\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.03265019475033582,\n \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.03265019475033582\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6064516129032258,\n \"acc_stderr\": 0.027791878753132274,\n \"acc_norm\": 0.6064516129032258,\n \"acc_norm_stderr\": 0.027791878753132274\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479049,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.02614848346915332,\n \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.02614848346915332\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5871794871794872,\n \"acc_stderr\": 0.024962683564331796,\n \"acc_norm\": 0.5871794871794872,\n \"acc_norm_stderr\": 0.024962683564331796\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131143,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131143\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.03149930577784906,\n \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.03149930577784906\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.01714985851425095,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.01714985851425095\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.47685185185185186,\n \"acc_stderr\": 0.034063153607115065,\n \"acc_norm\": 0.47685185185185186,\n \"acc_norm_stderr\": 0.034063153607115065\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145635,\n \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145635\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676166,\n \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676166\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n \"acc_stderr\": 0.032190792004199956,\n \"acc_norm\": 0.6412556053811659,\n \"acc_norm_stderr\": 0.032190792004199956\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082393,\n \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082393\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.04330043749650743,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.04330043749650743\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.80970625798212,\n \"acc_stderr\": 0.014036945850381396,\n \"acc_norm\": 0.80970625798212,\n \"acc_norm_stderr\": 0.014036945850381396\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7052023121387283,\n \"acc_stderr\": 0.024547617794803828,\n \"acc_norm\": 0.7052023121387283,\n \"acc_norm_stderr\": 0.024547617794803828\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3776536312849162,\n \"acc_stderr\": 0.01621414875213663,\n \"acc_norm\": 0.3776536312849162,\n \"acc_norm_stderr\": 0.01621414875213663\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6928104575163399,\n \"acc_stderr\": 0.026415601914388992,\n \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.026415601914388992\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n \"acc_stderr\": 0.025839898334877983,\n \"acc_norm\": 0.707395498392283,\n \"acc_norm_stderr\": 0.025839898334877983\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.691358024691358,\n \"acc_stderr\": 0.025702640260603742,\n \"acc_norm\": 0.691358024691358,\n \"acc_norm_stderr\": 0.025702640260603742\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873862,\n \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873862\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44328552803129073,\n \"acc_stderr\": 0.012687818419599924,\n \"acc_norm\": 0.44328552803129073,\n \"acc_norm_stderr\": 0.012687818419599924\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.02895975519682487,\n \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.02895975519682487\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000325,\n \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000325\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5572139303482587,\n \"acc_stderr\": 0.03512310964123937,\n \"acc_norm\": 0.5572139303482587,\n \"acc_norm_stderr\": 0.03512310964123937\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8538011695906432,\n \"acc_stderr\": 0.027097290118070806,\n \"acc_norm\": 0.8538011695906432,\n \"acc_norm_stderr\": 0.027097290118070806\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4039167686658507,\n \"mc1_stderr\": 0.017177276822584284,\n \"mc2\": 0.5612826178367374,\n \"mc2_stderr\": 0.015986434965174608\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7600631412786109,\n \"acc_stderr\": 0.01200207862948574\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5481425322213799,\n \"acc_stderr\": 0.013708494995677651\n }\n}\n```", "repo_url": "https://huggingface.co/kaitchup/Maixtchup-4x7b", "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": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|arc:challenge|25_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|gsm8k|5_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hellaswag|10_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-17T16-47-01.392242.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-17T16-47-01.392242.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T16-47-01.392242.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-01-17T16-47-01.392242.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_01_17T16_47_01.392242", "path": 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2024-01-17T16:49:39+00:00

TAGS #region-us

# Dataset Card for Evaluation run of kaitchup/Maixtchup-4x7b Dataset automatically created during the evaluation run of model kaitchup/Maixtchup-4x7b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-01-17T16:47:01.392242(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

a24f6e351a03d4c2cf01c83e752d1d5c8eb0676a

# Dataset of ophelia (Fire Emblem) This is the dataset of ophelia (Fire Emblem), containing 500 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, ahoge, grey_eyes, bangs, medium_breasts`, which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)). ## List of Packages | Name | Images | Size | Download | Type | Description | |:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------| | raw | 500 | 623.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 500 | 344.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 1160 | 730.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 500 | 543.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 1160 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/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/ophelia_fireemblem', 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 | 17 |  |  |  |  |  | 1girl, cape, circlet, solo, closed_mouth, smile, upper_body, looking_at_viewer, cleavage, bodystocking, covered_navel, blue_eyes, turtleneck, center_opening | | 1 | 15 |  |  |  |  |  | 1girl, cape, circlet, looking_at_viewer, solo, turtleneck, bodystocking, upper_body, covered_navel, open_mouth, one_eye_closed, asymmetrical_bangs, bridal_gauntlets, cleavage, smile, blue_eyes | | 2 | 8 |  |  |  |  |  | 1girl, circlet, looking_at_viewer, smile, solo, asymmetrical_bangs, official_alternate_costume, upper_body, closed_mouth, turtleneck, bodystocking, cleavage | | 3 | 9 |  |  |  |  |  | 1girl, completely_nude, looking_at_viewer, nipples, solo, smile, closed_mouth, navel, pussy, blush, large_breasts, barefoot, blue_eyes, circlet | | 4 | 14 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, open_mouth, penis, sex, blush, vaginal, circlet, completely_nude, cum_in_pussy, uncensored, large_breasts, navel, spread_legs | | 5 | 10 |  |  |  |  |  | 1boy, 1girl, circlet, hetero, penis, solo_focus, large_breasts, looking_at_viewer, nipples, blush, paizuri, censored, pov, cum_on_breasts, smile, upper_body | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cape | circlet | solo | closed_mouth | smile | upper_body | looking_at_viewer | cleavage | bodystocking | covered_navel | blue_eyes | turtleneck | center_opening | open_mouth | one_eye_closed | asymmetrical_bangs | bridal_gauntlets | official_alternate_costume | completely_nude | nipples | navel | pussy | blush | large_breasts | barefoot | 1boy | hetero | penis | sex | vaginal | cum_in_pussy | uncensored | spread_legs | solo_focus | paizuri | censored | pov | cum_on_breasts | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:-------|:---------------|:--------|:-------------|:--------------------|:-----------|:---------------|:----------------|:------------|:-------------|:-----------------|:-------------|:-----------------|:---------------------|:-------------------|:-----------------------------|:------------------|:----------|:--------|:--------|:--------|:----------------|:-----------|:-------|:---------|:--------|:------|:----------|:---------------|:-------------|:--------------|:-------------|:----------|:-----------|:------|:-----------------| | 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 15 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | 2 | 8 |  |  |  |  |  | X | | X | X | X | X | X | X | X | X | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | 3 | 9 |  |  |  |  |  | X | | X | X | X | X | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | 4 | 14 |  |  |  |  |  | X | | X | | | | | | | | | | | | X | | | | | X | X | X | | X | X | | X | X | X | X | X | X | X | X | | | | | | | 5 | 10 |  |  |  |  |  | X | | X | | | X | X | X | | | | | | | | | | | | | X | | | X | X | | X | X | X | | | | | | X | X | X | X | X |

CyberHarem/ophelia_fireemblem

2024-01-17T16:51:13+00:00

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

2024-01-17T18:41:57+00:00

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

Dataset of ophelia (Fire Emblem) ================================ This is the dataset of ophelia (Fire Emblem), containing 500 images and their tags. The core tags of this character are 'blonde\_hair, long\_hair, breasts, ahoge, grey\_eyes, bangs, medium\_breasts', which are pruned in this dataset. Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(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

c845e054745df6b928c4a6d2e263f622f509cd19

# Dataset of charlotte (Fire Emblem) This is the dataset of charlotte (Fire Emblem), containing 285 images and their tags. The core tags of this character are `blonde_hair, long_hair, breasts, bow, blue_eyes, hair_bow, large_breasts, bangs, white_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 | 285 | 304.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 285 | 183.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 665 | 377.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 285 | 275.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 665 | 516.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/charlotte_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | ### Load Raw Dataset with Waifuc We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code ```python import os import zipfile from huggingface_hub import hf_hub_download from waifuc.source import LocalSource # download raw archive file zip_file = hf_hub_download( repo_id='CyberHarem/charlotte_fireemblem', 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 | 26 |  |  |  |  |  | 1girl, hetero, solo_focus, 1boy, penis, pussy, nipples, blush, uncensored, vaginal, nude, open_mouth, navel, sex_from_behind, spread_legs, testicles | | 1 | 18 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, penis, nipples, paizuri, blush, nude, cum_on_breasts, smile, facial, uncensored | | 2 | 20 |  |  |  |  |  | 1girl, cleavage, solo, smile, navel, midriff, looking_at_viewer, shoulder_armor, bikini_armor, white_background, simple_background, spikes | | 3 | 11 |  |  |  |  |  | 1girl, solo, huge_breasts, looking_at_viewer, smile, thick_thighs, blush, blunt_bangs, short_shorts, ass, cleavage, simple_background, thighhighs, blue_background, denim_shorts, from_behind, looking_back | | 4 | 10 |  |  |  |  |  | 1girl, huge_penis, solo, uncensored, blush, erection, nipples, nude, open_mouth, testicles, navel, heart, animal_penis, blunt_bangs, ejaculation, futanari_masturbation, rolling_eyes, spread_legs, thighhighs, tongue, veiny_penis | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hetero | solo_focus | 1boy | penis | pussy | nipples | blush | uncensored | vaginal | nude | open_mouth | navel | sex_from_behind | spread_legs | testicles | paizuri | cum_on_breasts | smile | facial | cleavage | solo | midriff | looking_at_viewer | shoulder_armor | bikini_armor | white_background | simple_background | spikes | huge_breasts | thick_thighs | blunt_bangs | short_shorts | ass | thighhighs | blue_background | denim_shorts | from_behind | looking_back | huge_penis | erection | heart | animal_penis | ejaculation | futanari_masturbation | rolling_eyes | tongue | veiny_penis | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-------------|:-------|:--------|:--------|:----------|:--------|:-------------|:----------|:-------|:-------------|:--------|:------------------|:--------------|:------------|:----------|:-----------------|:--------|:---------|:-----------|:-------|:----------|:--------------------|:-----------------|:---------------|:-------------------|:--------------------|:---------|:---------------|:---------------|:--------------|:---------------|:------|:-------------|:------------------|:---------------|:--------------|:---------------|:-------------|:-----------|:--------|:---------------|:--------------|:------------------------|:---------------|:---------|:--------------| | 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1 | 18 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 20 |  |  |  |  |  | X | | | | | | | | | | | | X | | | | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | 3 | 11 |  |  |  |  |  | X | | | | | | | X | | | | | | | | | | | X | | X | X | | X | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | 4 | 10 |  |  |  |  |  | X | | | | | | X | X | X | | X | X | X | | X | X | | | | | | X | | | | | | | | | | X | | | X | | | | | X | X | X | X | X | X | X | X | X |

CyberHarem/charlotte_fireemblem

2024-01-17T16:51:13+00:00

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

2024-01-17T17:45:07+00:00

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

Dataset of charlotte (Fire Emblem) ================================== This is the dataset of charlotte (Fire Emblem), containing 285 images and their tags. The core tags of this character are 'blonde\_hair, long\_hair, breasts, bow, blue\_eyes, hair\_bow, large\_breasts, bangs, white\_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

dd909a5d2ec2c133b7cce62ef0c48d37693b6de0

# Dataset of serge (Fire Emblem) This is the dataset of serge (Fire Emblem), containing 151 images and their tags. The core tags of this character are `long_hair, breasts, red_hair, red_eyes, hairband, large_breasts, pink_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 | 151 | 164.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serge_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). | | 800 | 151 | 100.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serge_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. | | stage3-p480-800 | 325 | 192.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serge_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. | | 1200 | 151 | 147.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serge_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. | | stage3-p480-1200 | 325 | 259.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/serge_fireemblem/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/serge_fireemblem', repo_type='dataset', filename='dataset-raw.zip', ) # extract files to your directory dataset_dir = 'dataset_dir' os.makedirs(dataset_dir, exist_ok=True) with zipfile.ZipFile(zip_file, 'r') as zf: zf.extractall(dataset_dir) # load the dataset with waifuc source = LocalSource(dataset_dir) for item in source: print(item.image, item.meta['filename'], item.meta['tags']) ``` ## List of Clusters List of tag clustering result, maybe some outfits can be mined here. ### Raw Text Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 0 | 12 |  |  |  |  |  | 1girl, ninja, solo, bangs, fingerless_gloves, looking_at_viewer, simple_background, cleavage, medium_breasts, obi, pink_eyes, smile, white_background, white_scarf, black_gloves, holding, arm_guards, elbow_gloves, black_thighhighs, closed_mouth, official_alternate_costume, sheathed, short_sword, sleeveless_kimono, very_long_hair | | 1 | 17 |  |  |  |  |  | 1girl, smile, armor, solo, looking_at_viewer, simple_background, gloves, closed_mouth, upper_body, white_background | | 2 | 12 |  |  |  |  |  | 1girl, from_behind, solo, looking_back, back_cutout, gloves, looking_at_viewer, smile, axe, holding_weapon, backless_dress, boots, shoulder_armor, simple_background, thighhighs | | 3 | 6 |  |  |  |  |  | 1boy, 1girl, hetero, navel, nipples, blush, penis, sex, solo_focus, uncensored, clitoris, smile, sweat, vaginal, artist_name, lying, pussy_juice, spread_legs | | 4 | 8 |  |  |  |  |  | 1boy, 1girl, hetero, penis, solo_focus, uncensored, blush, looking_at_viewer, nipples, ass, sweat, completely_nude, girl_on_top, looking_back, pussy, smile, anus, cum, pink_eyes, reverse_cowgirl_position, sex_from_behind, vaginal, artist_name, bangs, open_mouth, pov, simple_background | ### Table Version | # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | ninja | solo | bangs | fingerless_gloves | looking_at_viewer | simple_background | cleavage | medium_breasts | obi | pink_eyes | smile | white_background | white_scarf | black_gloves | holding | arm_guards | elbow_gloves | black_thighhighs | closed_mouth | official_alternate_costume | sheathed | short_sword | sleeveless_kimono | very_long_hair | armor | gloves | upper_body | from_behind | looking_back | back_cutout | axe | holding_weapon | backless_dress | boots | shoulder_armor | thighhighs | 1boy | hetero | navel | nipples | blush | penis | sex | solo_focus | uncensored | clitoris | sweat | vaginal | artist_name | lying | pussy_juice | spread_legs | ass | completely_nude | girl_on_top | pussy | anus | cum | reverse_cowgirl_position | sex_from_behind | open_mouth | pov | |----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------|:--------------------|:--------------------|:--------------------|:-----------|:-----------------|:------|:------------|:--------|:-------------------|:--------------|:---------------|:----------|:-------------|:---------------|:-------------------|:---------------|:-----------------------------|:-----------|:--------------|:--------------------|:-----------------|:--------|:---------|:-------------|:--------------|:---------------|:--------------|:------|:-----------------|:-----------------|:--------|:-----------------|:-------------|:-------|:---------|:--------|:----------|:--------|:--------|:------|:-------------|:-------------|:-----------|:--------|:----------|:--------------|:--------|:--------------|:--------------|:------|:------------------|:--------------|:--------|:-------|:------|:---------------------------|:------------------|:-------------|:------| | 0 | 12 |  |  |  |  |  | X | 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 | 17 |  |  |  |  |  | X | | X | | | X | X | | | | | X | X | | | | | | | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 2 | 12 |  |  |  |  |  | X | | X | | | X | X | | | | | X | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | 3 | 6 |  |  |  |  |  | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | 4 | 8 |  |  |  |  |  | X | | | X | | X | 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/serge_fireemblem

2024-01-17T16:51:16+00:00

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

2024-01-17T17:24:17+00:00

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

Dataset of serge (Fire Emblem) ============================== This is the dataset of serge (Fire Emblem), containing 151 images and their tags. The core tags of this character are 'long\_hair, breasts, red\_hair, red\_eyes, hairband, large\_breasts, pink\_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

f3714cfd498cdeed07e0dcd41f7d99069fda71b3

# Dataset Card for Evaluation run of llmixer/BigWeave-v20-110b <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [llmixer/BigWeave-v20-110b](https://huggingface.co/llmixer/BigWeave-v20-110b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_llmixer__BigWeave-v20-110b", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T10:41:33.075058](https://huggingface.co/datasets/open-llm-leaderboard/details_llmixer__BigWeave-v20-110b/blob/main/results_2024-02-16T10-41-33.075058.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": 0.7006598594902614, "acc_stderr": 0.030334879953044784, "acc_norm": 0.7077385502169132, "acc_norm_stderr": 0.03092785311503184, "mc1": 0.44920440636474906, "mc1_stderr": 0.017412941986115305, "mc2": 0.6247452534043703, "mc2_stderr": 0.01525624326187566 }, "harness|arc:challenge|25": { "acc": 0.6484641638225256, "acc_stderr": 0.013952413699600935, "acc_norm": 0.681740614334471, "acc_norm_stderr": 0.013611993916971453 }, "harness|hellaswag|10": { "acc": 0.7175861382194781, "acc_stderr": 0.004492535748097629, "acc_norm": 0.885381398127863, "acc_norm_stderr": 0.0031791005658879977 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.37, "acc_stderr": 0.04852365870939098, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939098 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5925925925925926, "acc_stderr": 0.04244633238353228, "acc_norm": 0.5925925925925926, "acc_norm_stderr": 0.04244633238353228 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03317672787533157, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03317672787533157 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.74, "acc_stderr": 0.04408440022768081, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768081 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.7283018867924528, "acc_stderr": 0.027377706624670716, "acc_norm": 0.7283018867924528, "acc_norm_stderr": 0.027377706624670716 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.8541666666666666, "acc_stderr": 0.02951424596429177, "acc_norm": 0.8541666666666666, "acc_norm_stderr": 0.02951424596429177 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.49, "acc_stderr": 0.05024183937956912, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.57, "acc_stderr": 0.049756985195624284, "acc_norm": 0.57, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.39, "acc_stderr": 0.04902071300001975, "acc_norm": 0.39, "acc_norm_stderr": 0.04902071300001975 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6878612716763006, "acc_stderr": 0.035331333893236574, "acc_norm": 0.6878612716763006, "acc_norm_stderr": 0.035331333893236574 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.39215686274509803, "acc_stderr": 0.04858083574266345, "acc_norm": 0.39215686274509803, "acc_norm_stderr": 0.04858083574266345 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.79, "acc_stderr": 0.040936018074033256, "acc_norm": 0.79, "acc_norm_stderr": 0.040936018074033256 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.6893617021276596, "acc_stderr": 0.03025123757921317, "acc_norm": 0.6893617021276596, "acc_norm_stderr": 0.03025123757921317 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5263157894736842, "acc_stderr": 0.046970851366478626, "acc_norm": 0.5263157894736842, "acc_norm_stderr": 0.046970851366478626 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.6344827586206897, "acc_stderr": 0.04013124195424386, "acc_norm": 0.6344827586206897, "acc_norm_stderr": 0.04013124195424386 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.46296296296296297, "acc_stderr": 0.025680564640056882, "acc_norm": 0.46296296296296297, "acc_norm_stderr": 0.025680564640056882 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.48412698412698413, "acc_stderr": 0.04469881854072606, "acc_norm": 0.48412698412698413, "acc_norm_stderr": 0.04469881854072606 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.51, "acc_stderr": 0.05024183937956912, "acc_norm": 0.51, "acc_norm_stderr": 0.05024183937956912 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8516129032258064, "acc_stderr": 0.020222737554330385, "acc_norm": 0.8516129032258064, "acc_norm_stderr": 0.020222737554330385 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.5566502463054187, "acc_stderr": 0.03495334582162933, "acc_norm": 0.5566502463054187, "acc_norm_stderr": 0.03495334582162933 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.74, "acc_stderr": 0.04408440022768079, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768079 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8303030303030303, "acc_stderr": 0.029311188674983137, "acc_norm": 0.8303030303030303, "acc_norm_stderr": 0.029311188674983137 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.8838383838383839, "acc_stderr": 0.022828881775249377, "acc_norm": 0.8838383838383839, "acc_norm_stderr": 0.022828881775249377 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9222797927461139, "acc_stderr": 0.019321805557223157, "acc_norm": 0.9222797927461139, "acc_norm_stderr": 0.019321805557223157 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.7128205128205128, "acc_stderr": 0.022939925418530616, "acc_norm": 0.7128205128205128, "acc_norm_stderr": 0.022939925418530616 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.34444444444444444, "acc_stderr": 0.028972648884844267, "acc_norm": 0.34444444444444444, "acc_norm_stderr": 0.028972648884844267 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8025210084033614, "acc_stderr": 0.025859164122051456, "acc_norm": 0.8025210084033614, "acc_norm_stderr": 0.025859164122051456 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5099337748344371, "acc_stderr": 0.04081677107248437, "acc_norm": 0.5099337748344371, "acc_norm_stderr": 0.04081677107248437 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.8844036697247707, "acc_stderr": 0.01370874953417264, "acc_norm": 0.8844036697247707, "acc_norm_stderr": 0.01370874953417264 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.625, "acc_stderr": 0.033016908987210894, "acc_norm": 0.625, "acc_norm_stderr": 0.033016908987210894 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9068627450980392, "acc_stderr": 0.020397853969426998, "acc_norm": 0.9068627450980392, "acc_norm_stderr": 0.020397853969426998 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.8945147679324894, "acc_stderr": 0.019995560723758556, "acc_norm": 0.8945147679324894, "acc_norm_stderr": 0.019995560723758556 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7757847533632287, "acc_stderr": 0.027991534258519524, "acc_norm": 0.7757847533632287, "acc_norm_stderr": 0.027991534258519524 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness|hendrycksTest-international_law|5": { "acc": 0.8677685950413223, "acc_stderr": 0.03092278832044579, "acc_norm": 0.8677685950413223, "acc_norm_stderr": 0.03092278832044579 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8148148148148148, "acc_stderr": 0.03755265865037181, "acc_norm": 0.8148148148148148, "acc_norm_stderr": 0.03755265865037181 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8159509202453987, "acc_stderr": 0.030446777687971726, "acc_norm": 0.8159509202453987, "acc_norm_stderr": 0.030446777687971726 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6160714285714286, "acc_stderr": 0.04616143075028546, "acc_norm": 0.6160714285714286, "acc_norm_stderr": 0.04616143075028546 }, "harness|hendrycksTest-management|5": { "acc": 0.8155339805825242, "acc_stderr": 0.03840423627288276, "acc_norm": 0.8155339805825242, "acc_norm_stderr": 0.03840423627288276 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9017094017094017, "acc_stderr": 0.019503444900757567, "acc_norm": 0.9017094017094017, "acc_norm_stderr": 0.019503444900757567 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.69, "acc_stderr": 0.04648231987117316, "acc_norm": 0.69, "acc_norm_stderr": 0.04648231987117316 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.8646232439335888, "acc_stderr": 0.012234384586856488, "acc_norm": 0.8646232439335888, "acc_norm_stderr": 0.012234384586856488 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8121387283236994, "acc_stderr": 0.021029269752423217, "acc_norm": 0.8121387283236994, "acc_norm_stderr": 0.021029269752423217 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.40558659217877097, "acc_stderr": 0.016421670506339185, "acc_norm": 0.40558659217877097, "acc_norm_stderr": 0.016421670506339185 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.7777777777777778, "acc_stderr": 0.023805186524888156, "acc_norm": 0.7777777777777778, "acc_norm_stderr": 0.023805186524888156 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.7942122186495176, "acc_stderr": 0.022961339906764244, "acc_norm": 0.7942122186495176, "acc_norm_stderr": 0.022961339906764244 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8364197530864198, "acc_stderr": 0.020581466138257117, "acc_norm": 0.8364197530864198, "acc_norm_stderr": 0.020581466138257117 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.5531914893617021, "acc_stderr": 0.02965823509766691, "acc_norm": 0.5531914893617021, "acc_norm_stderr": 0.02965823509766691 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.5658409387222947, "acc_stderr": 0.012659033237067253, "acc_norm": 0.5658409387222947, "acc_norm_stderr": 0.012659033237067253 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.75, "acc_stderr": 0.026303648393696036, "acc_norm": 0.75, "acc_norm_stderr": 0.026303648393696036 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.7728758169934641, "acc_stderr": 0.016949853279212373, "acc_norm": 0.7728758169934641, "acc_norm_stderr": 0.016949853279212373 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7090909090909091, "acc_stderr": 0.04350271442923243, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.04350271442923243 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8163265306122449, "acc_stderr": 0.024789071332007636, "acc_norm": 0.8163265306122449, "acc_norm_stderr": 0.024789071332007636 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824667, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824667 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.91, "acc_stderr": 0.028762349126466125, "acc_norm": 0.91, "acc_norm_stderr": 0.028762349126466125 }, "harness|hendrycksTest-virology|5": { "acc": 0.5421686746987951, "acc_stderr": 0.0387862677100236, "acc_norm": 0.5421686746987951, "acc_norm_stderr": 0.0387862677100236 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8538011695906432, "acc_stderr": 0.027097290118070806, "acc_norm": 0.8538011695906432, "acc_norm_stderr": 0.027097290118070806 }, "harness|truthfulqa:mc|0": { "mc1": 0.44920440636474906, "mc1_stderr": 0.017412941986115305, "mc2": 0.6247452534043703, "mc2_stderr": 0.01525624326187566 }, "harness|winogrande|5": { "acc": 0.8208366219415943, "acc_stderr": 0.010777949156047987 }, "harness|gsm8k|5": { "acc": 0.3639120545868082, "acc_stderr": 0.013252539227966193 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for 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open-llm-leaderboard/details_llmixer__BigWeave-v20-110b

2024-02-16T10:43:51+00:00

{"pretty_name": "Evaluation run of llmixer/BigWeave-v20-110b", "dataset_summary": "Dataset automatically created during the evaluation run of model [llmixer/BigWeave-v20-110b](https://huggingface.co/llmixer/BigWeave-v20-110b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_llmixer__BigWeave-v20-110b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T10:41:33.075058](https://huggingface.co/datasets/open-llm-leaderboard/details_llmixer__BigWeave-v20-110b/blob/main/results_2024-02-16T10-41-33.075058.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\": 0.7006598594902614,\n \"acc_stderr\": 0.030334879953044784,\n \"acc_norm\": 0.7077385502169132,\n \"acc_norm_stderr\": 0.03092785311503184,\n \"mc1\": 0.44920440636474906,\n \"mc1_stderr\": 0.017412941986115305,\n \"mc2\": 0.6247452534043703,\n \"mc2_stderr\": 0.01525624326187566\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6484641638225256,\n \"acc_stderr\": 0.013952413699600935,\n \"acc_norm\": 0.681740614334471,\n \"acc_norm_stderr\": 0.013611993916971453\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7175861382194781,\n \"acc_stderr\": 0.004492535748097629,\n \"acc_norm\": 0.885381398127863,\n \"acc_norm_stderr\": 0.0031791005658879977\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03317672787533157,\n \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03317672787533157\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670716,\n \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670716\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8541666666666666,\n \"acc_stderr\": 0.02951424596429177,\n \"acc_norm\": 0.8541666666666666,\n \"acc_norm_stderr\": 0.02951424596429177\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.035331333893236574,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.035331333893236574\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.04858083574266345\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6893617021276596,\n \"acc_stderr\": 0.03025123757921317,\n \"acc_norm\": 0.6893617021276596,\n \"acc_norm_stderr\": 0.03025123757921317\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424386,\n \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424386\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.46296296296296297,\n \"acc_stderr\": 0.025680564640056882,\n \"acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.025680564640056882\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8516129032258064,\n \"acc_stderr\": 0.020222737554330385,\n \"acc_norm\": 0.8516129032258064,\n \"acc_norm_stderr\": 0.020222737554330385\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5566502463054187,\n \"acc_stderr\": 0.03495334582162933,\n \"acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.03495334582162933\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8303030303030303,\n \"acc_stderr\": 0.029311188674983137,\n \"acc_norm\": 0.8303030303030303,\n \"acc_norm_stderr\": 0.029311188674983137\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.8838383838383839,\n \"acc_stderr\": 0.022828881775249377,\n \"acc_norm\": 0.8838383838383839,\n \"acc_norm_stderr\": 0.022828881775249377\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9222797927461139,\n \"acc_stderr\": 0.019321805557223157,\n \"acc_norm\": 0.9222797927461139,\n \"acc_norm_stderr\": 0.019321805557223157\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530616,\n \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530616\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8025210084033614,\n \"acc_stderr\": 0.025859164122051456,\n \"acc_norm\": 0.8025210084033614,\n \"acc_norm_stderr\": 0.025859164122051456\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.625,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9068627450980392,\n \"acc_stderr\": 0.020397853969426998,\n \"acc_norm\": 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969426998\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8945147679324894,\n \"acc_stderr\": 0.019995560723758556,\n \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.019995560723758556\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7757847533632287,\n \"acc_stderr\": 0.027991534258519524,\n \"acc_norm\": 0.7757847533632287,\n \"acc_norm_stderr\": 0.027991534258519524\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8677685950413223,\n \"acc_stderr\": 0.03092278832044579,\n \"acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.03092278832044579\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037181,\n 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["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["**/details_harness|winogrande|5_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-16T10-41-33.075058.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_16T10_41_33.075058", "path": ["results_2024-02-16T10-41-33.075058.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T10-41-33.075058.parquet"]}]}]}

2024-02-16T10:44:12+00:00

TAGS #region-us

# Dataset Card for Evaluation run of llmixer/BigWeave-v20-110b Dataset automatically created during the evaluation run of model llmixer/BigWeave-v20-110b on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-02-16T10:41:33.075058(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

d26fa5f64870c21464cced5977d5a51135774a25

# Dataset Card for Evaluation run of logicker/SkkuDS-DPO-72B-v1 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [logicker/SkkuDS-DPO-72B-v1](https://huggingface.co/logicker/SkkuDS-DPO-72B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_logicker__SkkuDS-DPO-72B-v1", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T10:55:52.095277](https://huggingface.co/datasets/open-llm-leaderboard/details_logicker__SkkuDS-DPO-72B-v1/blob/main/results_2024-02-16T10-55-52.095277.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": 0.7681185312998495, "acc_stderr": 0.02797672385731024, "acc_norm": 0.7728008468755523, "acc_norm_stderr": 0.02849748439769033, "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.595432675425976, "mc2_stderr": 0.014511387340720846 }, "harness|arc:challenge|25": { "acc": 0.6271331058020477, "acc_stderr": 0.014131176760131172, "acc_norm": 0.659556313993174, "acc_norm_stderr": 0.013847460518892978 }, "harness|hellaswag|10": { "acc": 0.6671977693686517, "acc_stderr": 0.004702533775930293, "acc_norm": 0.8599880501892053, "acc_norm_stderr": 0.0034629026011361893 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.43, "acc_stderr": 0.049756985195624284, "acc_norm": 0.43, "acc_norm_stderr": 0.049756985195624284 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.7333333333333333, "acc_stderr": 0.038201699145179055, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.038201699145179055 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.881578947368421, "acc_stderr": 0.026293995855474928, "acc_norm": 0.881578947368421, "acc_norm_stderr": 0.026293995855474928 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.8, "acc_stderr": 0.04020151261036845, "acc_norm": 0.8, "acc_norm_stderr": 0.04020151261036845 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.8226415094339623, "acc_stderr": 0.023508739218846934, "acc_norm": 0.8226415094339623, "acc_norm_stderr": 0.023508739218846934 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.9166666666666666, "acc_stderr": 0.023112508176051236, "acc_norm": 0.9166666666666666, "acc_norm_stderr": 0.023112508176051236 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.57, "acc_stderr": 0.04975698519562428, "acc_norm": 0.57, "acc_norm_stderr": 0.04975698519562428 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.7514450867052023, "acc_stderr": 0.03295304696818317, "acc_norm": 0.7514450867052023, "acc_norm_stderr": 0.03295304696818317 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.5490196078431373, "acc_stderr": 0.049512182523962604, "acc_norm": 0.5490196078431373, "acc_norm_stderr": 0.049512182523962604 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.82, "acc_stderr": 0.03861229196653695, "acc_norm": 0.82, "acc_norm_stderr": 0.03861229196653695 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.8042553191489362, "acc_stderr": 0.025937853139977148, "acc_norm": 0.8042553191489362, "acc_norm_stderr": 0.025937853139977148 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.5877192982456141, "acc_stderr": 0.046306532033665956, "acc_norm": 0.5877192982456141, "acc_norm_stderr": 0.046306532033665956 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.7862068965517242, "acc_stderr": 0.03416520447747549, "acc_norm": 0.7862068965517242, "acc_norm_stderr": 0.03416520447747549 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.7116402116402116, "acc_stderr": 0.02333065405453588, "acc_norm": 0.7116402116402116, "acc_norm_stderr": 0.02333065405453588 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.5952380952380952, "acc_stderr": 0.04390259265377563, "acc_norm": 0.5952380952380952, "acc_norm_stderr": 0.04390259265377563 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.52, "acc_stderr": 0.050211673156867795, "acc_norm": 0.52, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.8838709677419355, "acc_stderr": 0.018225757949432306, "acc_norm": 0.8838709677419355, "acc_norm_stderr": 0.018225757949432306 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.6551724137931034, "acc_stderr": 0.03344283744280459, "acc_norm": 0.6551724137931034, "acc_norm_stderr": 0.03344283744280459 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.83, "acc_stderr": 0.03775251680686371, "acc_norm": 0.83, "acc_norm_stderr": 0.03775251680686371 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.8545454545454545, "acc_stderr": 0.027530196355066573, "acc_norm": 0.8545454545454545, "acc_norm_stderr": 0.027530196355066573 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.9343434343434344, "acc_stderr": 0.01764652667723333, "acc_norm": 0.9343434343434344, "acc_norm_stderr": 0.01764652667723333 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.9896373056994818, "acc_stderr": 0.007308424386792194, "acc_norm": 0.9896373056994818, "acc_norm_stderr": 0.007308424386792194 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.8153846153846154, "acc_stderr": 0.01967163241310029, "acc_norm": 0.8153846153846154, "acc_norm_stderr": 0.01967163241310029 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.5, "acc_stderr": 0.030485538042484616, "acc_norm": 0.5, "acc_norm_stderr": 0.030485538042484616 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.8445378151260504, "acc_stderr": 0.023536818625398904, "acc_norm": 0.8445378151260504, "acc_norm_stderr": 0.023536818625398904 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.5562913907284768, "acc_stderr": 0.04056527902281732, "acc_norm": 0.5562913907284768, "acc_norm_stderr": 0.04056527902281732 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.926605504587156, "acc_stderr": 0.011180976446357573, "acc_norm": 0.926605504587156, "acc_norm_stderr": 0.011180976446357573 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.6990740740740741, "acc_stderr": 0.03128039084329883, "acc_norm": 0.6990740740740741, "acc_norm_stderr": 0.03128039084329883 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.9313725490196079, "acc_stderr": 0.017744453647073322, "acc_norm": 0.9313725490196079, "acc_norm_stderr": 0.017744453647073322 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.9029535864978903, "acc_stderr": 0.019269323025640273, "acc_norm": 0.9029535864978903, "acc_norm_stderr": 0.019269323025640273 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.7982062780269058, "acc_stderr": 0.026936111912802273, "acc_norm": 0.7982062780269058, "acc_norm_stderr": 0.026936111912802273 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.8702290076335878, "acc_stderr": 0.029473649496907065, "acc_norm": 0.8702290076335878, "acc_norm_stderr": 0.029473649496907065 }, "harness|hendrycksTest-international_law|5": { "acc": 0.9090909090909091, "acc_stderr": 0.026243194054073892, "acc_norm": 0.9090909090909091, "acc_norm_stderr": 0.026243194054073892 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.8518518518518519, "acc_stderr": 0.03434300243630999, "acc_norm": 0.8518518518518519, "acc_norm_stderr": 0.03434300243630999 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.8711656441717791, "acc_stderr": 0.02632138319878367, "acc_norm": 0.8711656441717791, "acc_norm_stderr": 0.02632138319878367 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.6607142857142857, "acc_stderr": 0.044939490686135404, "acc_norm": 0.6607142857142857, "acc_norm_stderr": 0.044939490686135404 }, "harness|hendrycksTest-management|5": { "acc": 0.8640776699029126, "acc_stderr": 0.03393295729761011, "acc_norm": 0.8640776699029126, "acc_norm_stderr": 0.03393295729761011 }, "harness|hendrycksTest-marketing|5": { "acc": 0.9401709401709402, "acc_stderr": 0.015537514263253874, "acc_norm": 0.9401709401709402, "acc_norm_stderr": 0.015537514263253874 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.86, "acc_stderr": 0.034873508801977725, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977725 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.9144316730523627, "acc_stderr": 0.010002965568647285, "acc_norm": 0.9144316730523627, "acc_norm_stderr": 0.010002965568647285 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.8352601156069365, "acc_stderr": 0.019971040982442262, "acc_norm": 0.8352601156069365, "acc_norm_stderr": 0.019971040982442262 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.6424581005586593, "acc_stderr": 0.01602939447489489, "acc_norm": 0.6424581005586593, "acc_norm_stderr": 0.01602939447489489 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.8562091503267973, "acc_stderr": 0.020091188936043728, "acc_norm": 0.8562091503267973, "acc_norm_stderr": 0.020091188936043728 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.8295819935691319, "acc_stderr": 0.02135534302826405, "acc_norm": 0.8295819935691319, "acc_norm_stderr": 0.02135534302826405 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.8611111111111112, "acc_stderr": 0.01924252622654454, "acc_norm": 0.8611111111111112, "acc_norm_stderr": 0.01924252622654454 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.624113475177305, "acc_stderr": 0.028893955412115882, "acc_norm": 0.624113475177305, "acc_norm_stderr": 0.028893955412115882 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.6140808344198174, "acc_stderr": 0.012433398911476134, "acc_norm": 0.6140808344198174, "acc_norm_stderr": 0.012433398911476134 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.8198529411764706, "acc_stderr": 0.023345163616544838, "acc_norm": 0.8198529411764706, "acc_norm_stderr": 0.023345163616544838 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.8088235294117647, "acc_stderr": 0.015908290136278067, "acc_norm": 0.8088235294117647, "acc_norm_stderr": 0.015908290136278067 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.7363636363636363, "acc_stderr": 0.04220224692971987, "acc_norm": 0.7363636363636363, "acc_norm_stderr": 0.04220224692971987 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.8326530612244898, "acc_stderr": 0.02389714476891452, "acc_norm": 0.8326530612244898, "acc_norm_stderr": 0.02389714476891452 }, "harness|hendrycksTest-sociology|5": { "acc": 0.8905472636815921, "acc_stderr": 0.022076326101824667, "acc_norm": 0.8905472636815921, "acc_norm_stderr": 0.022076326101824667 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.94, "acc_stderr": 0.023868325657594194, "acc_norm": 0.94, "acc_norm_stderr": 0.023868325657594194 }, "harness|hendrycksTest-virology|5": { "acc": 0.572289156626506, "acc_stderr": 0.03851597683718533, "acc_norm": 0.572289156626506, "acc_norm_stderr": 0.03851597683718533 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8888888888888888, "acc_stderr": 0.024103384202072864, "acc_norm": 0.8888888888888888, "acc_norm_stderr": 0.024103384202072864 }, "harness|truthfulqa:mc|0": { "mc1": 0.41370869033047736, "mc1_stderr": 0.0172408618120998, "mc2": 0.595432675425976, "mc2_stderr": 0.014511387340720846 }, "harness|winogrande|5": { "acc": 0.8263614838200474, "acc_stderr": 0.010646116480330996 }, "harness|gsm8k|5": { "acc": 0.6588324488248674, "acc_stderr": 0.013059111935831497 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. 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open-llm-leaderboard/details_logicker__SkkuDS-DPO-72B-v1

2024-02-16T10:57:59+00:00

{"pretty_name": "Evaluation run of logicker/SkkuDS-DPO-72B-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [logicker/SkkuDS-DPO-72B-v1](https://huggingface.co/logicker/SkkuDS-DPO-72B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_logicker__SkkuDS-DPO-72B-v1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T10:55:52.095277](https://huggingface.co/datasets/open-llm-leaderboard/details_logicker__SkkuDS-DPO-72B-v1/blob/main/results_2024-02-16T10-55-52.095277.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\": 0.7681185312998495,\n \"acc_stderr\": 0.02797672385731024,\n \"acc_norm\": 0.7728008468755523,\n \"acc_norm_stderr\": 0.02849748439769033,\n \"mc1\": 0.41370869033047736,\n \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.595432675425976,\n \"mc2_stderr\": 0.014511387340720846\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6271331058020477,\n \"acc_stderr\": 0.014131176760131172,\n \"acc_norm\": 0.659556313993174,\n \"acc_norm_stderr\": 0.013847460518892978\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6671977693686517,\n \"acc_stderr\": 0.004702533775930293,\n \"acc_norm\": 0.8599880501892053,\n \"acc_norm_stderr\": 0.0034629026011361893\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.026293995855474928,\n \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.026293995855474928\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.8226415094339623,\n \"acc_stderr\": 0.023508739218846934,\n \"acc_norm\": 0.8226415094339623,\n \"acc_norm_stderr\": 0.023508739218846934\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9166666666666666,\n \"acc_stderr\": 0.023112508176051236,\n \"acc_norm\": 0.9166666666666666,\n \"acc_norm_stderr\": 0.023112508176051236\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.03295304696818317,\n \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.03295304696818317\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.5490196078431373,\n \"acc_stderr\": 0.049512182523962604,\n \"acc_norm\": 0.5490196078431373,\n \"acc_norm_stderr\": 0.049512182523962604\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653695,\n \"acc_norm\": 0.82,\n \"acc_norm_stderr\": 0.03861229196653695\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.8042553191489362,\n \"acc_stderr\": 0.025937853139977148,\n \"acc_norm\": 0.8042553191489362,\n \"acc_norm_stderr\": 0.025937853139977148\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5877192982456141,\n \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.5877192982456141,\n \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.7862068965517242,\n \"acc_stderr\": 0.03416520447747549,\n \"acc_norm\": 0.7862068965517242,\n \"acc_norm_stderr\": 0.03416520447747549\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.7116402116402116,\n \"acc_stderr\": 0.02333065405453588,\n \"acc_norm\": 0.7116402116402116,\n \"acc_norm_stderr\": 0.02333065405453588\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5952380952380952,\n \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.5952380952380952,\n \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8838709677419355,\n \"acc_stderr\": 0.018225757949432306,\n \"acc_norm\": 0.8838709677419355,\n \"acc_norm_stderr\": 0.018225757949432306\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03344283744280459,\n \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03344283744280459\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.9343434343434344,\n \"acc_stderr\": 0.01764652667723333,\n \"acc_norm\": 0.9343434343434344,\n \"acc_norm_stderr\": 0.01764652667723333\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9896373056994818,\n \"acc_stderr\": 0.007308424386792194,\n \"acc_norm\": 0.9896373056994818,\n \"acc_norm_stderr\": 0.007308424386792194\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.8153846153846154,\n \"acc_stderr\": 0.01967163241310029,\n \"acc_norm\": 0.8153846153846154,\n \"acc_norm_stderr\": 0.01967163241310029\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030485538042484616,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030485538042484616\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.5562913907284768,\n \"acc_stderr\": 0.04056527902281732,\n \"acc_norm\": 0.5562913907284768,\n \"acc_norm_stderr\": 0.04056527902281732\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.926605504587156,\n \"acc_stderr\": 0.011180976446357573,\n \"acc_norm\": 0.926605504587156,\n \"acc_norm_stderr\": 0.011180976446357573\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.6990740740740741,\n \"acc_stderr\": 0.03128039084329883,\n \"acc_norm\": 0.6990740740740741,\n \"acc_norm_stderr\": 0.03128039084329883\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.9313725490196079,\n \"acc_stderr\": 0.017744453647073322,\n \"acc_norm\": 0.9313725490196079,\n \"acc_norm_stderr\": 0.017744453647073322\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.9029535864978903,\n \"acc_stderr\": 0.019269323025640273,\n \"acc_norm\": 0.9029535864978903,\n \"acc_norm_stderr\": 0.019269323025640273\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n \"acc_stderr\": 0.026936111912802273,\n \"acc_norm\": 0.7982062780269058,\n \"acc_norm_stderr\": 0.026936111912802273\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.9090909090909091,\n \"acc_stderr\": 0.026243194054073892,\n \"acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.026243194054073892\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8518518518518519,\n \"acc_stderr\": 0.03434300243630999,\n \"acc_norm\": 0.8518518518518519,\n \"acc_norm_stderr\": 0.03434300243630999\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8711656441717791,\n \"acc_stderr\": 0.02632138319878367,\n \"acc_norm\": 0.8711656441717791,\n \"acc_norm_stderr\": 0.02632138319878367\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6607142857142857,\n \"acc_stderr\": 0.044939490686135404,\n \"acc_norm\": 0.6607142857142857,\n \"acc_norm_stderr\": 0.044939490686135404\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.03393295729761011,\n \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.03393295729761011\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9401709401709402,\n \"acc_stderr\": 0.015537514263253874,\n \"acc_norm\": 0.9401709401709402,\n \"acc_norm_stderr\": 0.015537514263253874\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977725,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977725\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9144316730523627,\n \"acc_stderr\": 0.010002965568647285,\n \"acc_norm\": 0.9144316730523627,\n \"acc_norm_stderr\": 0.010002965568647285\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.8352601156069365,\n \"acc_stderr\": 0.019971040982442262,\n \"acc_norm\": 0.8352601156069365,\n \"acc_norm_stderr\": 0.019971040982442262\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6424581005586593,\n \"acc_stderr\": 0.01602939447489489,\n \"acc_norm\": 0.6424581005586593,\n \"acc_norm_stderr\": 0.01602939447489489\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.8562091503267973,\n \"acc_stderr\": 0.020091188936043728,\n \"acc_norm\": 0.8562091503267973,\n \"acc_norm_stderr\": 0.020091188936043728\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8295819935691319,\n \"acc_stderr\": 0.02135534302826405,\n \"acc_norm\": 0.8295819935691319,\n \"acc_norm_stderr\": 0.02135534302826405\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.8611111111111112,\n \"acc_stderr\": 0.01924252622654454,\n \"acc_norm\": 0.8611111111111112,\n \"acc_norm_stderr\": 0.01924252622654454\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.624113475177305,\n \"acc_stderr\": 0.028893955412115882,\n \"acc_norm\": 0.624113475177305,\n \"acc_norm_stderr\": 0.028893955412115882\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6140808344198174,\n \"acc_stderr\": 0.012433398911476134,\n \"acc_norm\": 0.6140808344198174,\n \"acc_norm_stderr\": 0.012433398911476134\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.8198529411764706,\n \"acc_stderr\": 0.023345163616544838,\n \"acc_norm\": 0.8198529411764706,\n \"acc_norm_stderr\": 0.023345163616544838\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.015908290136278067,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.015908290136278067\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.8326530612244898,\n \"acc_stderr\": 0.02389714476891452,\n \"acc_norm\": 0.8326530612244898,\n \"acc_norm_stderr\": 0.02389714476891452\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n \"acc_stderr\": 0.022076326101824667,\n \"acc_norm\": 0.8905472636815921,\n \"acc_norm_stderr\": 0.022076326101824667\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.94,\n \"acc_stderr\": 0.023868325657594194,\n \"acc_norm\": 0.94,\n \"acc_norm_stderr\": 0.023868325657594194\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n \"acc_stderr\": 0.03851597683718533,\n \"acc_norm\": 0.572289156626506,\n \"acc_norm_stderr\": 0.03851597683718533\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8888888888888888,\n \"acc_stderr\": 0.024103384202072864,\n \"acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.024103384202072864\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.41370869033047736,\n \"mc1_stderr\": 0.0172408618120998,\n \"mc2\": 0.595432675425976,\n \"mc2_stderr\": 0.014511387340720846\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8263614838200474,\n \"acc_stderr\": 0.010646116480330996\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6588324488248674,\n \"acc_stderr\": 0.013059111935831497\n }\n}\n```", "repo_url": "https://huggingface.co/logicker/SkkuDS-DPO-72B-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": "2024_02_16T10_55_52.095277", "path": ["**/details_harness|arc:challenge|25_2024-02-16T10-55-52.095277.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-16T10-55-52.095277.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_16T10_55_52.095277", "path": ["**/details_harness|gsm8k|5_2024-02-16T10-55-52.095277.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-16T10-55-52.095277.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_16T10_55_52.095277", "path": ["**/details_harness|hellaswag|10_2024-02-16T10-55-52.095277.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-16T10-55-52.095277.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_16T10_55_52.095277", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T10-55-52.095277.parquet", 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2024-02-16T10:58:21+00:00

TAGS #region-us

# Dataset Card for Evaluation run of logicker/SkkuDS-DPO-72B-v1 Dataset automatically created during the evaluation run of model logicker/SkkuDS-DPO-72B-v1 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-02-16T10:55:52.095277(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

7419e7bdd0d4ff260875920f9c5056a346163aa1

# Crowd Counting Dataset The dataset includes images featuring crowds of people ranging from **0 to 5000 individuals**. The dataset includes a diverse range of scenes and scenarios, capturing crowds in various settings. Each image in the dataset is accompanied by a corresponding **JSON file** containing detailed labeling information for each person in the crowd for crowd count and classification.  **Types of crowds** in the dataset: *0-1000, 1000-2000, 2000-3000, 3000-4000 and 4000-5000*  This dataset provides a valuable resource for researchers and developers working on crowd counting technology, enabling them to train and evaluate their algorithms with a wide range of crowd sizes and scenarios. It can also be used for benchmarking and comparison of different crowd counting algorithms, as well as for real-world applications such as *public safety and security, urban planning, and retail analytics*. ## Full version of the dataset includes 647 labeled images of crowds, leave a request on **[TrainingData](https://trainingdata.pro/data-market/crowd-counting?utm_source=huggingface&utm_medium=cpc&utm_campaign=crowd-counting-dataset)** to buy the dataset ### Statistics for the dataset (number of images by the crowd's size and image width):  # Get the Dataset ## This is just an example of the data Leave a request on **[https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/crowd-counting?utm_source=huggingface&utm_medium=cpc&utm_campaign=crowd-counting-dataset) to learn about the price and buy the dataset** # Content - **images** - includes original images of crowds placed in subfolders according to its size, - **labels** - includes json-files with labeling and visualised labeling for the images in the previous folder, - **csv file** - includes information for each image in the dataset ### File with the extension .csv - **id**: id of the image, - **image**: link to access the original image, - **label**: link to access the json-file with labeling, - **type**: type of the crowd on the photo ## **[TrainingData](https://trainingdata.pro/data-market/crowd-counting?utm_source=huggingface&utm_medium=cpc&utm_campaign=crowd-counting-dataset)** provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: crowd counting, crowd density estimation, people counting, crowd analysis, image annotation, computer vision, deep learning, object detection, object counting, image classification, dense regression, crowd behavior analysis, crowd tracking, head detection, crowd segmentation, crowd motion analysis, image processing, machine learning, artificial intelligence, ai, human detection, crowd sensing, image dataset, public safety, crowd management, urban planning, event planning, traffic management*

TrainingDataPro/crowd-counting-dataset

2024-02-16T11:18:40+00:00

{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification", "image-to-image"], "tags": ["legal", "code"]}

2024-02-16T13:04:46+00:00

TAGS #task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #legal #code #region-us

# Crowd Counting Dataset The dataset includes images featuring crowds of people ranging from 0 to 5000 individuals. The dataset includes a diverse range of scenes and scenarios, capturing crowds in various settings. Each image in the dataset is accompanied by a corresponding JSON file containing detailed labeling information for each person in the crowd for crowd count and classification. : ![](URL # Get the Dataset ## This is just an example of the data Leave a request on URL to learn about the price and buy the dataset # Content - images - includes original images of crowds placed in subfolders according to its size, - labels - includes json-files with labeling and visualised labeling for the images in the previous folder, - csv file - includes information for each image in the dataset ### File with the extension .csv - id: id of the image, - image: link to access the original image, - label: link to access the json-file with labeling, - type: type of the crowd on the photo ## TrainingData provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: <URL TrainingData's GitHub: URL *keywords: crowd counting, crowd density estimation, people counting, crowd analysis, image annotation, computer vision, deep learning, object detection, object counting, image classification, dense regression, crowd behavior analysis, crowd tracking, head detection, crowd segmentation, crowd motion analysis, image processing, machine learning, artificial intelligence, ai, human detection, crowd sensing, image dataset, public safety, crowd management, urban planning, event planning, traffic management*

515f9788290c8d4485113abf1f2d43f6df77c4cc

# Dataset Card for "HealthCareMagic-100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

ruslanmv/HealthCareMagic-100k

2024-02-16T11:49:53+00:00

{"configs": [{"config_name": "default"}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126454896, "num_examples": 112165}], "download_size": 70518148, "dataset_size": 126454896}}

2024-02-16T11:55:26+00:00

TAGS #region-us

# Dataset Card for "HealthCareMagic-100k" More Information needed

ec4a0e6963d21687567c1baee3615b52c4eb0c41

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

ruslanmv/icliniq-7k

2024-02-16T11:59:51+00:00

{"configs": [{"config_name": "default"}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "answer_icliniq", "dtype": "string"}, {"name": "answer_chatgpt", "dtype": "string"}, {"name": "answer_chatdoctor", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16962106, "num_examples": 7321}], "download_size": 9373080, "dataset_size": 16962106}}

2024-02-16T12:06:04+00:00

TAGS #region-us

# Dataset Card for "ChatDoctor-iCliniq" More Information needed

db6c3ec125c140ce02c6cc13c6dda98dbeca1ff7

# AI Medical Chatbot Dataset This is an experimental Dataset designed to run a Medical Chatbot It contains at least 250k dialogues between a Patient and a Doctor.  For furter information visit the project here: [https://github.com/ruslanmv/ai-medical-chatbot](https://github.com/ruslanmv/ai-medical-chatbot)

ruslanmv/ai-medical-chatbot

2024-02-16T12:10:13+00:00

{"configs": [{"config_name": "default", "data_files": [{"path": "dialogues.*", "split": "train"}]}], "dataset_info": {"dataset_size": 141665910, "download_size": 141665910, "features": [{"dtype": "string", "name": "Description"}, {"dtype": "string", "name": "Patient"}, {"dtype": "string", "name": "Doctor"}], "splits": [{"name": "train", "num_bytes": 141665910, "num_examples": 256916}]}}

2024-02-16T14:35:02+00:00

TAGS #region-us

# AI Medical Chatbot Dataset This is an experimental Dataset designed to run a Medical Chatbot It contains at least 250k dialogues between a Patient and a Doctor.  For furter information visit the project here: URL

1771ecb2e231d6e743a6a2e32482544315a1f8e5

# Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

Izazk/izaz-mind2web-dataset

2024-02-16T12:12:56+00:00

{"size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text-classification", "feature-extraction"], "pretty_name": "Mind2webAction", "tags": ["code", "biology", "webdataset", "climate", "finance", "legal"]}

2024-02-16T12:34:30+00:00

TAGS #task_categories-text-generation #task_categories-text-classification #task_categories-feature-extraction #size_categories-10K<n<100K #code #biology #webdataset #climate #finance #legal #region-us

# Dataset Card for Dataset Name This dataset card aims to be a base template for new datasets. It has been generated using this raw template. ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

6152ada85bdb58760a75a775b3211ff202770371

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protonacvketogummies/ProtonGummies

2024-02-16T12:17:11+00:00

{}

2024-02-16T12:17:46+00:00

TAGS #region-us

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2714258b09b0863963f78a667252da267b3b91ce

# Brain Cancer MRI Object Detection & Segmentation Dataset The dataset consists of .dcm files containing **MRI scans of the brain** of the person with a cancer. The images are **labeled** by the doctors and accompanied by **report** in PDF-format. The dataset includes 10 studies, made from the different angles which provide a comprehensive understanding of a brain tumor structure. ### MRI study angles in the dataset  # 💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-mri-cancer)** to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - **AND MANY OTHER CONDITIONS**  The MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues. The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes. # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-mri-cancer) to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - **ST000001**: includes subfolders with 10 studies. Each study includes MRI-scans in **.dcm and .jpg formats**, - **DICOMDIR**: includes information about the patient's condition and links to access files, - **Brain_MRI_1.pdf**: includes medical report, provided by the radiologist, - **.csv file**: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's **demographic information**, - **Description** of the case, - Preliminary **diagnosis**, - **Recommendations** on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-mri-cancer) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning*

TrainingDataPro/brain-mri-dataset

2024-02-16T12:51:22+00:00

{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-image", "image-classification", "image-segmentation"], "tags": ["code", "medical", "biology"]}

2024-02-16T13:11:45+00:00

TAGS #task_categories-image-to-image #task_categories-image-classification #task_categories-image-segmentation #language-English #license-cc-by-nc-nd-4.0 #code #medical #biology #region-us

# Brain Cancer MRI Object Detection & Segmentation Dataset The dataset consists of .dcm files containing MRI scans of the brain of the person with a cancer. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 10 studies, made from the different angles which provide a comprehensive understanding of a brain tumor structure. ### MRI study angles in the dataset ![](URL # For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on TrainingData to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - AND MANY OTHER CONDITIONS ![](URL The MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues. The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes. # Buy the Dataset: This is just an example of the data. Leave a request on URL to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - ST000001: includes subfolders with 10 studies. Each study includes MRI-scans in .dcm and .jpg formats, - DICOMDIR: includes information about the patient's condition and links to access files, - Brain_MRI_1.pdf: includes medical report, provided by the radiologist, - .csv file: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's demographic information, - Description of the case, - Preliminary diagnosis, - Recommendations on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## TrainingData provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: <URL TrainingData's GitHub: URL *keywords: mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning*

c39b4e969668cc2fc7cf9e5bf2de0cc23829492c

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

gguichard/myriade_ontologie_corrected

2024-02-16T12:51:35+00:00

{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "wn_sens", "sequence": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10981396, "num_examples": 33834}], "download_size": 0, "dataset_size": 10981396}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-16T13:43:57+00:00

TAGS #region-us

# Dataset Card for "myriade_ontologie_corrected" More Information needed

9293f9fd80b2c724d0ff87791ac0bce09a319cf6

# Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset The dataset consists of .dcm files containing **MRI scans of the brain** of the person with a multiple sclerosis. The images are **labeled** by the doctors and accompanied by **report** in PDF-format. The dataset includes **13 studies**, made from the different angles which provide a comprehensive understanding of a multiple sclerosis as a condition. ### MRI study angles in the dataset  # 💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=multiple-sclerosis)** to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - **AND MANY OTHER CONDITIONS**  The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated multiple sclerosis detection and segmentation, analyzing response to different treatments, and studying the relationship between imaging features and clinical outcomes. # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=multiple-sclerosis) to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - **ST000001**: includes subfolders with 13 studies. Each study includes MRI-scans in **.dcm and .jpg formats**, - **DICOMDIR**: includes information about the patient's condition and links to access files, - **Brain_MRI_2.pdf**: includes medical report, provided by the radiologist, - **.csv file**: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's **demographic information**, - **Description** of the case, - Preliminary **diagnosis**, - **Recommendations** on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=multiple-sclerosis) provides high-quality data annotation tailored to your needs *keywords: brain mri, multiple sclerosis, neuroimaging, mri scans, lesions, brain atrophy, white matter abnormalities, disease progression, neurodegeneration, inflammation, imaging biomarkers, mri analysis, brain volume loss, gray matter changes, myelin damage, optic neuritis, spinal cord lesions, radiological assessment, aneurysm, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, medical imaging, radiology dataset, neurology dataset, oncology dataset, image dataset, abnormalities detection, brain anatomy, health, brain formations, imaging procedure, x-rays measurements, machine learning, computer vision, deep learning*

TrainingDataPro/multiple-sclerosis-dataset

2024-02-16T12:52:18+00:00

{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-image", "image-classification", "image-segmentation"], "tags": ["biology", "medical"]}

2024-02-16T13:11:01+00:00

TAGS #task_categories-image-to-image #task_categories-image-classification #task_categories-image-segmentation #language-English #license-cc-by-nc-nd-4.0 #biology #medical #region-us

# Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset The dataset consists of .dcm files containing MRI scans of the brain of the person with a multiple sclerosis. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 13 studies, made from the different angles which provide a comprehensive understanding of a multiple sclerosis as a condition. ### MRI study angles in the dataset ![](URL # For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on TrainingData to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - AND MANY OTHER CONDITIONS ![](URL The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated multiple sclerosis detection and segmentation, analyzing response to different treatments, and studying the relationship between imaging features and clinical outcomes. # Buy the Dataset: This is just an example of the data. Leave a request on URL to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - ST000001: includes subfolders with 13 studies. Each study includes MRI-scans in .dcm and .jpg formats, - DICOMDIR: includes information about the patient's condition and links to access files, - Brain_MRI_2.pdf: includes medical report, provided by the radiologist, - .csv file: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's demographic information, - Description of the case, - Preliminary diagnosis, - Recommendations on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## TrainingData provides high-quality data annotation tailored to your needs *keywords: brain mri, multiple sclerosis, neuroimaging, mri scans, lesions, brain atrophy, white matter abnormalities, disease progression, neurodegeneration, inflammation, imaging biomarkers, mri analysis, brain volume loss, gray matter changes, myelin damage, optic neuritis, spinal cord lesions, radiological assessment, aneurysm, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, medical imaging, radiology dataset, neurology dataset, oncology dataset, image dataset, abnormalities detection, brain anatomy, health, brain formations, imaging procedure, x-rays measurements, machine learning, computer vision, deep learning*

efd68fca13a19c0e9f33d3456b7baae506943e9b

# Brain MRI Dataset, Arnold-Chiari Malformation Detection & Segmentation The dataset consists of .dcm files containing **MRI scans of the brain** of the person with a Arnold-Chiari Malformation. The images are **labeled** by the doctors and accompanied by **report** in PDF-format. The dataset includes 6 studies, made from the different angles which provide a comprehensive understanding of a Arnold-Chiari Anomaly and signs of dysplasia of the cranio-vertebral junction (platybasia). ### MRI study angles in the dataset  # 💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-anomaly-detection)** to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - **AND MANY OTHER CONDITIONS** .gif?generation=1707995771155527&alt=media) The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes. # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-anomaly-detection) to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - **ST000001**: includes subfolders with 6 studies. Each study includes MRI-scans in **.dcm and .jpg formats**, - **DICOMDIR**: includes information about the patient's condition and links to access files, - **Brain_MRI_3.pdf**: includes medical report, provided by the radiologist, - **.csv file**: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's **demographic information**, - **Description** of the case, - Preliminary **diagnosis**, - **Recommendations** on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-anomaly-detection) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning*

TrainingDataPro/brain-anomaly-detection

2024-02-16T12:53:26+00:00

{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification", "image-to-image", "image-segmentation"], "tags": ["medical", "code", "biology"]}

2024-02-16T13:13:32+00:00

TAGS #task_categories-image-classification #task_categories-image-to-image #task_categories-image-segmentation #language-English #license-cc-by-nc-nd-4.0 #medical #code #biology #region-us

# Brain MRI Dataset, Arnold-Chiari Malformation Detection & Segmentation The dataset consists of .dcm files containing MRI scans of the brain of the person with a Arnold-Chiari Malformation. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 6 studies, made from the different angles which provide a comprehensive understanding of a Arnold-Chiari Anomaly and signs of dysplasia of the cranio-vertebral junction (platybasia). ### MRI study angles in the dataset ![](URL # For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on TrainingData to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - AND MANY OTHER CONDITIONS ![](URL The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes. # Buy the Dataset: This is just an example of the data. Leave a request on URL to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - ST000001: includes subfolders with 6 studies. Each study includes MRI-scans in .dcm and .jpg formats, - DICOMDIR: includes information about the patient's condition and links to access files, - Brain_MRI_3.pdf: includes medical report, provided by the radiologist, - .csv file: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's demographic information, - Description of the case, - Preliminary diagnosis, - Recommendations on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## TrainingData provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: <URL TrainingData's GitHub: URL *keywords: mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning*

56b3b0bce35f99900a359504f3a00a3e572b05c2

The data in this dataset is a subset of 20newsgroups/SciKit dataset: https://scikit-learn.org/0.19/modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups --- license: mit dataset_info: pretty_name: 'SciKit newsgroup20 subset' features: - name: index dtype: int64 - name: Text dtype: string - name: Label dtype: int32 - name: Class Name dtype: string task_categories: -text classification -sentence similarity tags: -text classification -sentence similarity splits: - name: train num_bytes: 799164 num_examples: 750 download_size: 477299 dataset_size: 799164 configs: - config_name: default data_files: - split: train path: data/train-* ---

acloudfan/newsgroups-mini

2024-02-16T13:03:47+00:00

{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "sentence-similarity"], "pretty_name": "scikit_20newsgroups", "tags": ["20newsgroups", "scikit"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "class", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 493413, "num_examples": 450}], "download_size": 300272, "dataset_size": 493413}}

2024-02-16T14:43:33+00:00

TAGS #task_categories-text-classification #task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #license-mit #20newsgroups #scikit #region-us

The data in this dataset is a subset of 20newsgroups/SciKit dataset: URL --- license: mit dataset_info: pretty_name: 'SciKit newsgroup20 subset' features: - name: index dtype: int64 - name: Text dtype: string - name: Label dtype: int32 - name: Class Name dtype: string task_categories: -text classification -sentence similarity tags: -text classification -sentence similarity splits: - name: train num_bytes: 799164 num_examples: 750 download_size: 477299 dataset_size: 799164 configs: - config_name: default data_files: - split: train path: data/train-* ---

93f1b8a21daca27934a87aac74f92e63148b76d7

# Open Instruct V1 Better Uncensored This is the open-instruct-v1 dataset processed with the Better Uncensored pipeline. About 2.5% of the dataset was removed, a quick review of the removed examples seems to point that is mostly false positives or answers with debatable moralizing content. **No clear refusal was seen in the quick review. The original dataset may be safe for training uncensored models, but if you want to be extra sure you can use this one.** ## Open Instruct V1 - A dataset for having LLMs follow instructions. Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training. ### Dataset Breakdown | Dataset | Amount of Samples | |----------------|-------------------| | [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 51759 | | [Self Instruct](https://github.com/yizhongw/self-instruct) | 82599 | | [GPT-4 Instruct](https://github.com/teknium1/GPTeacher) | 18194 | | [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) | 18019 | | [Dolly](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) | 15015 | | [Synthetic](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | 33143 | | [Roleplay](https://github.com/teknium1/GPTeacher) | 3146 | | [asss](https://huggingface.co/datasets/HuggingFaceH4/asss) | 448 | | [instruction-dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) | 327 | | Total | 222650 |

betteruncensored/open-instruct-v1

2024-02-16T13:03:52+00:00

{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "duplicated_from": "hakurei/open-instruct-v1"}

2024-02-16T13:18:53+00:00

TAGS #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us

Open Instruct V1 Better Uncensored ================================== This is the open-instruct-v1 dataset processed with the Better Uncensored pipeline. About 2.5% of the dataset was removed, a quick review of the removed examples seems to point that is mostly false positives or answers with debatable moralizing content. No clear refusal was seen in the quick review. The original dataset may be safe for training uncensored models, but if you want to be extra sure you can use this one. Open Instruct V1 - A dataset for having LLMs follow instructions. ----------------------------------------------------------------- Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training. ### Dataset Breakdown

0bbaa3272dbe059b198dcf120e8e3b7c45c4a005

# Dataset Card for hh-rlhf-megagon-ja This dataset loads data from [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja). ## Licence MIT License

ryo0634/hh-rlhf-megagon-ja

2024-02-16T13:39:59+00:00

{}

2024-02-16T13:40:50+00:00

TAGS #region-us

# Dataset Card for hh-rlhf-megagon-ja This dataset loads data from megagonlabs/instruction_ja. ## Licence MIT License

6201315cb45368b2285979497197faf09042a9c3

## Dataset Summary Vietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from [edoctor](https://edoctor.io/hoi-dap) and [vinmec](https://www.vinmec.com/vi/tin-tuc/hoi-dap-bac-si/). * Size: After merging data from these two sources, obtained 9335 QA pairs. * Language: Vietnamese ## Load with Datasets ```python from datasets import load_dataset # Load dataset from huggingface qa_dataset = load_dataset("hungnm/vietnamese-medical-qa") # print a QA example print(qa_dataset['train'][0]) ``` ```json { "question": "Chào bác sĩ,\nRăng cháu hiện tại có mủ ở dưới lợi nhưng khi đau cháu sẽ không ngủ được (quá đau). Tuy nhiên chỉ vài ngày là hết mà thỉnh thoảng nó lại bị đau. Chị cháu bảo là trước chị cháu cũng bị như vậy chỉ là đau răng tuổi dậy thì thôi. Bác sĩ cho cháu hỏi đau răng kèm có mủ dưới lợi là bệnh gì? Cháu có cần đi chữa trị không? Cháu cảm ơn.", "answer": "Chào bạn,\nĐể trả lời câu hỏi trên, bác sĩ xin giải đáp như sau:\nRăng bạn hiện tại có mủ dưới lợi gây đau nhức nhiều. Bạn có thể đến phòng khám răng hàm mặt bệnh viện để được thăm khám, chụp phim và tư vấn cho bạn được chính xác\nTrân trọng!" } ``` ## Dataset Version and Update * Version Details: * Current version: 1.0 * Last Update: 16/02/2024 * First Release: 16/02/2024 * Update Plan: We will keep updating as we gather additional data. ## Licensing Information This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Citation Information ```bibtex @misc{vietnamese-medical-qa, title={Vietnamese Medical QA: Question Answering dataset for medical in Vietnamese}, author={Hung Nguyen}, year={2024}, } ```

hungnm/vietnamese-medical-qa

2024-02-16T13:44:46+00:00

{"language": ["vi"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "pretty_name": "b", "dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10672406, "num_examples": 9335}], "download_size": 5289086, "dataset_size": 10672406}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["question answering", "medical", "vietnamese"]}

2024-02-16T14:34:40+00:00

TAGS #task_categories-question-answering #size_categories-1K<n<10K #language-Vietnamese #license-apache-2.0 #question answering #medical #vietnamese #region-us

## Dataset Summary Vietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from edoctor and vinmec. * Size: After merging data from these two sources, obtained 9335 QA pairs. * Language: Vietnamese ## Load with Datasets ## Dataset Version and Update * Version Details: * Current version: 1.0 * Last Update: 16/02/2024 * First Release: 16/02/2024 * Update Plan: We will keep updating as we gather additional data. ## Licensing Information This dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License.

f71d3eb69539dada81918d17c182c99eeb1d126c

# Test dataset with one broken split

severo/test-one-split-broken

2024-02-16T13:45:34+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "works", "path": "works.json"}, {"split": "broken", "path": "broken.json"}]}]}

2024-02-16T14:20:39+00:00

TAGS #region-us

# Test dataset with one broken split

0b9e7d3d254a2ef83716862f6deb2d48c048fc2e

## Dataset Description Your dataset description goes here. Describe what the dataset contains, its purpose, and any relevant information about its creation and usage. ## Data Format Each entry in the dataset consists of two fields: - `audio_path`: The path to the audio file. - `description`: The written description corresponding to the audio file. ## Example Here's an example entry from the dataset: ```json { "audio_path": "/path/to/audio/file.mp3", "description": "A written description of the audio content." }

lucasjca/ProcedimentosSUS

2024-02-16T14:00:13+00:00

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 41920700.0, "num_examples": 89}], "download_size": 41553598, "dataset_size": 41920700.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2024-02-16T18:25:29+00:00

TAGS #license-apache-2.0 #region-us

## Dataset Description Your dataset description goes here. Describe what the dataset contains, its purpose, and any relevant information about its creation and usage. ## Data Format Each entry in the dataset consists of two fields: - 'audio_path': The path to the audio file. - 'description': The written description corresponding to the audio file. ## Example Here's an example entry from the dataset: '''json { "audio_path": "/path/to/audio/file.mp3", "description": "A written description of the audio content." }

36789386a374c15c303b893fb0cdbf515ab13eec

# Dataset Card for Evaluation run of aloobun/Reyna-Mini-1.8B-v0.2 <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [aloobun/Reyna-Mini-1.8B-v0.2](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T14:08:02.040553](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2/blob/main/results_2024-02-16T14-08-02.040553.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": 0.44626133629619946, "acc_stderr": 0.034425038440545834, "acc_norm": 0.4487075344673435, "acc_norm_stderr": 0.03514635225565978, "mc1": 0.2582619339045288, "mc1_stderr": 0.015321821688476196, "mc2": 0.4123912546191622, "mc2_stderr": 0.013946155781929979 }, "harness|arc:challenge|25": { "acc": 0.34044368600682595, "acc_stderr": 0.01384746051889298, "acc_norm": 0.3660409556313993, "acc_norm_stderr": 0.014077223108470144 }, "harness|hellaswag|10": { "acc": 0.44692292372037445, "acc_stderr": 0.004961587574275628, "acc_norm": 0.6018721370244972, "acc_norm_stderr": 0.00488511646555027 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.32, "acc_stderr": 0.046882617226215034, "acc_norm": 0.32, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.3851851851851852, "acc_stderr": 0.042039210401562783, "acc_norm": 0.3851851851851852, "acc_norm_stderr": 0.042039210401562783 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.4342105263157895, "acc_stderr": 0.04033565667848319, "acc_norm": 0.4342105263157895, "acc_norm_stderr": 0.04033565667848319 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.050161355804659205, "acc_norm": 0.53, "acc_norm_stderr": 0.050161355804659205 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.47547169811320755, "acc_stderr": 0.030735822206205608, "acc_norm": 0.47547169811320755, "acc_norm_stderr": 0.030735822206205608 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4097222222222222, "acc_stderr": 0.04112490974670787, "acc_norm": 0.4097222222222222, "acc_norm_stderr": 0.04112490974670787 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.28, "acc_stderr": 0.04512608598542126, "acc_norm": 0.28, "acc_norm_stderr": 0.04512608598542126 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.41, "acc_stderr": 0.04943110704237103, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237103 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.43352601156069365, "acc_stderr": 0.037786210790920545, "acc_norm": 0.43352601156069365, "acc_norm_stderr": 0.037786210790920545 }, "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": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.43829787234042555, "acc_stderr": 0.032436186361081004, "acc_norm": 0.43829787234042555, "acc_norm_stderr": 0.032436186361081004 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.2543859649122807, "acc_stderr": 0.040969851398436716, "acc_norm": 0.2543859649122807, "acc_norm_stderr": 0.040969851398436716 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.46206896551724136, "acc_stderr": 0.041546596717075474, "acc_norm": 0.46206896551724136, "acc_norm_stderr": 0.041546596717075474 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.34656084656084657, "acc_stderr": 0.024508777521028417, "acc_norm": 0.34656084656084657, "acc_norm_stderr": 0.024508777521028417 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2222222222222222, "acc_stderr": 0.03718489006818115, "acc_norm": 0.2222222222222222, "acc_norm_stderr": 0.03718489006818115 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.34, "acc_stderr": 0.04760952285695236, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695236 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.47096774193548385, "acc_stderr": 0.028396016402761, "acc_norm": 0.47096774193548385, "acc_norm_stderr": 0.028396016402761 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3497536945812808, "acc_stderr": 0.033554009049695646, "acc_norm": 0.3497536945812808, "acc_norm_stderr": 0.033554009049695646 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.48, "acc_stderr": 0.050211673156867795, "acc_norm": 0.48, "acc_norm_stderr": 0.050211673156867795 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.6121212121212121, "acc_stderr": 0.03804913653971012, "acc_norm": 0.6121212121212121, "acc_norm_stderr": 0.03804913653971012 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.5555555555555556, "acc_stderr": 0.035402943770953675, "acc_norm": 0.5555555555555556, "acc_norm_stderr": 0.035402943770953675 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.538860103626943, "acc_stderr": 0.035975244117345775, "acc_norm": 0.538860103626943, "acc_norm_stderr": 0.035975244117345775 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.33589743589743587, "acc_stderr": 0.023946724741563976, "acc_norm": 0.33589743589743587, "acc_norm_stderr": 0.023946724741563976 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3111111111111111, "acc_stderr": 0.028226446749683515, "acc_norm": 0.3111111111111111, "acc_norm_stderr": 0.028226446749683515 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.03196876989195778, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.03196876989195778 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.2251655629139073, "acc_stderr": 0.03410435282008937, "acc_norm": 0.2251655629139073, "acc_norm_stderr": 0.03410435282008937 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.5596330275229358, "acc_stderr": 0.02128431062376155, "acc_norm": 0.5596330275229358, "acc_norm_stderr": 0.02128431062376155 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.2777777777777778, "acc_stderr": 0.030546745264953178, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.030546745264953178 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.43137254901960786, "acc_stderr": 0.03476099060501636, "acc_norm": 0.43137254901960786, "acc_norm_stderr": 0.03476099060501636 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.5991561181434599, "acc_stderr": 0.031900803894732356, "acc_norm": 0.5991561181434599, "acc_norm_stderr": 0.031900803894732356 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.5112107623318386, "acc_stderr": 0.033549366530984746, "acc_norm": 0.5112107623318386, "acc_norm_stderr": 0.033549366530984746 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.5190839694656488, "acc_stderr": 0.04382094705550988, "acc_norm": 0.5190839694656488, "acc_norm_stderr": 0.04382094705550988 }, "harness|hendrycksTest-international_law|5": { "acc": 0.6611570247933884, "acc_stderr": 0.043207678075366705, "acc_norm": 0.6611570247933884, "acc_norm_stderr": 0.043207678075366705 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.5, "acc_stderr": 0.04833682445228318, "acc_norm": 0.5, "acc_norm_stderr": 0.04833682445228318 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.4110429447852761, "acc_stderr": 0.038656978537853624, "acc_norm": 0.4110429447852761, "acc_norm_stderr": 0.038656978537853624 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4375, "acc_stderr": 0.04708567521880525, "acc_norm": 0.4375, "acc_norm_stderr": 0.04708567521880525 }, "harness|hendrycksTest-management|5": { "acc": 0.6504854368932039, "acc_stderr": 0.047211885060971716, "acc_norm": 0.6504854368932039, "acc_norm_stderr": 0.047211885060971716 }, "harness|hendrycksTest-marketing|5": { "acc": 0.7307692307692307, "acc_stderr": 0.029058588303748845, "acc_norm": 0.7307692307692307, "acc_norm_stderr": 0.029058588303748845 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.52, "acc_stderr": 0.05021167315686779, "acc_norm": 0.52, "acc_norm_stderr": 0.05021167315686779 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.5900383141762452, "acc_stderr": 0.017587672312336048, "acc_norm": 0.5900383141762452, "acc_norm_stderr": 0.017587672312336048 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.5289017341040463, "acc_stderr": 0.02687408588351835, "acc_norm": 0.5289017341040463, "acc_norm_stderr": 0.02687408588351835 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.24692737430167597, "acc_stderr": 0.01442229220480884, "acc_norm": 0.24692737430167597, "acc_norm_stderr": 0.01442229220480884 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.5588235294117647, "acc_stderr": 0.02843109544417664, "acc_norm": 0.5588235294117647, "acc_norm_stderr": 0.02843109544417664 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.43086816720257237, "acc_stderr": 0.028125340983972714, "acc_norm": 0.43086816720257237, "acc_norm_stderr": 0.028125340983972714 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.4722222222222222, "acc_stderr": 0.027777777777777804, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.027777777777777804 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028121636040639882, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028121636040639882 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.3468057366362451, "acc_stderr": 0.012156071332318713, "acc_norm": 0.3468057366362451, "acc_norm_stderr": 0.012156071332318713 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.3272058823529412, "acc_stderr": 0.02850145286039655, "acc_norm": 0.3272058823529412, "acc_norm_stderr": 0.02850145286039655 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.42483660130718953, "acc_stderr": 0.019997973035458336, "acc_norm": 0.42483660130718953, "acc_norm_stderr": 0.019997973035458336 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.5818181818181818, "acc_stderr": 0.04724577405731572, "acc_norm": 0.5818181818181818, "acc_norm_stderr": 0.04724577405731572 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.42448979591836733, "acc_stderr": 0.031642094879429414, "acc_norm": 0.42448979591836733, "acc_norm_stderr": 0.031642094879429414 }, "harness|hendrycksTest-sociology|5": { "acc": 0.5970149253731343, "acc_stderr": 0.034683432951111266, "acc_norm": 0.5970149253731343, "acc_norm_stderr": 0.034683432951111266 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.68, "acc_stderr": 0.046882617226215034, "acc_norm": 0.68, "acc_norm_stderr": 0.046882617226215034 }, "harness|hendrycksTest-virology|5": { "acc": 0.4036144578313253, "acc_stderr": 0.038194861407583984, "acc_norm": 0.4036144578313253, "acc_norm_stderr": 0.038194861407583984 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.5614035087719298, "acc_stderr": 0.038057975055904594, "acc_norm": 0.5614035087719298, "acc_norm_stderr": 0.038057975055904594 }, "harness|truthfulqa:mc|0": { "mc1": 0.2582619339045288, "mc1_stderr": 0.015321821688476196, "mc2": 0.4123912546191622, "mc2_stderr": 0.013946155781929979 }, "harness|winogrande|5": { "acc": 0.6156274664561957, "acc_stderr": 0.013671567600836196 }, "harness|gsm8k|5": { "acc": 0.31311599696739956, "acc_stderr": 0.01277428566938509 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2

2024-02-16T14:10:07+00:00

{"pretty_name": "Evaluation run of aloobun/Reyna-Mini-1.8B-v0.2", "dataset_summary": "Dataset automatically created during the evaluation run of model [aloobun/Reyna-Mini-1.8B-v0.2](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T14:08:02.040553](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2/blob/main/results_2024-02-16T14-08-02.040553.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\": 0.44626133629619946,\n \"acc_stderr\": 0.034425038440545834,\n \"acc_norm\": 0.4487075344673435,\n \"acc_norm_stderr\": 0.03514635225565978,\n \"mc1\": 0.2582619339045288,\n \"mc1_stderr\": 0.015321821688476196,\n \"mc2\": 0.4123912546191622,\n \"mc2_stderr\": 0.013946155781929979\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.34044368600682595,\n \"acc_stderr\": 0.01384746051889298,\n \"acc_norm\": 0.3660409556313993,\n \"acc_norm_stderr\": 0.014077223108470144\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.44692292372037445,\n \"acc_stderr\": 0.004961587574275628,\n \"acc_norm\": 0.6018721370244972,\n \"acc_norm_stderr\": 0.00488511646555027\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3851851851851852,\n \"acc_stderr\": 0.042039210401562783,\n \"acc_norm\": 0.3851851851851852,\n \"acc_norm_stderr\": 0.042039210401562783\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4342105263157895,\n \"acc_stderr\": 0.04033565667848319,\n \"acc_norm\": 0.4342105263157895,\n \"acc_norm_stderr\": 0.04033565667848319\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.47547169811320755,\n \"acc_stderr\": 0.030735822206205608,\n \"acc_norm\": 0.47547169811320755,\n \"acc_norm_stderr\": 0.030735822206205608\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4097222222222222,\n \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.4097222222222222,\n \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542126,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542126\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237103,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237103\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.43352601156069365,\n \"acc_stderr\": 0.037786210790920545,\n \"acc_norm\": 0.43352601156069365,\n \"acc_norm_stderr\": 0.037786210790920545\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\": 0.59,\n \"acc_stderr\": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|winogrande|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["results_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T14-08-02.040553.parquet"]}]}]}

2024-02-16T14:10:32+00:00

TAGS #region-us

# Dataset Card for Evaluation run of aloobun/Reyna-Mini-1.8B-v0.2 Dataset automatically created during the evaluation run of model aloobun/Reyna-Mini-1.8B-v0.2 on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-02-16T14:08:02.040553(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

0c321465e6cb4b24dde374e72228100b0fe012b3

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

Drewskidang/reformat

2024-02-16T14:10:52+00:00

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5719386, "num_examples": 21257}], "download_size": 2490392, "dataset_size": 5719386}}

2024-02-16T14:10:54+00:00

TAGS #region-us

# Dataset Card for "reformat" More Information needed

467cf4bd133e9b5d4a6a87227b42267811001e7e

# Dataset Card for Evaluation run of NovoCode/Tiger-DPO <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [NovoCode/Tiger-DPO](https://huggingface.co/NovoCode/Tiger-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_NovoCode__Tiger-DPO", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T14:49:32.301206](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Tiger-DPO/blob/main/results_2024-02-16T14-49-32.301206.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": 0.5958020167795036, "acc_stderr": 0.03336424247295358, "acc_norm": 0.6002192767446106, "acc_norm_stderr": 0.03404810463176771, "mc1": 0.32313341493268055, "mc1_stderr": 0.016371836286454604, "mc2": 0.507583773070451, "mc2_stderr": 0.014770823381787772 }, "harness|arc:challenge|25": { "acc": 0.43686006825938567, "acc_stderr": 0.014494421584256525, "acc_norm": 0.48208191126279865, "acc_norm_stderr": 0.01460200558549098 }, "harness|hellaswag|10": { "acc": 0.620991834295957, "acc_stderr": 0.004841486716855774, "acc_norm": 0.8181637124078869, "acc_norm_stderr": 0.0038492126228151665 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5777777777777777, "acc_stderr": 0.04266763404099582, "acc_norm": 0.5777777777777777, "acc_norm_stderr": 0.04266763404099582 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6447368421052632, "acc_stderr": 0.03894734487013317, "acc_norm": 0.6447368421052632, "acc_norm_stderr": 0.03894734487013317 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.53, "acc_stderr": 0.05016135580465919, "acc_norm": 0.53, "acc_norm_stderr": 0.05016135580465919 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6754716981132075, "acc_stderr": 0.02881561571343211, "acc_norm": 0.6754716981132075, "acc_norm_stderr": 0.02881561571343211 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.6388888888888888, "acc_stderr": 0.040166600304512336, "acc_norm": 0.6388888888888888, "acc_norm_stderr": 0.040166600304512336 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.41, "acc_stderr": 0.04943110704237102, "acc_norm": 0.41, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.45, "acc_stderr": 0.04999999999999999, "acc_norm": 0.45, "acc_norm_stderr": 0.04999999999999999 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.35, "acc_stderr": 0.047937248544110196, "acc_norm": 0.35, "acc_norm_stderr": 0.047937248544110196 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.6011560693641619, "acc_stderr": 0.037336266553835096, "acc_norm": 0.6011560693641619, "acc_norm_stderr": 0.037336266553835096 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.38235294117647056, "acc_stderr": 0.04835503696107223, "acc_norm": 0.38235294117647056, "acc_norm_stderr": 0.04835503696107223 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.71, "acc_stderr": 0.045604802157206845, "acc_norm": 0.71, "acc_norm_stderr": 0.045604802157206845 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.502127659574468, "acc_stderr": 0.032685726586674915, "acc_norm": 0.502127659574468, "acc_norm_stderr": 0.032685726586674915 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.45614035087719296, "acc_stderr": 0.046854730419077895, "acc_norm": 0.45614035087719296, "acc_norm_stderr": 0.046854730419077895 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.5448275862068965, "acc_stderr": 0.04149886942192117, "acc_norm": 0.5448275862068965, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3862433862433862, "acc_stderr": 0.025075981767601677, "acc_norm": 0.3862433862433862, "acc_norm_stderr": 0.025075981767601677 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.38095238095238093, "acc_stderr": 0.04343525428949098, "acc_norm": 0.38095238095238093, "acc_norm_stderr": 0.04343525428949098 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.38, "acc_stderr": 0.048783173121456316, "acc_norm": 0.38, "acc_norm_stderr": 0.048783173121456316 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6645161290322581, "acc_stderr": 0.026860206444724352, "acc_norm": 0.6645161290322581, "acc_norm_stderr": 0.026860206444724352 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.4876847290640394, "acc_stderr": 0.035169204442208966, "acc_norm": 0.4876847290640394, "acc_norm_stderr": 0.035169204442208966 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.59, "acc_stderr": 0.04943110704237102, "acc_norm": 0.59, "acc_norm_stderr": 0.04943110704237102 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7090909090909091, "acc_stderr": 0.03546563019624335, "acc_norm": 0.7090909090909091, "acc_norm_stderr": 0.03546563019624335 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7626262626262627, "acc_stderr": 0.0303137105381989, "acc_norm": 0.7626262626262627, "acc_norm_stderr": 0.0303137105381989 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.8238341968911918, "acc_stderr": 0.027493504244548057, "acc_norm": 0.8238341968911918, "acc_norm_stderr": 0.027493504244548057 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.5846153846153846, "acc_stderr": 0.02498535492310234, "acc_norm": 0.5846153846153846, "acc_norm_stderr": 0.02498535492310234 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.35555555555555557, "acc_stderr": 0.029185714949857413, "acc_norm": 0.35555555555555557, "acc_norm_stderr": 0.029185714949857413 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.6260504201680672, "acc_stderr": 0.031429466378837076, "acc_norm": 0.6260504201680672, "acc_norm_stderr": 0.031429466378837076 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.39072847682119205, "acc_stderr": 0.039837983066598075, "acc_norm": 0.39072847682119205, "acc_norm_stderr": 0.039837983066598075 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7926605504587156, "acc_stderr": 0.01738141556360868, "acc_norm": 0.7926605504587156, "acc_norm_stderr": 0.01738141556360868 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.49537037037037035, "acc_stderr": 0.03409825519163572, "acc_norm": 0.49537037037037035, "acc_norm_stderr": 0.03409825519163572 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7794117647058824, "acc_stderr": 0.02910225438967407, "acc_norm": 0.7794117647058824, "acc_norm_stderr": 0.02910225438967407 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7510548523206751, "acc_stderr": 0.028146970599422644, "acc_norm": 0.7510548523206751, "acc_norm_stderr": 0.028146970599422644 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6278026905829597, "acc_stderr": 0.032443052830087304, "acc_norm": 0.6278026905829597, "acc_norm_stderr": 0.032443052830087304 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7251908396946565, "acc_stderr": 0.03915345408847837, "acc_norm": 0.7251908396946565, "acc_norm_stderr": 0.03915345408847837 }, "harness|hendrycksTest-international_law|5": { "acc": 0.768595041322314, "acc_stderr": 0.03849856098794088, "acc_norm": 0.768595041322314, "acc_norm_stderr": 0.03849856098794088 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.7129629629629629, "acc_stderr": 0.043733130409147614, "acc_norm": 0.7129629629629629, "acc_norm_stderr": 0.043733130409147614 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.7116564417177914, "acc_stderr": 0.035590395316173425, "acc_norm": 0.7116564417177914, "acc_norm_stderr": 0.035590395316173425 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4017857142857143, "acc_stderr": 0.04653333146973647, "acc_norm": 0.4017857142857143, "acc_norm_stderr": 0.04653333146973647 }, "harness|hendrycksTest-management|5": { "acc": 0.7475728155339806, "acc_stderr": 0.04301250399690879, "acc_norm": 0.7475728155339806, "acc_norm_stderr": 0.04301250399690879 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8717948717948718, "acc_stderr": 0.021901905115073318, "acc_norm": 0.8717948717948718, "acc_norm_stderr": 0.021901905115073318 }, "harness|hendrycksTest-medical_genetics|5": { "acc": 0.63, "acc_stderr": 0.04852365870939099, "acc_norm": 0.63, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-miscellaneous|5": { "acc": 0.7841634738186463, "acc_stderr": 0.01471168438613996, "acc_norm": 0.7841634738186463, "acc_norm_stderr": 0.01471168438613996 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6763005780346821, "acc_stderr": 0.02519018132760842, "acc_norm": 0.6763005780346821, "acc_norm_stderr": 0.02519018132760842 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.38994413407821227, "acc_stderr": 0.01631237662921307, "acc_norm": 0.38994413407821227, "acc_norm_stderr": 0.01631237662921307 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6764705882352942, "acc_stderr": 0.026787453111906497, "acc_norm": 0.6764705882352942, "acc_norm_stderr": 0.026787453111906497 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6688102893890675, "acc_stderr": 0.026730620728004913, "acc_norm": 0.6688102893890675, "acc_norm_stderr": 0.026730620728004913 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.6635802469135802, "acc_stderr": 0.02628973494595293, "acc_norm": 0.6635802469135802, "acc_norm_stderr": 0.02628973494595293 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.43617021276595747, "acc_stderr": 0.02958345203628407, "acc_norm": 0.43617021276595747, "acc_norm_stderr": 0.02958345203628407 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4132985658409387, "acc_stderr": 0.012576779494860087, "acc_norm": 0.4132985658409387, "acc_norm_stderr": 0.012576779494860087 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6286764705882353, "acc_stderr": 0.029349803139765873, "acc_norm": 0.6286764705882353, "acc_norm_stderr": 0.029349803139765873 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.5931372549019608, "acc_stderr": 0.019873802005061173, "acc_norm": 0.5931372549019608, "acc_norm_stderr": 0.019873802005061173 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6636363636363637, "acc_stderr": 0.04525393596302505, "acc_norm": 0.6636363636363637, "acc_norm_stderr": 0.04525393596302505 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.689795918367347, "acc_stderr": 0.029613459872484378, "acc_norm": 0.689795918367347, "acc_norm_stderr": 0.029613459872484378 }, "harness|hendrycksTest-sociology|5": { "acc": 0.7860696517412935, "acc_stderr": 0.02899690969332891, "acc_norm": 0.7860696517412935, "acc_norm_stderr": 0.02899690969332891 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.84, "acc_stderr": 0.03684529491774709, "acc_norm": 0.84, "acc_norm_stderr": 0.03684529491774709 }, "harness|hendrycksTest-virology|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.7894736842105263, "acc_stderr": 0.03126781714663179, "acc_norm": 0.7894736842105263, "acc_norm_stderr": 0.03126781714663179 }, "harness|truthfulqa:mc|0": { "mc1": 0.32313341493268055, "mc1_stderr": 0.016371836286454604, "mc2": 0.507583773070451, "mc2_stderr": 0.014770823381787772 }, "harness|winogrande|5": { "acc": 0.7632202052091555, "acc_stderr": 0.011947592365207397 }, "harness|gsm8k|5": { "acc": 0.41015921152388174, "acc_stderr": 0.013548335117860353 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More 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It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). 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open-llm-leaderboard/details_NovoCode__Tiger-DPO

2024-02-16T14:11:00+00:00

{"pretty_name": "Evaluation run of NovoCode/Tiger-DPO", "dataset_summary": "Dataset automatically created during the evaluation run of model [NovoCode/Tiger-DPO](https://huggingface.co/NovoCode/Tiger-DPO) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NovoCode__Tiger-DPO\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T14:49:32.301206](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Tiger-DPO/blob/main/results_2024-02-16T14-49-32.301206.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\": 0.5958020167795036,\n \"acc_stderr\": 0.03336424247295358,\n \"acc_norm\": 0.6002192767446106,\n \"acc_norm_stderr\": 0.03404810463176771,\n \"mc1\": 0.32313341493268055,\n \"mc1_stderr\": 0.016371836286454604,\n \"mc2\": 0.507583773070451,\n \"mc2_stderr\": 0.014770823381787772\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.43686006825938567,\n \"acc_stderr\": 0.014494421584256525,\n \"acc_norm\": 0.48208191126279865,\n \"acc_norm_stderr\": 0.01460200558549098\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.620991834295957,\n \"acc_stderr\": 0.004841486716855774,\n \"acc_norm\": 0.8181637124078869,\n \"acc_norm_stderr\": 0.0038492126228151665\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.03894734487013317,\n \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.03894734487013317\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.040166600304512336,\n \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.040166600304512336\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6011560693641619,\n \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.6011560693641619,\n \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.032685726586674915,\n \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.032685726586674915\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3862433862433862,\n \"acc_stderr\": 0.025075981767601677,\n \"acc_norm\": 0.3862433862433862,\n \"acc_norm_stderr\": 0.025075981767601677\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6645161290322581,\n \"acc_stderr\": 0.026860206444724352,\n \"acc_norm\": 0.6645161290322581,\n \"acc_norm_stderr\": 0.026860206444724352\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624335,\n \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624335\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.027493504244548057,\n \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.027493504244548057\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5846153846153846,\n \"acc_stderr\": 0.02498535492310234,\n \"acc_norm\": 0.5846153846153846,\n \"acc_norm_stderr\": 0.02498535492310234\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857413,\n \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857413\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6260504201680672,\n \"acc_stderr\": 0.031429466378837076,\n \"acc_norm\": 0.6260504201680672,\n \"acc_norm_stderr\": 0.031429466378837076\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7926605504587156,\n \"acc_stderr\": 0.01738141556360868,\n \"acc_norm\": 0.7926605504587156,\n \"acc_norm_stderr\": 0.01738141556360868\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967407,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967407\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7510548523206751,\n \"acc_stderr\": 0.028146970599422644,\n \"acc_norm\": 0.7510548523206751,\n \"acc_norm_stderr\": 0.028146970599422644\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847837,\n \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847837\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7116564417177914,\n \"acc_stderr\": 0.035590395316173425,\n \"acc_norm\": 0.7116564417177914,\n \"acc_norm_stderr\": 0.035590395316173425\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n \"acc_stderr\": 0.04653333146973647,\n \"acc_norm\": 0.4017857142857143,\n \"acc_norm_stderr\": 0.04653333146973647\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690879,\n \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690879\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n \"acc_stderr\": 0.021901905115073318,\n \"acc_norm\": 0.8717948717948718,\n \"acc_norm_stderr\": 0.021901905115073318\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7841634738186463,\n \"acc_stderr\": 0.01471168438613996,\n \"acc_norm\": 0.7841634738186463,\n \"acc_norm_stderr\": 0.01471168438613996\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.02519018132760842,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.02519018132760842\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.38994413407821227,\n \"acc_stderr\": 0.01631237662921307,\n \"acc_norm\": 0.38994413407821227,\n \"acc_norm_stderr\": 0.01631237662921307\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.026787453111906497,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.026787453111906497\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6688102893890675,\n \"acc_stderr\": 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2024-02-16T14:51:54+00:00

TAGS #region-us

# Dataset Card for Evaluation run of NovoCode/Tiger-DPO Dataset automatically created during the evaluation run of model NovoCode/Tiger-DPO on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-02-16T14:49:32.301206(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

d2fb0045bf6f4b4d160d95f53626d05905a8878e

# Dataset Card for Evaluation run of CorticalStack/mistral-7b-openhermes-sft <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [CorticalStack/mistral-7b-openhermes-sft](https://huggingface.co/CorticalStack/mistral-7b-openhermes-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)). To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_CorticalStack__mistral-7b-openhermes-sft", "harness_winogrande_5", split="train") ``` ## Latest results These are the [latest results from run 2024-02-16T14:13:04.061725](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-openhermes-sft/blob/main/results_2024-02-16T14-13-04.061725.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": 0.6070598204374664, "acc_stderr": 0.03297690039129263, "acc_norm": 0.6130046390646828, "acc_norm_stderr": 0.033660154914381686, "mc1": 0.31456548347613217, "mc1_stderr": 0.016255241993179185, "mc2": 0.4630793817398098, "mc2_stderr": 0.014741207245405565 }, "harness|arc:challenge|25": { "acc": 0.5776450511945392, "acc_stderr": 0.01443413871337998, "acc_norm": 0.60580204778157, "acc_norm_stderr": 0.01428052266746732 }, "harness|hellaswag|10": { "acc": 0.6233817964548894, "acc_stderr": 0.004835475957610925, "acc_norm": 0.8200557657837084, "acc_norm_stderr": 0.003833559228158668 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.5703703703703704, "acc_stderr": 0.042763494943765995, "acc_norm": 0.5703703703703704, "acc_norm_stderr": 0.042763494943765995 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.6381578947368421, "acc_stderr": 0.039105257528497236, "acc_norm": 0.6381578947368421, "acc_norm_stderr": 0.039105257528497236 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.54, "acc_stderr": 0.05009082659620333, "acc_norm": 0.54, "acc_norm_stderr": 0.05009082659620333 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.6830188679245283, "acc_stderr": 0.02863723563980089, "acc_norm": 0.6830188679245283, "acc_norm_stderr": 0.02863723563980089 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.7013888888888888, "acc_stderr": 0.03827052357950756, "acc_norm": 0.7013888888888888, "acc_norm_stderr": 0.03827052357950756 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.42, "acc_stderr": 0.049604496374885836, "acc_norm": 0.42, "acc_norm_stderr": 0.049604496374885836 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.52, "acc_stderr": 0.05021167315686779, "acc_norm": 0.52, "acc_norm_stderr": 0.05021167315686779 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.3, "acc_stderr": 0.046056618647183814, "acc_norm": 0.3, "acc_norm_stderr": 0.046056618647183814 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.5780346820809249, "acc_stderr": 0.0376574669386515, "acc_norm": 0.5780346820809249, "acc_norm_stderr": 0.0376574669386515 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.4117647058823529, "acc_stderr": 0.048971049527263666, "acc_norm": 0.4117647058823529, "acc_norm_stderr": 0.048971049527263666 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.74, "acc_stderr": 0.04408440022768077, "acc_norm": 0.74, "acc_norm_stderr": 0.04408440022768077 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.5404255319148936, "acc_stderr": 0.03257901482099835, "acc_norm": 0.5404255319148936, "acc_norm_stderr": 0.03257901482099835 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.4824561403508772, "acc_stderr": 0.04700708033551038, "acc_norm": 0.4824561403508772, "acc_norm_stderr": 0.04700708033551038 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.593103448275862, "acc_stderr": 0.04093793981266236, "acc_norm": 0.593103448275862, "acc_norm_stderr": 0.04093793981266236 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.3994708994708995, "acc_stderr": 0.025225450284067884, "acc_norm": 0.3994708994708995, "acc_norm_stderr": 0.025225450284067884 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.42063492063492064, "acc_stderr": 0.04415438226743744, "acc_norm": 0.42063492063492064, "acc_norm_stderr": 0.04415438226743744 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.37, "acc_stderr": 0.048523658709391, "acc_norm": 0.37, "acc_norm_stderr": 0.048523658709391 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.7096774193548387, "acc_stderr": 0.02582210611941589, "acc_norm": 0.7096774193548387, "acc_norm_stderr": 0.02582210611941589 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.46798029556650245, "acc_stderr": 0.03510766597959215, "acc_norm": 0.46798029556650245, "acc_norm_stderr": 0.03510766597959215 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.65, "acc_stderr": 0.04793724854411019, "acc_norm": 0.65, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.7333333333333333, "acc_stderr": 0.03453131801885417, "acc_norm": 0.7333333333333333, "acc_norm_stderr": 0.03453131801885417 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.7676767676767676, "acc_stderr": 0.030088629490217487, "acc_norm": 0.7676767676767676, "acc_norm_stderr": 0.030088629490217487 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.844559585492228, "acc_stderr": 0.026148483469153303, "acc_norm": 0.844559585492228, "acc_norm_stderr": 0.026148483469153303 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.6282051282051282, "acc_stderr": 0.024503472557110943, "acc_norm": 0.6282051282051282, "acc_norm_stderr": 0.024503472557110943 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.3333333333333333, "acc_stderr": 0.028742040903948485, "acc_norm": 0.3333333333333333, "acc_norm_stderr": 0.028742040903948485 }, "harness|hendrycksTest-high_school_microeconomics|5": { "acc": 0.592436974789916, "acc_stderr": 0.03191863374478466, "acc_norm": 0.592436974789916, "acc_norm_stderr": 0.03191863374478466 }, "harness|hendrycksTest-high_school_physics|5": { "acc": 0.3509933774834437, "acc_stderr": 0.03896981964257375, "acc_norm": 0.3509933774834437, "acc_norm_stderr": 0.03896981964257375 }, "harness|hendrycksTest-high_school_psychology|5": { "acc": 0.7779816513761468, "acc_stderr": 0.017818849564796634, "acc_norm": 0.7779816513761468, "acc_norm_stderr": 0.017818849564796634 }, "harness|hendrycksTest-high_school_statistics|5": { "acc": 0.4722222222222222, "acc_stderr": 0.0340470532865388, "acc_norm": 0.4722222222222222, "acc_norm_stderr": 0.0340470532865388 }, "harness|hendrycksTest-high_school_us_history|5": { "acc": 0.7647058823529411, "acc_stderr": 0.029771775228145624, "acc_norm": 0.7647058823529411, "acc_norm_stderr": 0.029771775228145624 }, "harness|hendrycksTest-high_school_world_history|5": { "acc": 0.7552742616033755, "acc_stderr": 0.027985699387036423, "acc_norm": 0.7552742616033755, "acc_norm_stderr": 0.027985699387036423 }, "harness|hendrycksTest-human_aging|5": { "acc": 0.6502242152466368, "acc_stderr": 0.03200736719484503, "acc_norm": 0.6502242152466368, "acc_norm_stderr": 0.03200736719484503 }, "harness|hendrycksTest-human_sexuality|5": { "acc": 0.7709923664122137, "acc_stderr": 0.036853466317118506, "acc_norm": 0.7709923664122137, "acc_norm_stderr": 0.036853466317118506 }, "harness|hendrycksTest-international_law|5": { "acc": 0.7851239669421488, "acc_stderr": 0.037494924487096966, "acc_norm": 0.7851239669421488, "acc_norm_stderr": 0.037494924487096966 }, "harness|hendrycksTest-jurisprudence|5": { "acc": 0.6944444444444444, "acc_stderr": 0.04453197507374984, "acc_norm": 0.6944444444444444, "acc_norm_stderr": 0.04453197507374984 }, "harness|hendrycksTest-logical_fallacies|5": { "acc": 0.6993865030674846, "acc_stderr": 0.03602511318806771, "acc_norm": 0.6993865030674846, "acc_norm_stderr": 0.03602511318806771 }, "harness|hendrycksTest-machine_learning|5": { "acc": 0.4107142857142857, "acc_stderr": 0.04669510663875191, "acc_norm": 0.4107142857142857, "acc_norm_stderr": 0.04669510663875191 }, "harness|hendrycksTest-management|5": { "acc": 0.7572815533980582, "acc_stderr": 0.04245022486384495, "acc_norm": 0.7572815533980582, "acc_norm_stderr": 0.04245022486384495 }, "harness|hendrycksTest-marketing|5": { "acc": 0.8589743589743589, "acc_stderr": 0.02280138253459754, "acc_norm": 0.8589743589743589, "acc_norm_stderr": 0.02280138253459754 }, "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": 0.8020434227330779, "acc_stderr": 0.01424887354921758, "acc_norm": 0.8020434227330779, "acc_norm_stderr": 0.01424887354921758 }, "harness|hendrycksTest-moral_disputes|5": { "acc": 0.6705202312138728, "acc_stderr": 0.025305258131879716, "acc_norm": 0.6705202312138728, "acc_norm_stderr": 0.025305258131879716 }, "harness|hendrycksTest-moral_scenarios|5": { "acc": 0.27039106145251396, "acc_stderr": 0.014854993938010073, "acc_norm": 0.27039106145251396, "acc_norm_stderr": 0.014854993938010073 }, "harness|hendrycksTest-nutrition|5": { "acc": 0.6830065359477124, "acc_stderr": 0.026643278474508755, "acc_norm": 0.6830065359477124, "acc_norm_stderr": 0.026643278474508755 }, "harness|hendrycksTest-philosophy|5": { "acc": 0.6881028938906752, "acc_stderr": 0.02631185807185416, "acc_norm": 0.6881028938906752, "acc_norm_stderr": 0.02631185807185416 }, "harness|hendrycksTest-prehistory|5": { "acc": 0.7067901234567902, "acc_stderr": 0.025329888171900922, "acc_norm": 0.7067901234567902, "acc_norm_stderr": 0.025329888171900922 }, "harness|hendrycksTest-professional_accounting|5": { "acc": 0.45390070921985815, "acc_stderr": 0.029700453247291463, "acc_norm": 0.45390070921985815, "acc_norm_stderr": 0.029700453247291463 }, "harness|hendrycksTest-professional_law|5": { "acc": 0.4380704041720991, "acc_stderr": 0.012671902782567654, "acc_norm": 0.4380704041720991, "acc_norm_stderr": 0.012671902782567654 }, "harness|hendrycksTest-professional_medicine|5": { "acc": 0.6470588235294118, "acc_stderr": 0.029029422815681393, "acc_norm": 0.6470588235294118, "acc_norm_stderr": 0.029029422815681393 }, "harness|hendrycksTest-professional_psychology|5": { "acc": 0.6176470588235294, "acc_stderr": 0.01965992249362335, "acc_norm": 0.6176470588235294, "acc_norm_stderr": 0.01965992249362335 }, "harness|hendrycksTest-public_relations|5": { "acc": 0.6545454545454545, "acc_stderr": 0.04554619617541054, "acc_norm": 0.6545454545454545, "acc_norm_stderr": 0.04554619617541054 }, "harness|hendrycksTest-security_studies|5": { "acc": 0.7061224489795919, "acc_stderr": 0.02916273841024977, "acc_norm": 0.7061224489795919, "acc_norm_stderr": 0.02916273841024977 }, "harness|hendrycksTest-sociology|5": { "acc": 0.845771144278607, "acc_stderr": 0.02553843336857833, "acc_norm": 0.845771144278607, "acc_norm_stderr": 0.02553843336857833 }, "harness|hendrycksTest-us_foreign_policy|5": { "acc": 0.86, "acc_stderr": 0.034873508801977704, "acc_norm": 0.86, "acc_norm_stderr": 0.034873508801977704 }, "harness|hendrycksTest-virology|5": { "acc": 0.4819277108433735, "acc_stderr": 0.038899512528272166, "acc_norm": 0.4819277108433735, "acc_norm_stderr": 0.038899512528272166 }, "harness|hendrycksTest-world_religions|5": { "acc": 0.8011695906432749, "acc_stderr": 0.030611116557432528, "acc_norm": 0.8011695906432749, "acc_norm_stderr": 0.030611116557432528 }, "harness|truthfulqa:mc|0": { "mc1": 0.31456548347613217, "mc1_stderr": 0.016255241993179185, "mc2": 0.4630793817398098, "mc2_stderr": 0.014741207245405565 }, "harness|winogrande|5": { "acc": 0.7758484609313339, "acc_stderr": 0.011720400740774104 }, "harness|gsm8k|5": { "acc": 0.312357846853677, "acc_stderr": 0.012765850404191427 } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]

open-llm-leaderboard/details_CorticalStack__mistral-7b-openhermes-sft

2024-02-16T14:15:25+00:00

{"pretty_name": "Evaluation run of CorticalStack/mistral-7b-openhermes-sft", "dataset_summary": "Dataset automatically created during the evaluation run of model [CorticalStack/mistral-7b-openhermes-sft](https://huggingface.co/CorticalStack/mistral-7b-openhermes-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CorticalStack__mistral-7b-openhermes-sft\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2024-02-16T14:13:04.061725](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-openhermes-sft/blob/main/results_2024-02-16T14-13-04.061725.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\": 0.6070598204374664,\n \"acc_stderr\": 0.03297690039129263,\n \"acc_norm\": 0.6130046390646828,\n \"acc_norm_stderr\": 0.033660154914381686,\n \"mc1\": 0.31456548347613217,\n \"mc1_stderr\": 0.016255241993179185,\n \"mc2\": 0.4630793817398098,\n \"mc2_stderr\": 0.014741207245405565\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5776450511945392,\n \"acc_stderr\": 0.01443413871337998,\n \"acc_norm\": 0.60580204778157,\n \"acc_norm_stderr\": 0.01428052266746732\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6233817964548894,\n \"acc_stderr\": 0.004835475957610925,\n \"acc_norm\": 0.8200557657837084,\n \"acc_norm_stderr\": 0.003833559228158668\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.039105257528497236,\n \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.039105257528497236\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5780346820809249,\n \"acc_stderr\": 0.0376574669386515,\n \"acc_norm\": 0.5780346820809249,\n \"acc_norm_stderr\": 0.0376574669386515\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3994708994708995,\n \"acc_stderr\": 0.025225450284067884,\n \"acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.025225450284067884\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7096774193548387,\n \"acc_stderr\": 0.02582210611941589,\n \"acc_norm\": 0.7096774193548387,\n \"acc_norm_stderr\": 0.02582210611941589\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.03510766597959215,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.03510766597959215\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.844559585492228,\n \"acc_stderr\": 0.026148483469153303,\n \"acc_norm\": 0.844559585492228,\n \"acc_norm_stderr\": 0.026148483469153303\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6282051282051282,\n \"acc_stderr\": 0.024503472557110943,\n \"acc_norm\": 0.6282051282051282,\n \"acc_norm_stderr\": 0.024503472557110943\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948485,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948485\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.592436974789916,\n \"acc_stderr\": 0.03191863374478466,\n \"acc_norm\": 0.592436974789916,\n \"acc_norm_stderr\": 0.03191863374478466\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7779816513761468,\n \"acc_stderr\": 0.017818849564796634,\n \"acc_norm\": 0.7779816513761468,\n \"acc_norm_stderr\": 0.017818849564796634\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145624,\n \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145624\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6502242152466368,\n \"acc_stderr\": 0.03200736719484503,\n \"acc_norm\": 0.6502242152466368,\n \"acc_norm_stderr\": 0.03200736719484503\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.04453197507374984,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.04453197507374984\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4107142857142857,\n \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.4107142857142857,\n \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n \"acc_stderr\": 0.02280138253459754,\n \"acc_norm\": 0.8589743589743589,\n \"acc_norm_stderr\": 0.02280138253459754\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\": 0.8020434227330779,\n \"acc_stderr\": 0.01424887354921758,\n \"acc_norm\": 0.8020434227330779,\n \"acc_norm_stderr\": 0.01424887354921758\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6705202312138728,\n \"acc_stderr\": 0.025305258131879716,\n \"acc_norm\": 0.6705202312138728,\n \"acc_norm_stderr\": 0.025305258131879716\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.27039106145251396,\n \"acc_stderr\": 0.014854993938010073,\n \"acc_norm\": 0.27039106145251396,\n \"acc_norm_stderr\": 0.014854993938010073\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n \"acc_stderr\": 0.02631185807185416,\n \"acc_norm\": 0.6881028938906752,\n \"acc_norm_stderr\": 0.02631185807185416\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.025329888171900922,\n \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.025329888171900922\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.45390070921985815,\n \"acc_stderr\": 0.029700453247291463,\n \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.029700453247291463\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4380704041720991,\n \"acc_stderr\": 0.012671902782567654,\n \"acc_norm\": 0.4380704041720991,\n \"acc_norm_stderr\": 0.012671902782567654\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.029029422815681393,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.029029422815681393\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.01965992249362335,\n \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.01965992249362335\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.02916273841024977,\n \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.02916273841024977\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.02553843336857833,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.02553843336857833\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.31456548347613217,\n \"mc1_stderr\": 0.016255241993179185,\n \"mc2\": 0.4630793817398098,\n \"mc2_stderr\": 0.014741207245405565\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7758484609313339,\n \"acc_stderr\": 0.011720400740774104\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.312357846853677,\n \"acc_stderr\": 0.012765850404191427\n }\n}\n```", "repo_url": "https://huggingface.co/CorticalStack/mistral-7b-openhermes-sft", "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": "2024_02_16T14_13_04.061725", "path": ["**/details_harness|arc:challenge|25_2024-02-16T14-13-04.061725.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-16T14-13-04.061725.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_16T14_13_04.061725", "path": ["**/details_harness|gsm8k|5_2024-02-16T14-13-04.061725.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-16T14-13-04.061725.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_16T14_13_04.061725", "path": ["**/details_harness|hellaswag|10_2024-02-16T14-13-04.061725.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-16T14-13-04.061725.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_16T14_13_04.061725", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T14-13-04.061725.parquet", "**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T14-13-04.061725.parquet", 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2024-02-16T14:15:48+00:00

TAGS #region-us

# Dataset Card for Evaluation run of CorticalStack/mistral-7b-openhermes-sft Dataset automatically created during the evaluation run of model CorticalStack/mistral-7b-openhermes-sft on the Open LLM Leaderboard. The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard). To load the details from a run, you can for instance do the following: ## Latest results These are the latest results from run 2024-02-16T14:13:04.061725(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): ## Dataset Details ### Dataset Description - Curated by: - Funded by [optional]: - Shared by [optional]: - Language(s) (NLP): - License: ### Dataset Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Out-of-Scope Use ## Dataset Structure ## Dataset Creation ### Curation Rationale ### Source Data #### Data Collection and Processing #### Who are the source data producers? ### Annotations [optional] #### Annotation process #### Who are the annotators? #### Personal and Sensitive Information ## Bias, Risks, and Limitations ### Recommendations Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Dataset Card Authors [optional] ## Dataset Card Contact

7eadbd8948d5b6ddda5948c349994bfa2294c1ef

## The annotations are in the **annotations_coco.json** file

saberder/humpback

2024-02-16T14:15:58+00:00

{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["object-detection", "image-classification"], "pretty_name": "Humpback", "tags": ["biology"]}

2024-02-16T16:09:40+00:00

TAGS #task_categories-object-detection #task_categories-image-classification #size_categories-1K<n<10K #language-English #biology #region-us

## The annotations are in the annotations_coco.json file