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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | [
"license:cc-by-nc-4.0",
"region:us"
] | 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
| [
"## Description\n\nWeather. Channel.",
"## Model\n\nSVD",
"## Voice\n\nJulian",
"# Tags\n\n- News",
"# Style\n\ngroundhog, live tv channel, weather news report, tv studio",
"# Music\n\nsoft breaking news intro",
"## Prompt\n\nGroundhog TV is an AI tube channel generating videos to summarize the weather forecast of the day.\nThe channel should keep the tone light, eventually making joke depending on the weather, sun, rain etc"
] | [
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"## Description\n\nWeather. Channel.",
"## Model\n\nSVD",
"## Voice\n\nJulian",
"# Tags\n\n- News",
"# Style\n\ngroundhog, live tv channel, weather news report, tv studio",
"# Music\n\nsoft breaking news intro",
"## Prompt\n\nGroundhog TV is an AI tube channel generating videos to summarize the weather forecast of the day.\nThe channel should keep the tone light, eventually making joke depending on the weather, sun, rain etc"
] | [
17,
6,
4,
3,
4,
16,
6,
48
] | [
"passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n## Description\n\nWeather. Channel.## Model\n\nSVD## Voice\n\nJulian# Tags\n\n- News# Style\n\ngroundhog, live tv channel, weather news report, tv studio# Music\n\nsoft breaking news intro## Prompt\n\nGroundhog TV is an AI tube channel generating videos to summarize the weather forecast of the day.\nThe channel should keep the tone light, eventually making joke depending on the weather, sun, rain etc"
] | [
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] |
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 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | 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 | [] | [
"en"
] | 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.
| [
"# Function Invocation and Time-Based Greeting Dataset\n\nThis 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."
] | [
"TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n",
"# Function Invocation and Time-Based Greeting Dataset\n\nThis 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."
] | [
51,
183
] | [
"passage: TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# Function Invocation and Time-Based Greeting Dataset\n\nThis 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."
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"filtered_wikibook\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"filtered_wikibook\"\n\nMore Information needed"
] | [
6,
15
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"filtered_wikibook\"\n\nMore Information needed"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:---------------|:------------|:-------|:-------------|:--------------------|:------------|:--------------------|:--------|:----------------|:-------|:-----------|:--------------------|:-------------------|:----------------|:------------|:-------------|:--------------|:--------------|:-----------------|:--------------------|:-----------------|:---------------|:------------|:---------|:-----------------|:-----------|:--------|:--------|:-------------|:--------|:-------------|:--------|:-------------|:----------------------|:----------|:-----------|:-----------------------------|:------------------|:--------|:---------------|:-------|:--------|:----------------|:------------|:-----------|:----------------|:------------|:--------|:---------------|:----------------|:--------------|:-----------------|:----------------|:--------------|:----------------|:--------|:-----------|:----------------------|:-----------|:-----------|:-----------|:------------------|:-----------------|:---------------|:------------|:-----------------|:---------------|:----------------|:--------|:--------|:-----------|:-----------|:---------|:------|:--------|:--------|:----------|:--------------|:-----------|:----------------|:-------------|:--------------|:-------------|:----------|:----------|:-----------|:------------------|:------|:----------------------|:----------|:-------|:---------|:--------|:----------|:-------------|:---------------|:-------------------|:--------------|:-------------|:--------------|:--------------|:--------------|:----------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | X | X | X | X | | 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/eirik_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | [
"language:en",
"license:mit",
"region:us"
] | 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 | [] | [
"en"
] | 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. | [] | [
"TAGS\n#language-English #license-mit #region-us \n"
] | [
15
] | [
"passage: TAGS\n#language-English #license-mit #region-us \n"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 11 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | |
| 12 | 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/paola_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-generation",
"license:mit",
"region:us"
] | 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.
| [] | [
"TAGS\n#task_categories-text-generation #license-mit #region-us \n"
] | [
22
] | [
"passage: TAGS\n#task_categories-text-generation #license-mit #region-us \n"
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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 | [
"task_categories:time-series-forecasting",
"arxiv:2206.15474",
"region:us"
] | 2024-01-17T12:05:02+00:00 | {"task_categories": ["time-series-forecasting"]} | 2024-01-23T11:34:41+00:00 | [
"2206.15474"
] | [] | 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}
} | [
"# Forecasting Future World Events with Neural Networks\n\nThis is an (unofficial) repository for \"Forecasting Future World Events with Neural Networks\" \nby Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks.\n\n<img align=\"center\" src=\"assets/URL\" width=\"750\">",
"## Introduction\n\nForecasting 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\n\nThe 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'.\n\nEach question has the following fields:\n \n\nThe authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only.",
"## IntervalQA Dataset\n\nMotivated 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.\n\nDownload the IntervalQA dataset here.\n\nIf you find this useful in your research, please consider citing:\n\n @article{zouforecasting2022,\n title={Forecasting Future World Events with Neural Networks},\n 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},\n journal={NeurIPS},\n year={2022}\n }"
] | [
"TAGS\n#task_categories-time-series-forecasting #arxiv-2206.15474 #region-us \n",
"# Forecasting Future World Events with Neural Networks\n\nThis is an (unofficial) repository for \"Forecasting Future World Events with Neural Networks\" \nby Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks.\n\n<img align=\"center\" src=\"assets/URL\" width=\"750\">",
"## Introduction\n\nForecasting 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\n\nThe 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'.\n\nEach question has the following fields:\n \n\nThe authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only.",
"## IntervalQA Dataset\n\nMotivated 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.\n\nDownload the IntervalQA dataset here.\n\nIf you find this useful in your research, please consider citing:\n\n @article{zouforecasting2022,\n title={Forecasting Future World Events with Neural Networks},\n 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},\n journal={NeurIPS},\n year={2022}\n }"
] | [
29,
103,
227,
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] | [
"passage: TAGS\n#task_categories-time-series-forecasting #arxiv-2206.15474 #region-us \n# Forecasting Future World Events with Neural Networks\n\nThis is an (unofficial) repository for \"Forecasting Future World Events with Neural Networks\" \nby Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks.\n\n<img align=\"center\" src=\"assets/URL\" width=\"750\">## Introduction\n\nForecasting 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\n\nThe 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'.\n\nEach question has the following fields:\n \n\nThe authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only."
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"20000-50000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"20000-50000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed"
] | [
6,
33
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"20000-50000-ultrafeedback-binarized-preferences-cleaned-ita\"\n\nMore Information needed"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"language:zh",
"license:mit",
"region:us"
] | 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 | [] | [
"zh"
] | 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 | [
"# LIMA数据集中文版",
"## 数据集构造方法\n- 使用GPT-4-Turbo将原始LIMA数据集的问题部分翻译为中文。\n- 使用GPT-4-Turbo回答翻译后的问题。\n- 注意，本数据集不包含原始LIMA数据集的多轮问答部分"
] | [
"TAGS\n#language-Chinese #license-mit #region-us \n",
"# LIMA数据集中文版",
"## 数据集构造方法\n- 使用GPT-4-Turbo将原始LIMA数据集的问题部分翻译为中文。\n- 使用GPT-4-Turbo回答翻译后的问题。\n- 注意，本数据集不包含原始LIMA数据集的多轮问答部分"
] | [
16,
7,
60
] | [
"passage: TAGS\n#language-Chinese #license-mit #region-us \n# LIMA数据集中文版## 数据集构造方法\n- 使用GPT-4-Turbo将原始LIMA数据集的问题部分翻译为中文。\n- 使用GPT-4-Turbo回答翻译后的问题。\n- 注意，本数据集不包含原始LIMA数据集的多轮问答部分"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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,
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"acc_norm": 0.28,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_Cartinoe5930__iDUS | [
"region:us"
] | 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\": 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#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
| [
"# Dataset Card for Evaluation run of Cartinoe5930/iDUS\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/iDUS on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Cartinoe5930/iDUS\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/iDUS on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Cartinoe5930/iDUS\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/iDUS on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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] |
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 | [
"region:us"
] | 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 | [] | [
"TAGS\n#region-us \n"
] | [
6
] | [
"passage: TAGS\n#region-us \n"
] | [
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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
{
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"mc2": 0.6934208246675816,
"mc2_stderr": 0.014891018416465928
},
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},
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},
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"harness|hendrycksTest-high_school_us_history|5": {
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}
}
```
## 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_zhengr__MixTAO-7Bx2-MoE-DPO | [
"region:us"
] | 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", "**/details_harness|hendrycksTest-anatomy|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-17T13-23-29.676681.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-17T13-23-29.676681.parquet", 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#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
| [
"# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-DPO\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-DPO on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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",
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"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-DPO\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-DPO on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of zhengr/MixTAO-7Bx2-MoE-DPO\n\n\n\nDataset automatically created during the evaluation run of model zhengr/MixTAO-7Bx2-MoE-DPO on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"VietnameseBookCorpus-raw-parquet\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"VietnameseBookCorpus-raw-parquet\"\n\nMore Information needed"
] | [
6,
22
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"VietnameseBookCorpus-raw-parquet\"\n\nMore Information needed"
] | [
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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 | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:pt",
"license:other",
"portuguese",
"language-modeling",
"arxiv:2401.16640",
"arxiv:2112.11446",
"region:us"
] | 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 | [
"2401.16640",
"2112.11446"
] | [
"pt"
] | 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!
| [
"# Portuguese-Corpus",
"## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese\n- Point of Contact: AIRES at PUCRS",
"### Dataset Summary\n\nPortuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the Hub.\n\nIn 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\n\nThis dataset can be utilized for tasks involving language modeling.",
"### Languages\n\nPortuguese.",
"## Dataset Structure",
"### Data Instances\n\nThe dataset consists of the following features:\n\n- text: a string of text in Portuguese.\n- metadata: the source where that string originated.",
"### Data Fields",
"### Data Splits\n\nAvailable splits are 'train'.",
"## Dataset Creation",
"### Curation Rationale\n\nThis 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\n\nWe 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?\n\nAll 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\n\nPortuguese-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?\n\nNicholas Kluge Corrêa.",
"### Personal and Sensitive Information\n\nThis 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\n\nThe 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\n\nThe 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\n\nA 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\n\nNicholas Kluge Corrêa.",
"### Licensing Information\n\nThe following datasets (_only training splits are a part of the corpus_) and respective licenses form the Portuguese-Corpus:\n\n- Wikipedia (License: CC BY-SA 3.0)\n\n- CulturaX (License: ODC-By, cc0-1.0)\n\n- OSCAR (License: cc0-1.0)\n\n- CCc100 (License: Common Crawl terms of use)\n\n- Roots Wikiquote (License: CC BY-SA 3.0)\n\n- Roots Ted Talks (License: CC BY-NC-ND 4.0)",
"### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!"
] | [
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Portuguese #license-other #portuguese #language-modeling #arxiv-2401.16640 #arxiv-2112.11446 #region-us \n",
"# Portuguese-Corpus",
"## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese\n- Point of Contact: AIRES at PUCRS",
"### Dataset Summary\n\nPortuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the Hub.\n\nIn 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\n\nThis dataset can be utilized for tasks involving language modeling.",
"### Languages\n\nPortuguese.",
"## Dataset Structure",
"### Data Instances\n\nThe dataset consists of the following features:\n\n- text: a string of text in Portuguese.\n- metadata: the source where that string originated.",
"### Data Fields",
"### Data Splits\n\nAvailable splits are 'train'.",
"## Dataset Creation",
"### Curation Rationale\n\nThis 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\n\nWe 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?\n\nAll 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\n\nPortuguese-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?\n\nNicholas Kluge Corrêa.",
"### Personal and Sensitive Information\n\nThis 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\n\nThe 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\n\nThe 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\n\nA 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\n\nNicholas Kluge Corrêa.",
"### Licensing Information\n\nThe following datasets (_only training splits are a part of the corpus_) and respective licenses form the Portuguese-Corpus:\n\n- Wikipedia (License: CC BY-SA 3.0)\n\n- CulturaX (License: ODC-By, cc0-1.0)\n\n- OSCAR (License: cc0-1.0)\n\n- CCc100 (License: Common Crawl terms of use)\n\n- Roots Wikiquote (License: CC BY-SA 3.0)\n\n- Roots Ted Talks (License: CC BY-NC-ND 4.0)",
"### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!"
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"passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Portuguese #license-other #portuguese #language-modeling #arxiv-2401.16640 #arxiv-2112.11446 #region-us \n# Portuguese-Corpus## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguese\n- Point of Contact: AIRES at PUCRS### Dataset Summary\n\nPortuguese-Corpus is a concatenation of several portions of Brazilian Portuguese datasets found in the Hub.\n\nIn 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\n\nThis dataset can be utilized for tasks involving language modeling.### Languages\n\nPortuguese.## Dataset Structure### Data Instances\n\nThe dataset consists of the following features:\n\n- text: a string of text in Portuguese.\n- metadata: the source where that string originated.### Data Fields### Data Splits\n\nAvailable splits are 'train'.## Dataset Creation### Curation Rationale\n\nThis 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.",
"passage: ### Source Data#### Initial Data Collection and Normalization\n\nWe 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?\n\nAll 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\n\nPortuguese-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?\n\nNicholas Kluge Corrêa.### Personal and Sensitive Information\n\nThis 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\n\nThe 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\n\nThe 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."
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] |
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 | [
"task_categories:question-answering",
"language:en",
"region:us"
] | 2024-01-17T13:42:26+00:00 | {"language": ["en"], "task_categories": ["question-answering"]} | 2024-01-17T14:27:40+00:00 | [] | [
"en"
] | 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.
| [
"# 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\n\n1. 'pseudo', The question authors.\n2. 'title', Title of the Post.\n3. 'question', The question's body.\n4. 'vote', Voting on Kaggle is similar to liking. \n5. '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.\n6. 'nbr_comment', The comment number.\n7. 'date', The post date.\n8. 'url_post', Link the comment dataset using the post URL.\n9. 'url_competition', If the question is related to a competition, include the competition URL.\n10. 'rank_competition', The author's rank in the competition.",
"### kaggle_comment\n\n1. 'pseudo_com', the answer authors.\n2. 'answer', The answer's body.\n3. 'vote_com', The answer's number of likes\n4. '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.\n5. 'date_com', The answer date.\n6. 'url_post', Link the comment dataset using the post URL.\n7. 'rank_competition', the author's rank in the competition.\n\n\nData scraping by Mathieu Duverne on august 2023."
] | [
"TAGS\n#task_categories-question-answering #language-English #region-us \n",
"# 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\n\n1. 'pseudo', The question authors.\n2. 'title', Title of the Post.\n3. 'question', The question's body.\n4. 'vote', Voting on Kaggle is similar to liking. \n5. '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.\n6. 'nbr_comment', The comment number.\n7. 'date', The post date.\n8. 'url_post', Link the comment dataset using the post URL.\n9. 'url_competition', If the question is related to a competition, include the competition URL.\n10. 'rank_competition', The author's rank in the competition.",
"### kaggle_comment\n\n1. 'pseudo_com', the answer authors.\n2. 'answer', The answer's body.\n3. 'vote_com', The answer's number of likes\n4. '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.\n5. 'date_com', The answer date.\n6. 'url_post', Link the comment dataset using the post URL.\n7. 'rank_competition', the author's rank in the competition.\n\n\nData scraping by Mathieu Duverne on august 2023."
] | [
22,
29,
4,
171,
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"passage: TAGS\n#task_categories-question-answering #language-English #region-us \n# 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\n\n1. 'pseudo', The question authors.\n2. 'title', Title of the Post.\n3. 'question', The question's body.\n4. 'vote', Voting on Kaggle is similar to liking. \n5. '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.\n6. 'nbr_comment', The comment number.\n7. 'date', The post date.\n8. 'url_post', Link the comment dataset using the post URL.\n9. 'url_competition', If the question is related to a competition, include the competition URL.\n10. 'rank_competition', The author's rank in the competition.### kaggle_comment\n\n1. 'pseudo_com', the answer authors.\n2. 'answer', The answer's body.\n3. 'vote_com', The answer's number of likes\n4. '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.\n5. 'date_com', The answer date.\n6. 'url_post', Link the comment dataset using the post URL.\n7. 'rank_competition', the author's rank in the competition.\n\n\nData scraping by Mathieu Duverne on august 2023."
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 8 | 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 | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | X | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | X | | | | | X | X | X | X | X | X | X | X | X | X |
| CyberHarem/ferry_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"region:us"
] | 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
| [
"# Dataset Card for \"SemEval_traindata_emotions\"\n\nКак был получен"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"SemEval_traindata_emotions\"\n\nКак был получен"
] | [
6,
21
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"Vietnamese-Book-Corpus\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"Vietnamese-Book-Corpus\"\n\nMore Information needed"
] | [
6,
19
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"Vietnamese-Book-Corpus\"\n\nMore Information needed"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"arxiv:2206.15474",
"region:us"
] | 2024-01-17T14:39:53+00:00 | {} | 2024-02-05T20:39:07+00:00 | [
"2206.15474"
] | [] | 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}
}
| [
"# Autocast\n\nThis 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.\n\nThe original dataset files are:\n- 'autocast_questions.json' \n- 'autocast_competition_test_set.json' \n- 'negated_tf_questions.json' \n\nWe have also processed the dataset to filter out source links with:\n\n- URLs returning non-200 HTTP status codes\n- URLs from sites that are difficult to scrape like twitter, bloomberg\n- Links with less than 1000 words are removed.\n\nOnly samples with a minimum of 5 working URLs are retained. The maximum number of working source links is 20.\n\nThe refined dataset files are:\n- 'autocast_questions_filtered.json' - a JSON subset of the initial autocast dataset.\n- 'autocast_questions_filtered.pkl' - a pickle file mapping URLs to the scraped data.\n- 'retrieved_docs.pkl' - this contains all texts that were retrieved.\n\n<img align=\"center\" src=\"assets/URL\" width=\"750\">",
"# Forecasting Future World Events with Neural Networks",
"## Introduction\n\nForecasting 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\n\nThe 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'.\n\nEach question has the following fields:\n \n\nThe original authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only.",
"## IntervalQA Dataset\n\nMotivated 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.\n\nDownload the IntervalQA dataset here.\n\nIf you find this useful in your research, please consider citing the original authors:\n\n @article{zouforecasting2022,\n title={Forecasting Future World Events with Neural Networks},\n 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},\n journal={NeurIPS},\n year={2022}\n }"
] | [
"TAGS\n#arxiv-2206.15474 #region-us \n",
"# Autocast\n\nThis 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.\n\nThe original dataset files are:\n- 'autocast_questions.json' \n- 'autocast_competition_test_set.json' \n- 'negated_tf_questions.json' \n\nWe have also processed the dataset to filter out source links with:\n\n- URLs returning non-200 HTTP status codes\n- URLs from sites that are difficult to scrape like twitter, bloomberg\n- Links with less than 1000 words are removed.\n\nOnly samples with a minimum of 5 working URLs are retained. The maximum number of working source links is 20.\n\nThe refined dataset files are:\n- 'autocast_questions_filtered.json' - a JSON subset of the initial autocast dataset.\n- 'autocast_questions_filtered.pkl' - a pickle file mapping URLs to the scraped data.\n- 'retrieved_docs.pkl' - this contains all texts that were retrieved.\n\n<img align=\"center\" src=\"assets/URL\" width=\"750\">",
"# Forecasting Future World Events with Neural Networks",
"## Introduction\n\nForecasting 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\n\nThe 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'.\n\nEach question has the following fields:\n \n\nThe original authors obtained permission from Metaculus to host the dataset on GitHub for research purposes only.",
"## IntervalQA Dataset\n\nMotivated 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.\n\nDownload the IntervalQA dataset here.\n\nIf you find this useful in your research, please consider citing the original authors:\n\n @article{zouforecasting2022,\n title={Forecasting Future World Events with Neural Networks},\n 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},\n journal={NeurIPS},\n year={2022}\n }"
] | [
14,
309,
12,
227,
78,
178
] | [
"passage: TAGS\n#arxiv-2206.15474 #region-us \n# Autocast\n\nThis 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.\n\nThe original dataset files are:\n- 'autocast_questions.json' \n- 'autocast_competition_test_set.json' \n- 'negated_tf_questions.json' \n\nWe have also processed the dataset to filter out source links with:\n\n- URLs returning non-200 HTTP status codes\n- URLs from sites that are difficult to scrape like twitter, bloomberg\n- Links with less than 1000 words are removed.\n\nOnly samples with a minimum of 5 working URLs are retained. The maximum number of working source links is 20.\n\nThe refined dataset files are:\n- 'autocast_questions_filtered.json' - a JSON subset of the initial autocast dataset.\n- 'autocast_questions_filtered.pkl' - a pickle file mapping URLs to the scraped data.\n- 'retrieved_docs.pkl' - this contains all texts that were retrieved.\n\n<img align=\"center\" src=\"assets/URL\" width=\"750\"># Forecasting Future World Events with Neural Networks"
] | [
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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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | X | | X | | | | | | | X | | | | | X | | X | | X | | | | | | X | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 12 |  |  |  |  |  | X | | | 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 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 | | | | | | | | | | | | |
| 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 | 6 |  |  |  |  |  | X | | | X | | | | X | | | | | | | | | X | X | | | | X | | | | | X | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X |
| CyberHarem/cellica_fireemblem | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | | 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 | 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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"cult-dpo\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"cult-dpo\"\n\nMore Information needed"
] | [
6,
14
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"cult-dpo\"\n\nMore Information needed"
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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 | [
"task_categories:automatic-speech-recognition",
"language:ms",
"license:mit",
"region:us"
] | 2024-01-17T14:59:47+00:00 | {"language": ["ms"], "license": "mit", "task_categories": ["automatic-speech-recognition"]} | 2024-02-12T07:57:36+00:00 | [] | [
"ms"
] | 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
| [
"# Pseudostreaming Malaya-Speech STT\n\nOriginal dataset at URL\n\nWe use URL to generate pseudostreaming dataset, source code at URL\n\nTotal 8667.802379812754 hours.\n\ndata format from URL,",
"## how-to"
] | [
"TAGS\n#task_categories-automatic-speech-recognition #language-Malay (macrolanguage) #license-mit #region-us \n",
"# Pseudostreaming Malaya-Speech STT\n\nOriginal dataset at URL\n\nWe use URL to generate pseudostreaming dataset, source code at URL\n\nTotal 8667.802379812754 hours.\n\ndata format from URL,",
"## how-to"
] | [
37,
49,
4
] | [
"passage: TAGS\n#task_categories-automatic-speech-recognition #language-Malay (macrolanguage) #license-mit #region-us \n# Pseudostreaming Malaya-Speech STT\n\nOriginal dataset at URL\n\nWe use URL to generate pseudostreaming dataset, source code at URL\n\nTotal 8667.802379812754 hours.\n\ndata format from URL,## how-to"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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] |
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 | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2024-01-17T15:10:32+00:00 | {"language": ["en"], "license": "apache-2.0"} | 2024-01-17T15:20:57+00:00 | [] | [
"en"
] | 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.
| [
"# LVIS",
"### Dataset Summary\n\nThis dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. \n\n- URL",
"### Loading\nThis code returns train, validation and test generators.\n\n\n\nObjects is a dictionary which contains annotation information like bbox, class.",
"### Access Generators\n\n\nAn example row is as follows."
] | [
"TAGS\n#language-English #license-apache-2.0 #region-us \n",
"# LVIS",
"### Dataset Summary\n\nThis dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. \n\n- URL",
"### Loading\nThis code returns train, validation and test generators.\n\n\n\nObjects is a dictionary which contains annotation information like bbox, class.",
"### Access Generators\n\n\nAn example row is as follows."
] | [
18,
3,
36,
35,
14
] | [
"passage: TAGS\n#language-English #license-apache-2.0 #region-us \n# LVIS### Dataset Summary\n\nThis dataset is the implementation of LVIS dataset into Hugging Face datasets. Please visit the original website for more information. \n\n- URL### Loading\nThis code returns train, validation and test generators.\n\n\n\nObjects is a dictionary which contains annotation information like bbox, class.### Access Generators\n\n\nAn example row is as follows."
] | [
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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
{
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"mc1_stderr": 0.015201522246299979,
"mc2": 0.4002214148044727,
"mc2_stderr": 0.014908452990717655
},
"harness|arc:challenge|25": {
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},
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},
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"acc": 0.5277777777777778,
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"acc_norm_stderr": 0.048262172941398944
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"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm_stderr": 0.03901591825836184
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"acc_norm_stderr": 0.03078232157768817
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5504469987228607,
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"acc_norm_stderr": 0.017788725283507337
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"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.48265895953757226,
"acc_norm_stderr": 0.026902900458666647
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"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24804469273743016,
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"acc_norm_stderr": 0.01444415780826144
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm_stderr": 0.02840830202033269
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"harness|hendrycksTest-philosophy|5": {
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"acc_norm_stderr": 0.028396770444111298
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.45987654320987653,
"acc_norm_stderr": 0.02773102275353928
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.3324641460234681,
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"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm_stderr": 0.029768263528933102
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"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm_stderr": 0.019835176484375376
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"harness|hendrycksTest-public_relations|5": {
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"acc_stderr": 0.04776449162396197,
"acc_norm": 0.5363636363636364,
"acc_norm_stderr": 0.04776449162396197
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"harness|hendrycksTest-security_studies|5": {
"acc": 0.5387755102040817,
"acc_stderr": 0.031912820526692774,
"acc_norm": 0.5387755102040817,
"acc_norm_stderr": 0.031912820526692774
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"harness|hendrycksTest-sociology|5": {
"acc": 0.582089552238806,
"acc_stderr": 0.034875586404620636,
"acc_norm": 0.582089552238806,
"acc_norm_stderr": 0.034875586404620636
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"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
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"harness|hendrycksTest-world_religions|5": {
"acc": 0.5497076023391813,
"acc_stderr": 0.038158273659132366,
"acc_norm": 0.5497076023391813,
"acc_norm_stderr": 0.038158273659132366
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"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.015201522246299979,
"mc2": 0.4002214148044727,
"mc2_stderr": 0.014908452990717655
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"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]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## 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. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### 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. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_KnutJaegersberg__Deita-Qwen-1_8B | [
"region:us"
] | 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\": 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#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
| [
"# Dataset Card for Evaluation run of KnutJaegersberg/Deita-Qwen-1_8B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-Qwen-1_8B on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of KnutJaegersberg/Deita-Qwen-1_8B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-Qwen-1_8B on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Deita-Qwen-1_8B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-Qwen-1_8B on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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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 | [
"task_categories:text-generation",
"size_categories:100B<n<1T",
"language:zh",
"region:us"
] | 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 | [] | [
"zh"
] | 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
| [] | [
"TAGS\n#task_categories-text-generation #size_categories-100B<n<1T #language-Chinese #region-us \n"
] | [
34
] | [
"passage: TAGS\n#task_categories-text-generation #size_categories-100B<n<1T #language-Chinese #region-us \n"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"VQA",
"region:us"
] | 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 | [] | [
"en"
] | TAGS
#size_categories-100K<n<1M #language-English #license-mit #VQA #region-us
|
The dataset is available at: URL
!image/png
| [] | [
"TAGS\n#size_categories-100K<n<1M #language-English #license-mit #VQA #region-us \n"
] | [
30
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"Vietnamese-Books-dedup\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"Vietnamese-Books-dedup\"\n\nMore Information needed"
] | [
6,
20
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"Vietnamese-Books-dedup\"\n\nMore Information needed"
] | [
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] | [
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"sample_105000_rows\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"sample_105000_rows\"\n\nMore Information needed"
] | [
6,
18
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"passage: TAGS\n#region-us \n# Dataset Card for \"sample_105000_rows\"\n\nMore Information needed"
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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
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_kaitchup__Maixtchup-4x7b | [
"region:us"
] | 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\": 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#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
| [
"# Dataset Card for Evaluation run of kaitchup/Maixtchup-4x7b\n\n\n\nDataset automatically created during the evaluation run of model kaitchup/Maixtchup-4x7b on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of kaitchup/Maixtchup-4x7b\n\n\n\nDataset automatically created during the evaluation run of model kaitchup/Maixtchup-4x7b on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kaitchup/Maixtchup-4x7b\n\n\n\nDataset automatically created during the evaluation run of model kaitchup/Maixtchup-4x7b on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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] |
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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 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
| [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] | [
44,
61,
5,
4
] | [
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
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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": {
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"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]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## 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. -->
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Who are the source data producers?
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#### Annotation process
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#### Who are the annotators?
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#### 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. -->
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## Bias, Risks, and Limitations
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_llmixer__BigWeave-v20-110b | [
"region:us"
] | 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\": 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#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
| [
"# Dataset Card for Evaluation run of llmixer/BigWeave-v20-110b\n\n\n\nDataset automatically created during the evaluation run of model llmixer/BigWeave-v20-110b on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of llmixer/BigWeave-v20-110b\n\n\n\nDataset automatically created during the evaluation run of model llmixer/BigWeave-v20-110b on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of llmixer/BigWeave-v20-110b\n\n\n\nDataset automatically created during the evaluation run of model llmixer/BigWeave-v20-110b on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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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,
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}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
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## Uses
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### Direct Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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#### Annotation process
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#### Personal and Sensitive Information
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_logicker__SkkuDS-DPO-72B-v1 | [
"region:us"
] | 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 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#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
| [
"# Dataset Card for Evaluation run of logicker/SkkuDS-DPO-72B-v1\n\n\n\nDataset automatically created during the evaluation run of model logicker/SkkuDS-DPO-72B-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of logicker/SkkuDS-DPO-72B-v1\n\n\n\nDataset automatically created during the evaluation run of model logicker/SkkuDS-DPO-72B-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of logicker/SkkuDS-DPO-72B-v1\n\n\n\nDataset automatically created during the evaluation run of model logicker/SkkuDS-DPO-72B-v1 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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] |
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 | [
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] | 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 | [] | [
"en"
] | 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* | [
"# Crowd Counting Dataset\n\nThe 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.\n\n![](URL\n\nTypes of crowds in the dataset: *0-1000, 1000-2000, 2000-3000, 3000-4000 and 4000-5000*\n\n![](URL\n\nThis 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 to buy the dataset",
"### Statistics for the dataset (number of images by the crowd's size and image width):\n\n![](URL",
"# Get the Dataset",
"## This is just an example of the data \nLeave a request on URL to learn about the price and buy the dataset",
"# Content\n- images - includes original images of crowds placed in subfolders according to its size,\n- labels - includes json-files with labeling and visualised labeling for the images in the previous folder,\n- csv file - includes information for each image in the dataset",
"### File with the extension .csv\n- id: id of the image,\n- image: link to access the original image,\n- label: link to access the json-file with labeling,\n- type: type of the crowd on the photo",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*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*"
] | [
"TAGS\n#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #legal #code #region-us \n",
"# Crowd Counting Dataset\n\nThe 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.\n\n![](URL\n\nTypes of crowds in the dataset: *0-1000, 1000-2000, 2000-3000, 3000-4000 and 4000-5000*\n\n![](URL\n\nThis 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 to buy the dataset",
"### Statistics for the dataset (number of images by the crowd's size and image width):\n\n![](URL",
"# Get the Dataset",
"## This is just an example of the data \nLeave a request on URL to learn about the price and buy the dataset",
"# Content\n- images - includes original images of crowds placed in subfolders according to its size,\n- labels - includes json-files with labeling and visualised labeling for the images in the previous folder,\n- csv file - includes information for each image in the dataset",
"### File with the extension .csv\n- id: id of the image,\n- image: link to access the original image,\n- label: link to access the json-file with labeling,\n- type: type of the crowd on the photo",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*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*"
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"passage: TAGS\n#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #legal #code #region-us \n# Crowd Counting Dataset\n\nThe 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.\n\n:\n\n![](URL# Get the Dataset## This is just an example of the data \nLeave a request on URL to learn about the price and buy the dataset# Content\n- images - includes original images of crowds placed in subfolders according to its size,\n- labels - includes json-files with labeling and visualised labeling for the images in the previous folder,\n- csv file - includes information for each image in the dataset### File with the extension .csv\n- id: id of the image,\n- image: link to access the original image,\n- label: link to access the json-file with labeling,\n- type: type of the crowd on the photo"
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"HealthCareMagic-100k\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"HealthCareMagic-100k\"\n\nMore Information needed"
] | [
6,
17
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"HealthCareMagic-100k\"\n\nMore Information needed"
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"ChatDoctor-iCliniq\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"ChatDoctor-iCliniq\"\n\nMore Information needed"
] | [
6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"ChatDoctor-iCliniq\"\n\nMore Information needed"
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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 | [
"region:us"
] | 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 | [
"# AI Medical Chatbot Dataset\n\nThis is an experimental Dataset designed to run a Medical Chatbot\nIt contains at least 250k dialogues between a Patient and a Doctor.\n\n\n\nFor furter information visit the project here:\nURL"
] | [
"TAGS\n#region-us \n",
"# AI Medical Chatbot Dataset\n\nThis is an experimental Dataset designed to run a Medical Chatbot\nIt contains at least 250k dialogues between a Patient and a Doctor.\n\n\n\nFor furter information visit the project here:\nURL"
] | [
6,
52
] | [
"passage: TAGS\n#region-us \n# AI Medical Chatbot Dataset\n\nThis is an experimental Dataset designed to run a Medical Chatbot\nIt contains at least 250k dialogues between a Patient and a Doctor.\n\n\n\nFor furter information visit the project here:\nURL"
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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. -->
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
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### 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. -->
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<!-- 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. -->
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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[More Information Needed] | Izazk/izaz-mind2web-dataset | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
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] | 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]
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<p><span style="font-weight: 400;">The ketogenic diet and apple cider vinegar (ACV) are combined in the potent weight loss supplement </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;">. The ketogenic state, in which fat rather than carbohydrates is used for energy, is facilitated by eating these sweets. Each bottle contains sixty all-natural gummies, and the product is packaged in bottles. In addition to apple cider vinegar, the main active ingredient, these candies also have a blend of other potent ingredients including ginger extract and pomegranate juice. While ginger extract may help with digestion and has anti-inflammatory properties, pomegranate juice is full of antioxidants that help the body combat free radicals that can cause harm throughout the body.</span></p>
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<p><span style="font-weight: 400;">These sweets are carefully crafted by hand using premium ingredients, each of which has its own set of health benefits. You may rest easy knowing you're getting the best product on the market since they've been put through rigorous testing to ensure their purity and efficacy. </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> make maintaining your health easier and more enjoyable than ever before. If you want to reap the benefits of these delicious candies, all you have to do is eat one every day.</span></p>
<p> </p>
<p><span style="font-weight: 400;">You just need to eat two of these </span><a href="https://www.facebook.com/ProtonKetoGummiesACVGummies/"><strong>gummies</strong></a><span style="font-weight: 400;"> daily as part of a healthy routine that also includes eating well and exercising to get the full benefits. It's crucial to bear in mind that results may vary from person to person dependent on factors including metabolism and lifestyle choices. It is recommended that patients see their primary care physicians prior to initiating any new supplement regimen. They can provide you advice that is tailored to your specific needs and goals.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Some of the benefits of keeping up with a regular exercise routine and eating a balanced, nutritious diet include:</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">There are many benefits to maintaining a healthy weight and </span><a href="https://www.facebook.com/ProtonACVGummiesKetoGummies/"><strong>body shape</strong></a><span style="font-weight: 400;"> that go far beyond how you appear on the outside. Heart disease, diabetes, and high blood pressure are just some of the chronic ailments that may be avoided by maintaining a healthy weight. Maintaining a healthy weight is associated with several health benefits. Taking care of yourself reduces the strain placed on your internal organs, enhancing their functionality.</span></p>
<p> </p>
<p><span style="font-weight: 400;">Keeping up with your workout routine may give you more pep and help you get more done. The demands of daily life may be better handled by engaging in regular exercise since it increases stamina and strengthens muscles. Furthermore, it promotes the release of endorphins, neurotransmitters that are often known as the "</span><a href="https://www.facebook.com/ProtonKetoGummiesACVGummiesBuy/"><strong>feel-good" hormones</strong></a><span style="font-weight: 400;">. Positive emotions and less stress are possible thanks to endorphins.</span></p>
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<p><span style="font-weight: 400;">Developing a physically fit </span><a href="https://www.facebook.com/ProtonACVGummiesKetoGummiesBuy/"><strong>physique may also boost one's</strong></a><span style="font-weight: 400;"> confidence. When you have a healthy self-image about your physical appearance, you'll experience more happiness in all facets of your life. If you get the courage to try new things and strive for your own personal goals, you may find that the quality of your social interactions and relationships improves as a result.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>How does this product affect your health as a whole?</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">Many people are curious about the effectiveness of </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> and wonder how quickly they will begin to feel its effects. It's important to remember that everyone has a somewhat different biochemistry, which might lead to slightly different results. However, regular users should start to observe physical improvements within only a few weeks. Within this time frame, your body will be able to adjust to the formula, and the active ingredients will have a chance to do their thing.</span></p>
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<p><span style="font-weight: 400;">The outstanding effectiveness of </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> may be attributed to the synergistic combination of its basic elements. Natural apple cider vinegar (ACV) extract is used in the production of these gummies, along with additional potent substances that are known for their effectiveness in promoting weight loss. Apple cider vinegar has been shown to increase metabolic rate and decrease fat storage. By including these gummies into your daily routine, you can guarantee that your body is getting the nutrients it needs to support your weight loss efforts. The gummies also help reduce cravings and increase fullness, both of which may lead to greater fat loss. Proton Keto Gummies work best when used in conjunction with a healthy lifestyle that includes regular exercise and a balanced diet.</span></p>
<p> </p>
<p><span style="font-weight: 400;">While results may vary from person to person, those who consistently use the medication and maintain a healthy lifestyle should see improvements rather quickly.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>How much does this item cost, and where can I get it?</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">Prior to placing a purchase of </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;">, it is crucial to have a solid grasp of the payment details. The success of your transaction may be ensured if you take the time to learn about the many options and procedures involved.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Several Different Payment Options</strong></a></span></h3>
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<p><span style="font-weight: 400;">For the sake of their customers, businesses now offer a broad array of payment options. Online payment options that might fit into this category include major credit and debit cards, PayPal, and others. It is crucial that you verify the options before proceeding.</span></p>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Keeping Safe by Taking Measures</strong></a></span></h3>
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<p><span style="font-weight: 400;">It is crucial to have a safe connection while making an online transaction. </span><a href="https://www.facebook.com/ProtonKetoGummiesACVGummies/"><strong>Trustworthy online merchants</strong></a><span style="font-weight: 400;"> will protect your privacy and financial data by using secure payment methods like SSL encryption. Look for indicators of security, such as encryption and the logos of trusted third parties, before entering any financial information. Payment Information When completing the purchase procedure, it is crucial that you provide accurate payment information. Providing the correct name, billing address, and contact information for the card or account holder is required.</span></p>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Purchasing Confirmation</strong></a></span></h3>
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<p><span style="font-weight: 400;">After entering your billing information correctly, you must wait for a confirmation of your order before moving further. Keep this verification in case you need to refer to it later. To make a purchase of </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> with complete assurance that your financial data will be handled securely and without incident, it is important that you have a firm knowledge of the payment details associated with such a purchase.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>When thinking about the supplement, keep the following in mind:</strong></a></span></h3>
<p> </p>
<p><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> are an easily digestible dietary supplement that brings together the benefits of the ketogenic diet with the strength of apple cider vinegar (ACV). These sweets can help you achieve your weight loss goals by promoting a healthy metabolism and increasing fat burning. The chemicals used in these sweets have a stellar reputation for doing their jobs. Each gummy has a healthy dose of apple cider vinegar, which is known to aid digestion, reduce cravings, and increase energy levels. One of the best features of Proton Keto Gummies is how simple they are to use. Get all the benefits of ACV in a format that's easy to eat and transportable, so you can take use of it whenever you choose.</span></p>
<p> </p>
<p><span style="font-weight: 400;">In addition to providing the health advantages of apple cider vinegar, these candies also include Proton nutrients for those on a ketogenic diet. You may force your body into a metabolic state called ketosis by following a low-carb, high-fat eating plan like the ketogenic diet. In this state, fat reserves are used as energy instead of carbohydrates. The potential for significant weight loss results exists when combined with regular exercise and the appropriate diet. These sweets are a very effective dietary </span><a href="https://www.facebook.com/ProtonKetoGummiesACVGummiesBuy/"><strong>supplement</strong></a> <span style="font-weight: 400;">that may speed up your weight loss journey. They achieve this by combining key components of the ketogenic diet with the power of apple cider vinegar to boost metabolism and promote fat burning.</span></p>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>This dietary supplement works by the following means:</strong></a></span></h3>
<p> </p>
<p><a href="https://www.facebook.com/ProtonACVGummiesKetoGummiesBuy/"><strong>Apple cider vinegar,</strong></a><span style="font-weight: 400;"> often known as ACV, was one of the strong ingredients in these candies, along with BHB ketones. Apple cider vinegar aids weight loss by decreasing hunger and increasing satiety. Not only does it help keep your metabolism running smoothly, but it also aids in keeping your blood sugar levels stable.</span></p>
<p> </p>
<p><span style="font-weight: 400;">The apple cider vinegar in these candies aids in digestion and speeds up the breakdown of fats in the body. It improves your body's ability to burn stored fat and also helps prevent fat from being deposited in other parts of the body. Also, the BHB ketones included in </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> are a great help when it comes to getting your body into ketosis.</span></p>
<p> </p>
<p><span style="font-weight: 400;">When your body is able to utilize fat for energy instead of glucose, it is said to be in a metabolic state known as ketosis. These candies contain exogenous ketones, which may help you enter and remain in ketosis more quickly.</span></p>
<p> </p>
<p><span style="font-weight: 400;">The </span><a href="https://www.facebook.com/ProtonKetoGummiesACVGummies/"><strong>Proton Keto Gummies</strong></a><span style="font-weight: 400;"> that make these gummies helpful for weight loss efforts are apple cider vinegar and BHB ketones. They facilitate entering ketosis, a condition in which the body burns fat more efficiently, by reducing cravings, increasing fat burning, maintaining normal blood sugar levels, and enhancing digestion. Remember that the results you get from weight loss pills and other items may vary depending on the individual using them.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Please list the several benefits of utilizing this product : </strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">The many benefits of </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> make them a great choice for customers who are trying to boost their metabolism and keep their weight in a healthy level. Some of its numerous benefits include the following:</span></p>
<p> </p>
<p><span style="font-weight: 400;">Apple cider vinegar (ACV) is an ingredient in these gummies, and it has been widely praised for its health benefits. Apple cider vinegar promotes cleansing, aids digestion, and keeps gut flora in good condition. BHB ketones, an </span><a href="https://www.facebook.com/ProtonKetoGummiesACVGummies/"><strong>Proton</strong></a><span style="font-weight: 400;"> part of the process of establishing ketosis inside the body, are included in these gummies. In a metabolic condition known as ketosis, fat is used for energy instead of carbohydrates, leading to increased strength and decreased body fat percentage. When the body is in a state of ketosis, it produces ketone bodies.</span></p>
<p> </p>
<p><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> have a proprietary blend of all-natural ingredients that have been shown to curb hunger without sacrificing taste. It might help those who struggle to maintain a healthy weight or who turn to food when they're feeling down. In addition, the chewable form factor of these candies makes it simple to include them into your daily schedule. Regular use of these sweets may improve health in a number of ways. They may aid digestion, boost energy, and promote metabolic health. Benefits of Proton Keto ACV Gummies include improved digestion, increased weight loss via induction of the ketosis state, decreased appetite and cravings, and the ability to be easily consumed on the go.</span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="https://healthcare24hrs.com/protonketoacvgummies"><strong>What are the possible side effects?</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">One of the most frequent concerns raised before using a new nutritional supplement is whether or not there will be any unintended consequences. </span><a href="https://healthcare24hrs.com/protonketoacvgummies"><strong>Proton Keto ACV Gummies</strong></a><span style="font-weight: 400;"> are produced with just natural ingredients, so you may use them with confidence knowing they won't do any harm. There are no artificial ingredients or chemicals in the composition that might be harmful to your health. However, it is crucial to consult with your doctor before starting a new supplementing practice, just as it is before taking any form of dietary supplement. If you're on other medications, this is very important.</span></p>
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| <p><span style="background-color: #ffff99;"><strong>Our Official Facebook Pages ⇒</strong></span></p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="URL Confirmation</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">After entering your billing information correctly, you must wait for a confirmation of your order before moving further. Keep this verification in case you need to refer to it later. To make a purchase of </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> with complete assurance that your financial data will be handled securely and without incident, it is important that you have a firm knowledge of the payment details associated with such a purchase.</span></p>
<p> </p>
<p><a href="URL src="https://i.URL alt="777" border="0" /></a></p>
<p> </p>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="URL thinking about the supplement, keep the following in mind:</strong></a></span></h3>
<p> </p>
<p><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> are an easily digestible dietary supplement that brings together the benefits of the ketogenic diet with the strength of apple cider vinegar (ACV). These sweets can help you achieve your weight loss goals by promoting a healthy metabolism and increasing fat burning. The chemicals used in these sweets have a stellar reputation for doing their jobs. Each gummy has a healthy dose of apple cider vinegar, which is known to aid digestion, reduce cravings, and increase energy levels. One of the best features of Proton Keto Gummies is how simple they are to use. Get all the benefits of ACV in a format that's easy to eat and transportable, so you can take use of it whenever you choose.</span></p>
<p> </p>
<p><span style="font-weight: 400;">In addition to providing the health advantages of apple cider vinegar, these candies also include Proton nutrients for those on a ketogenic diet. You may force your body into a metabolic state called ketosis by following a low-carb, high-fat eating plan like the ketogenic diet. In this state, fat reserves are used as energy instead of carbohydrates. The potential for significant weight loss results exists when combined with regular exercise and the appropriate diet. These sweets are a very effective dietary </span><a href="URL <span style="font-weight: 400;">that may speed up your weight loss journey. They achieve this by combining key components of the ketogenic diet with the power of apple cider vinegar to boost metabolism and promote fat burning.</span></p>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="URL dietary supplement works by the following means:</strong></a></span></h3>
<p> </p>
<p><a href="URL cider vinegar,</strong></a><span style="font-weight: 400;"> often known as ACV, was one of the strong ingredients in these candies, along with BHB ketones. Apple cider vinegar aids weight loss by decreasing hunger and increasing satiety. Not only does it help keep your metabolism running smoothly, but it also aids in keeping your blood sugar levels stable.</span></p>
<p> </p>
<p><span style="font-weight: 400;">The apple cider vinegar in these candies aids in digestion and speeds up the breakdown of fats in the body. It improves your body's ability to burn stored fat and also helps prevent fat from being deposited in other parts of the body. Also, the BHB ketones included in </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> are a great help when it comes to getting your body into ketosis.</span></p>
<p> </p>
<p><span style="font-weight: 400;">When your body is able to utilize fat for energy instead of glucose, it is said to be in a metabolic state known as ketosis. These candies contain exogenous ketones, which may help you enter and remain in ketosis more quickly.</span></p>
<p> </p>
<p><span style="font-weight: 400;">The </span><a href="URL Keto Gummies</strong></a><span style="font-weight: 400;"> that make these gummies helpful for weight loss efforts are apple cider vinegar and BHB ketones. They facilitate entering ketosis, a condition in which the body burns fat more efficiently, by reducing cravings, increasing fat burning, maintaining normal blood sugar levels, and enhancing digestion. Remember that the results you get from weight loss pills and other items may vary depending on the individual using them.</span></p>
<p> </p>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="URL list the several benefits of utilizing this product : </strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">The many benefits of </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> make them a great choice for customers who are trying to boost their metabolism and keep their weight in a healthy level. Some of its numerous benefits include the following:</span></p>
<p> </p>
<p><span style="font-weight: 400;">Apple cider vinegar (ACV) is an ingredient in these gummies, and it has been widely praised for its health benefits. Apple cider vinegar promotes cleansing, aids digestion, and keeps gut flora in good condition. BHB ketones, an </span><a href="URL style="font-weight: 400;"> part of the process of establishing ketosis inside the body, are included in these gummies. In a metabolic condition known as ketosis, fat is used for energy instead of carbohydrates, leading to increased strength and decreased body fat percentage. When the body is in a state of ketosis, it produces ketone bodies.</span></p>
<p> </p>
<p><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> have a proprietary blend of all-natural ingredients that have been shown to curb hunger without sacrificing taste. It might help those who struggle to maintain a healthy weight or who turn to food when they're feeling down. In addition, the chewable form factor of these candies makes it simple to include them into your daily schedule. Regular use of these sweets may improve health in a number of ways. They may aid digestion, boost energy, and promote metabolic health. Benefits of Proton Keto ACV Gummies include improved digestion, increased weight loss via induction of the ketosis state, decreased appetite and cravings, and the ability to be easily consumed on the go.</span></p>
<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="URL are the possible side effects?</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">One of the most frequent concerns raised before using a new nutritional supplement is whether or not there will be any unintended consequences. </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> are produced with just natural ingredients, so you may use them with confidence knowing they won't do any harm. There are no artificial ingredients or chemicals in the composition that might be harmful to your health. However, it is crucial to consult with your doctor before starting a new supplementing practice, just as it is before taking any form of dietary supplement. If you're on other medications, this is very important.</span></p>
<p> </p>
<p><a href="URL src="https://i.URL alt="380703321-122107793222041563-3011249091168290970-n" border="0" /></a></p>
<p> </p>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
<p> </p>
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<p><span style="background-color: #ffff99;"><strong>Recent Trends Of USA :-</strong></span></p>
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<p><a href="URL | [] | [
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<h3><span style="font-weight: 400;">Item Name - </span><span style="color: #993366;"><a style="color: #993366;" href="URL Keto ACV Gummies</strong></a></span></h3>
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<p><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> make maintaining your health easier and more enjoyable than ever before. Are you weary of trying to shed those last few pounds of belly fat, only to feel like all your hard work in the gym and kitchen is for naught? If that's the case, you may relax; we'll be talking about </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> today. These candies are the perfect solution to your issues with excess fat. These gummies combine the health benefits of apple cider vinegar (ACV) with the ketogenic diet into a single, tasty product for your convenience. Deal is here, Click for purchase!!!</span></p>
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<p><span style="font-weight: 400;">People who have used this novel </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> weight-loss product are talking about their experiences, asking each other questions, and offering advice to others who are considering purchasing and trying it themselves. Many users of have reported success in reducing their body fat percentage and the circumference of their hips and thighs as a result. Several consumers have mentioned significant weight loss after including these sweets into their regular regimen for only a few weeks. Now, I'll explain how this product works and list all of the amazing benefits you may expect to enjoy as a result of using it. In addition, we'll talk about why it's so important to keep your body in good form.</span></p>
<p> </p>
<p><a href="URL src="https://i.URL alt="111" border="0" /></a></p>
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<h2><span style="color: #ff0000;"><a style="color: #ff0000;" href="URL CLICK HERE TO BUY NOW & GET HUGE DISCOUNT ON PROTON KETO ACV GUMMIES </strong></a></span></h2>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="URL the nutritional aid :</strong></a></span></h3>
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<p><span style="font-weight: 400;">The ketogenic diet and apple cider vinegar (ACV) are combined in the potent weight loss supplement </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;">. The ketogenic state, in which fat rather than carbohydrates is used for energy, is facilitated by eating these sweets. Each bottle contains sixty all-natural gummies, and the product is packaged in bottles. In addition to apple cider vinegar, the main active ingredient, these candies also have a blend of other potent ingredients including ginger extract and pomegranate juice. While ginger extract may help with digestion and has anti-inflammatory properties, pomegranate juice is full of antioxidants that help the body combat free radicals that can cause harm throughout the body.</span></p>
<p> </p>
<p><span style="font-weight: 400;">These sweets are carefully crafted by hand using premium ingredients, each of which has its own set of health benefits. You may rest easy knowing you're getting the best product on the market since they've been put through rigorous testing to ensure their purity and efficacy. </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> make maintaining your health easier and more enjoyable than ever before. If you want to reap the benefits of these delicious candies, all you have to do is eat one every day.</span></p>
<p> </p>
<p><span style="font-weight: 400;">You just need to eat two of these </span><a href="URL style="font-weight: 400;"> daily as part of a healthy routine that also includes eating well and exercising to get the full benefits. It's crucial to bear in mind that results may vary from person to person dependent on factors including metabolism and lifestyle choices. It is recommended that patients see their primary care physicians prior to initiating any new supplement regimen. They can provide you advice that is tailored to your specific needs and goals.</span></p>
<p> </p>
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<h3><span style="color: #008000;"><a style="color: #008000;" href="URL of the benefits of keeping up with a regular exercise routine and eating a balanced, nutritious diet include:</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">There are many benefits to maintaining a healthy weight and </span><a href="URL shape</strong></a><span style="font-weight: 400;"> that go far beyond how you appear on the outside. Heart disease, diabetes, and high blood pressure are just some of the chronic ailments that may be avoided by maintaining a healthy weight. Maintaining a healthy weight is associated with several health benefits. Taking care of yourself reduces the strain placed on your internal organs, enhancing their functionality.</span></p>
<p> </p>
<p><span style="font-weight: 400;">Keeping up with your workout routine may give you more pep and help you get more done. The demands of daily life may be better handled by engaging in regular exercise since it increases stamina and strengthens muscles. Furthermore, it promotes the release of endorphins, neurotransmitters that are often known as the "</span><a href="URL hormones</strong></a><span style="font-weight: 400;">. Positive emotions and less stress are possible thanks to endorphins.</span></p>
<p> </p>
<p><span style="font-weight: 400;">Developing a physically fit </span><a href="URL may also boost one's</strong></a><span style="font-weight: 400;"> confidence. When you have a healthy self-image about your physical appearance, you'll experience more happiness in all facets of your life. If you get the courage to try new things and strive for your own personal goals, you may find that the quality of your social interactions and relationships improves as a result.</span></p>
<p> </p>
<p><a href="URL src="https://i.URL alt="444" border="0" /></a><br /><br /></p>
<p> </p>
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<p> </p>
<h3><span style="color: #008000;"><a style="color: #008000;" href="URL does this product affect your health as a whole?</strong></a></span></h3>
<p> </p>
<p><span style="font-weight: 400;">Many people are curious about the effectiveness of </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> and wonder how quickly they will begin to feel its effects. It's important to remember that everyone has a somewhat different biochemistry, which might lead to slightly different results. However, regular users should start to observe physical improvements within only a few weeks. Within this time frame, your body will be able to adjust to the formula, and the active ingredients will have a chance to do their thing.</span></p>
<p> </p>
<p><span style="font-weight: 400;">The outstanding effectiveness of </span><a href="URL Keto ACV Gummies</strong></a><span style="font-weight: 400;"> may be attributed to the synergistic combination of its basic elements. Natural apple cider vinegar (ACV) extract is used in the production of these gummies, along with additional potent substances that are known for their effectiveness in promoting weight loss. Apple cider vinegar has been shown to increase metabolic rate and decrease fat storage. By including these gummies into your daily routine, you can guarantee that your body is getting the nutrients it needs to support your weight loss efforts. The gummies also help reduce cravings and increase fullness, both of which may lead to greater fat loss. Proton Keto Gummies work best when used in conjunction with a healthy lifestyle that includes regular exercise and a balanced diet.</span></p>
<p> </p>
<p><span style="font-weight: 400;">While results may vary from person to person, those who consistently use the medication and maintain a healthy lifestyle should see improvements rather quickly.</span></p>
<p> </p>
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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 | [
"task_categories:image-to-image",
"task_categories:image-classification",
"task_categories:image-segmentation",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"medical",
"biology",
"region:us"
] | 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 | [] | [
"en"
] | 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* | [
"# Brain Cancer MRI Object Detection & Segmentation Dataset\n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS \n\n![](URL\n\nThe MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues.\n\nThe 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:\n- ST000001: includes subfolders with 10 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_1.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files",
"### Medical reports include the following data:\n- Patient's demographic information, \n- Description of the case, \n- Preliminary diagnosis, \n- Recommendations on the further actions\n\n*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\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*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*"
] | [
"TAGS\n#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 \n",
"# Brain Cancer MRI Object Detection & Segmentation Dataset\n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS \n\n![](URL\n\nThe MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues.\n\nThe 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:\n- ST000001: includes subfolders with 10 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_1.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files",
"### Medical reports include the following data:\n- Patient's demographic information, \n- Description of the case, \n- Preliminary diagnosis, \n- Recommendations on the further actions\n\n*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\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*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*"
] | [
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"passage: TAGS\n#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 \n# Brain Cancer MRI Object Detection & Segmentation Dataset\n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS \n\n![](URL\n\nThe MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues.\n\nThe 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"
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"myriade_ontologie_corrected\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"myriade_ontologie_corrected\"\n\nMore Information needed"
] | [
6,
19
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"myriade_ontologie_corrected\"\n\nMore Information needed"
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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 | [
"task_categories:image-to-image",
"task_categories:image-classification",
"task_categories:image-segmentation",
"language:en",
"license:cc-by-nc-nd-4.0",
"biology",
"medical",
"region:us"
] | 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 | [] | [
"en"
] | 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* | [
"# Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset\n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS\n \n![](URL\n\nThe 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:\n- ST000001: includes subfolders with 13 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_2.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files",
"### Medical reports include the following data:\n- Patient's demographic information, \n- Description of the case, \n- Preliminary diagnosis, \n- Recommendations on the further actions\n\n*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\n\n*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*"
] | [
"TAGS\n#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 \n",
"# Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset\n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS\n \n![](URL\n\nThe 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:\n- ST000001: includes subfolders with 13 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_2.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files",
"### Medical reports include the following data:\n- Patient's demographic information, \n- Description of the case, \n- Preliminary diagnosis, \n- Recommendations on the further actions\n\n*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\n\n*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*"
] | [
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"passage: TAGS\n#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 \n# Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset\n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS\n \n![](URL\n\nThe 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:\n- ST000001: includes subfolders with 13 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_2.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files"
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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 | [
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] | 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 | [] | [
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] | 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* | [
"# Brain MRI Dataset, Arnold-Chiari Malformation Detection & Segmentation \n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS \n\n![](URL\n\nThe 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:\n- ST000001: includes subfolders with 6 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_3.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files",
"### Medical reports include the following data:\n- Patient's demographic information, \n- Description of the case, \n- Preliminary diagnosis, \n- Recommendations on the further actions\n\n*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\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*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*"
] | [
"TAGS\n#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 \n",
"# Brain MRI Dataset, Arnold-Chiari Malformation Detection & Segmentation \n\nThe 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. \n\nThe 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\n![](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:\n- Cancer\n- Multiple sclerosis\n- Metastatic lesion\n- Arnold-Chiari malformation\n- Focal gliosis of the brain\n- AND MANY OTHER CONDITIONS \n\n![](URL\n\nThe 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:\n- ST000001: includes subfolders with 6 studies. Each study includes MRI-scans in .dcm and .jpg formats,\n- DICOMDIR: includes information about the patient's condition and links to access files,\n- Brain_MRI_3.pdf: includes medical report, provided by the radiologist,\n- .csv file: includes id of the studies and the number of files",
"### Medical reports include the following data:\n- Patient's demographic information, \n- Description of the case, \n- Preliminary diagnosis, \n- Recommendations on the further actions\n\n*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\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*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*"
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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 | [
"task_categories:text-classification",
"task_categories:sentence-similarity",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"20newsgroups",
"scikit",
"region:us"
] | 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 | [] | [
"en"
] | 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-*
--- | [] | [
"TAGS\n#task_categories-text-classification #task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #license-mit #20newsgroups #scikit #region-us \n"
] | [
59
] | [
"passage: TAGS\n#task_categories-text-classification #task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #license-mit #20newsgroups #scikit #region-us \n"
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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 | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | 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 | [] | [
"en"
] | 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
| [
"### Dataset Breakdown"
] | [
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n",
"### Dataset Breakdown"
] | [
41,
6
] | [
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-apache-2.0 #region-us \n### Dataset Breakdown"
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] |
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 | [
"region:us"
] | 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
| [
"# Dataset Card for hh-rlhf-megagon-ja\n\nThis dataset loads data from megagonlabs/instruction_ja.",
"## Licence\n\nMIT License"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for hh-rlhf-megagon-ja\n\nThis dataset loads data from megagonlabs/instruction_ja.",
"## Licence\n\nMIT License"
] | [
6,
32,
5
] | [
"passage: TAGS\n#region-us \n# Dataset Card for hh-rlhf-megagon-ja\n\nThis dataset loads data from megagonlabs/instruction_ja.## Licence\n\nMIT License"
] | [
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] |
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 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:vi",
"license:apache-2.0",
"question answering",
"medical",
"vietnamese",
"region:us"
] | 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 | [] | [
"vi"
] | 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.
| [
"## Dataset Summary\n\nVietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from edoctor and vinmec.\n* Size: After merging data from these two sources, obtained 9335 QA pairs.\n* Language: Vietnamese",
"## Load with Datasets",
"## Dataset Version and Update\n* Version Details:\n * Current version: 1.0\n * Last Update: 16/02/2024\n * First Release: 16/02/2024\n\n* Update Plan: We will keep updating as we gather additional data.",
"## Licensing Information\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License."
] | [
"TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-Vietnamese #license-apache-2.0 #question answering #medical #vietnamese #region-us \n",
"## Dataset Summary\n\nVietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from edoctor and vinmec.\n* Size: After merging data from these two sources, obtained 9335 QA pairs.\n* Language: Vietnamese",
"## Load with Datasets",
"## Dataset Version and Update\n* Version Details:\n * Current version: 1.0\n * Last Update: 16/02/2024\n * First Release: 16/02/2024\n\n* Update Plan: We will keep updating as we gather additional data.",
"## Licensing Information\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License."
] | [
57,
62,
7,
47,
30
] | [
"passage: TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-Vietnamese #license-apache-2.0 #question answering #medical #vietnamese #region-us \n## Dataset Summary\n\nVietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from edoctor and vinmec.\n* Size: After merging data from these two sources, obtained 9335 QA pairs.\n* Language: Vietnamese## Load with Datasets## Dataset Version and Update\n* Version Details:\n * Current version: 1.0\n * Last Update: 16/02/2024\n * First Release: 16/02/2024\n\n* Update Plan: We will keep updating as we gather additional data.## Licensing Information\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License."
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f71d3eb69539dada81918d17c182c99eeb1d126c |
# Test dataset with one broken split | severo/test-one-split-broken | [
"region:us"
] | 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 | [
"# Test dataset with one broken split"
] | [
"TAGS\n#region-us \n",
"# Test dataset with one broken split"
] | [
6,
8
] | [
"passage: TAGS\n#region-us \n# Test dataset with one broken split"
] | [
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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 | [
"license:apache-2.0",
"region:us"
] | 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."
} | [
"## Dataset Description\n\nYour dataset description goes here. Describe what the dataset contains, its purpose, and any relevant information about its creation and usage.",
"## Data Format\n\nEach entry in the dataset consists of two fields:\n\n- 'audio_path': The path to the audio file.\n- 'description': The written description corresponding to the audio file.",
"## Example\n\nHere's an example entry from the dataset:\n\n'''json\n{\n \"audio_path\": \"/path/to/audio/file.mp3\",\n \"description\": \"A written description of the audio content.\"\n}"
] | [
"TAGS\n#license-apache-2.0 #region-us \n",
"## Dataset Description\n\nYour dataset description goes here. Describe what the dataset contains, its purpose, and any relevant information about its creation and usage.",
"## Data Format\n\nEach entry in the dataset consists of two fields:\n\n- 'audio_path': The path to the audio file.\n- 'description': The written description corresponding to the audio file.",
"## Example\n\nHere's an example entry from the dataset:\n\n'''json\n{\n \"audio_path\": \"/path/to/audio/file.mp3\",\n \"description\": \"A written description of the audio content.\"\n}"
] | [
14,
33,
47,
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] | [
"passage: TAGS\n#license-apache-2.0 #region-us \n## Dataset Description\n\nYour dataset description goes here. Describe what the dataset contains, its purpose, and any relevant information about its creation and usage.## Data Format\n\nEach entry in the dataset consists of two fields:\n\n- 'audio_path': The path to the audio file.\n- 'description': The written description corresponding to the audio file.## Example\n\nHere's an example entry from the dataset:\n\n'''json\n{\n \"audio_path\": \"/path/to/audio/file.mp3\",\n \"description\": \"A written description of the audio content.\"\n}"
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-0.0470825619995594,
-0.06549250334501266
] |
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
{
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"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": {
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},
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"acc_norm_stderr": 0.042039210401562783
},
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},
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},
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},
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},
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"harness|hendrycksTest-college_medicine|5": {
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},
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},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.4117647058823529,
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_statistics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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},
"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 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] | open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2 | [
"region:us"
] | 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\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.43829787234042555,\n \"acc_stderr\": 0.032436186361081004,\n \"acc_norm\": 0.43829787234042555,\n \"acc_norm_stderr\": 0.032436186361081004\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.040969851398436716,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.040969851398436716\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.041546596717075474,\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.041546596717075474\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.34656084656084657,\n \"acc_stderr\": 0.024508777521028417,\n \"acc_norm\": 0.34656084656084657,\n \"acc_norm_stderr\": 0.024508777521028417\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03718489006818115,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03718489006818115\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.47096774193548385,\n \"acc_stderr\": 0.028396016402761,\n \"acc_norm\": 0.47096774193548385,\n \"acc_norm_stderr\": 0.028396016402761\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3497536945812808,\n \"acc_stderr\": 0.033554009049695646,\n \"acc_norm\": 0.3497536945812808,\n \"acc_norm_stderr\": 0.033554009049695646\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6121212121212121,\n \"acc_stderr\": 0.03804913653971012,\n \"acc_norm\": 0.6121212121212121,\n \"acc_norm_stderr\": 0.03804913653971012\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.035402943770953675,\n \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.035402943770953675\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.538860103626943,\n \"acc_stderr\": 0.035975244117345775,\n \"acc_norm\": 0.538860103626943,\n \"acc_norm_stderr\": 0.035975244117345775\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.33589743589743587,\n \"acc_stderr\": 0.023946724741563976,\n \"acc_norm\": 0.33589743589743587,\n \"acc_norm_stderr\": 0.023946724741563976\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683515,\n \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683515\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.03196876989195778,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.03196876989195778\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008937,\n \"acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008937\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5596330275229358,\n \"acc_stderr\": 0.02128431062376155,\n \"acc_norm\": 0.5596330275229358,\n \"acc_norm_stderr\": 0.02128431062376155\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.030546745264953178,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.030546745264953178\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.03476099060501636,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.03476099060501636\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5991561181434599,\n \"acc_stderr\": 0.031900803894732356,\n \"acc_norm\": 0.5991561181434599,\n \"acc_norm_stderr\": 0.031900803894732356\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5112107623318386,\n \"acc_stderr\": 0.033549366530984746,\n \"acc_norm\": 0.5112107623318386,\n \"acc_norm_stderr\": 0.033549366530984746\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5190839694656488,\n \"acc_stderr\": 0.04382094705550988,\n \"acc_norm\": 0.5190839694656488,\n \"acc_norm_stderr\": 0.04382094705550988\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6611570247933884,\n \"acc_stderr\": 0.043207678075366705,\n \"acc_norm\": 0.6611570247933884,\n \"acc_norm_stderr\": 0.043207678075366705\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04833682445228318,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04833682445228318\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.4110429447852761,\n \"acc_stderr\": 0.038656978537853624,\n \"acc_norm\": 0.4110429447852761,\n \"acc_norm_stderr\": 0.038656978537853624\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\": 0.6504854368932039,\n \"acc_stderr\": 0.047211885060971716,\n \"acc_norm\": 0.6504854368932039,\n \"acc_norm_stderr\": 0.047211885060971716\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7307692307692307,\n \"acc_stderr\": 0.029058588303748845,\n \"acc_norm\": 0.7307692307692307,\n \"acc_norm_stderr\": 0.029058588303748845\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.5900383141762452,\n \"acc_stderr\": 0.017587672312336048,\n \"acc_norm\": 0.5900383141762452,\n \"acc_norm_stderr\": 0.017587672312336048\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5289017341040463,\n \"acc_stderr\": 0.02687408588351835,\n \"acc_norm\": 0.5289017341040463,\n \"acc_norm_stderr\": 0.02687408588351835\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n \"acc_stderr\": 0.01442229220480884,\n \"acc_norm\": 0.24692737430167597,\n \"acc_norm_stderr\": 0.01442229220480884\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.02843109544417664,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.02843109544417664\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.43086816720257237,\n \"acc_stderr\": 0.028125340983972714,\n \"acc_norm\": 0.43086816720257237,\n \"acc_norm_stderr\": 0.028125340983972714\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.027777777777777804,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.027777777777777804\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028121636040639882,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028121636040639882\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3468057366362451,\n \"acc_stderr\": 0.012156071332318713,\n \"acc_norm\": 0.3468057366362451,\n \"acc_norm_stderr\": 0.012156071332318713\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.3272058823529412,\n \"acc_stderr\": 0.02850145286039655,\n \"acc_norm\": 0.3272058823529412,\n \"acc_norm_stderr\": 0.02850145286039655\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.42483660130718953,\n \"acc_stderr\": 0.019997973035458336,\n \"acc_norm\": 0.42483660130718953,\n \"acc_norm_stderr\": 0.019997973035458336\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.42448979591836733,\n \"acc_stderr\": 0.031642094879429414,\n \"acc_norm\": 0.42448979591836733,\n \"acc_norm_stderr\": 0.031642094879429414\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.5970149253731343,\n \"acc_stderr\": 0.034683432951111266,\n \"acc_norm\": 0.5970149253731343,\n \"acc_norm_stderr\": 0.034683432951111266\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4036144578313253,\n \"acc_stderr\": 0.038194861407583984,\n \"acc_norm\": 0.4036144578313253,\n \"acc_norm_stderr\": 0.038194861407583984\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.5614035087719298,\n \"acc_stderr\": 0.038057975055904594,\n \"acc_norm\": 0.5614035087719298,\n \"acc_norm_stderr\": 0.038057975055904594\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2582619339045288,\n \"mc1_stderr\": 0.015321821688476196,\n \"mc2\": 0.4123912546191622,\n \"mc2_stderr\": 0.013946155781929979\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6156274664561957,\n \"acc_stderr\": 0.013671567600836196\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.31311599696739956,\n \"acc_stderr\": 0.01277428566938509\n }\n}\n```", "repo_url": "https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2", "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_08_02.040553", "path": ["**/details_harness|arc:challenge|25_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|gsm8k|5_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hellaswag|10_2024-02-16T14-08-02.040553.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2024-02-16T14-08-02.040553.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2024_02_16T14_08_02.040553", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-anatomy|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-astronomy|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-college_biology|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-college_physics|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-computer_security|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-econometrics|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-global_facts|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T14-08-02.040553.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T14-08-02.040553.parquet", 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#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
| [
"# Dataset Card for Evaluation run of aloobun/Reyna-Mini-1.8B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/Reyna-Mini-1.8B-v0.2 on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"# Dataset Card for Evaluation run of aloobun/Reyna-Mini-1.8B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/Reyna-Mini-1.8B-v0.2 on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of aloobun/Reyna-Mini-1.8B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/Reyna-Mini-1.8B-v0.2 on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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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 | [
"region:us"
] | 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 | [
"# Dataset Card for \"reformat\"\n\nMore Information needed"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for \"reformat\"\n\nMore Information needed"
] | [
6,
12
] | [
"passage: TAGS\n#region-us \n# Dataset Card for \"reformat\"\n\nMore Information needed"
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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
{
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"mc1_stderr": 0.016371836286454604,
"mc2": 0.507583773070451,
"mc2_stderr": 0.014770823381787772
},
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},
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"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
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[More Information Needed] | open-llm-leaderboard/details_NovoCode__Tiger-DPO | [
"region:us"
] | 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\": 0.026730620728004913,\n \"acc_norm\": 0.6688102893890675,\n \"acc_norm_stderr\": 0.026730620728004913\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6635802469135802,\n \"acc_stderr\": 0.02628973494595293,\n \"acc_norm\": 0.6635802469135802,\n \"acc_norm_stderr\": 0.02628973494595293\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.43617021276595747,\n \"acc_stderr\": 0.02958345203628407,\n \"acc_norm\": 0.43617021276595747,\n \"acc_norm_stderr\": 0.02958345203628407\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4132985658409387,\n \"acc_stderr\": 0.012576779494860087,\n \"acc_norm\": 0.4132985658409387,\n \"acc_norm_stderr\": 0.012576779494860087\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.029349803139765873,\n \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.029349803139765873\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5931372549019608,\n \"acc_stderr\": 0.019873802005061173,\n \"acc_norm\": 0.5931372549019608,\n \"acc_norm_stderr\": 0.019873802005061173\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.689795918367347,\n \"acc_stderr\": 0.029613459872484378,\n \"acc_norm\": 0.689795918367347,\n \"acc_norm_stderr\": 0.029613459872484378\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\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\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32313341493268055,\n \"mc1_stderr\": 0.016371836286454604,\n \"mc2\": 0.507583773070451,\n \"mc2_stderr\": 0.014770823381787772\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.011947592365207397\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.41015921152388174,\n \"acc_stderr\": 0.013548335117860353\n }\n}\n```", "repo_url": "https://huggingface.co/NovoCode/Tiger-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": 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["results_2024-02-16T14-08-40.800066.parquet"]}, {"split": "2024_02_16T14_49_32.301206", "path": ["results_2024-02-16T14-49-32.301206.parquet"]}, {"split": "latest", "path": ["results_2024-02-16T14-49-32.301206.parquet"]}]}]} | 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
| [
"# Dataset Card for Evaluation run of NovoCode/Tiger-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Tiger-DPO on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of NovoCode/Tiger-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Tiger-DPO on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NovoCode/Tiger-DPO\n\n\n\nDataset automatically created during the evaluation run of model NovoCode/Tiger-DPO on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
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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
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},
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},
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},
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}
```
## Dataset Details
### Dataset Description
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[More Information Needed] | open-llm-leaderboard/details_CorticalStack__mistral-7b-openhermes-sft | [
"region:us"
] | 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": 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#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
| [
"# Dataset Card for Evaluation run of CorticalStack/mistral-7b-openhermes-sft\n\n\n\nDataset automatically created during the evaluation run of model CorticalStack/mistral-7b-openhermes-sft on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of CorticalStack/mistral-7b-openhermes-sft\n\n\n\nDataset automatically created during the evaluation run of model CorticalStack/mistral-7b-openhermes-sft on the 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).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] | [
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of CorticalStack/mistral-7b-openhermes-sft\n\n\n\nDataset automatically created during the evaluation run of model CorticalStack/mistral-7b-openhermes-sft on the 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).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 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\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]"
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7eadbd8948d5b6ddda5948c349994bfa2294c1ef |
## The annotations are in the **annotations_coco.json** file | saberder/humpback | [
"task_categories:object-detection",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"biology",
"region:us"
] | 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 | [] | [
"en"
] | 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 | [
"## The annotations are in the annotations_coco.json file"
] | [
"TAGS\n#task_categories-object-detection #task_categories-image-classification #size_categories-1K<n<10K #language-English #biology #region-us \n",
"## The annotations are in the annotations_coco.json file"
] | [
47,
18
] | [
"passage: TAGS\n#task_categories-object-detection #task_categories-image-classification #size_categories-1K<n<10K #language-English #biology #region-us \n## The annotations are in the annotations_coco.json file"
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