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bfb554d35d0164a98e3580335d1e56ad777ad078
|
# Dataset Card for "babylm-100M-aochildes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-aochildes
|
[
"region:us"
] |
2023-09-16T04:11:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20503941, "num_examples": 763989}, {"name": "valid", "num_bytes": 1987198, "num_examples": 70000}, {"name": "test", "num_bytes": 1648555, "num_examples": 60000}], "download_size": 13398724, "dataset_size": 24139694}}
|
2023-09-16T04:11:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-aochildes"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-aochildes\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-aochildes\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-aochildes\"\n\nMore Information needed"
] |
9eecf3d895a53e798e48c5c15fc272e84b7f32f6
|
# Dataset Card for "babylm-100M-bnc-spoken"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-bnc-spoken
|
[
"region:us"
] |
2023-09-16T04:16:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45016509, "num_examples": 848205}, {"name": "valid", "num_bytes": 4721951, "num_examples": 89921}, {"name": "test", "num_bytes": 5165775, "num_examples": 99951}], "download_size": 33089084, "dataset_size": 54904235}}
|
2023-09-16T04:16:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-bnc-spoken"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-bnc-spoken\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-bnc-spoken\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-bnc-spoken\"\n\nMore Information needed"
] |
3a1254ff8000507c37187182e26133e964a0dfc5
|
# Dataset Card for "babylm-100M-cbt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-cbt
|
[
"region:us"
] |
2023-09-16T04:16:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26532921, "num_examples": 263519}, {"name": "valid", "num_bytes": 1220938, "num_examples": 12747}, {"name": "test", "num_bytes": 1578682, "num_examples": 16646}], "download_size": 17973969, "dataset_size": 29332541}}
|
2023-09-16T04:17:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-cbt"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-cbt\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-cbt\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-cbt\"\n\nMore Information needed"
] |
5fed925240049febe37f0bc3487dd3e51ed23002
|
# Dataset of suou_momoko/周防桃子 (THE iDOLM@STER: Million Live!)
This is the dataset of suou_momoko/周防桃子 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are `blue_eyes, brown_hair, short_hair, bangs, ahoge, hair_ornament, hair_flower`, which are 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 | 646.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 358.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1209 | 794.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 569.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1209 | 1.14 GiB | [Download](https://huggingface.co/datasets/CyberHarem/suou_momoko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/suou_momoko_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, yellow_dress, floral_print, simple_background, white_background, long_sleeves, open_mouth, upper_body, short_sleeves, smile, v-shaped_eyebrows, white_flower |
| 1 | 9 |  |  |  |  |  | 1girl, blush, flower, looking_at_viewer, solo, yellow_dress, simple_background, white_background, floral_print, upper_body, smile, collarbone, wavy_hair |
| 2 | 9 |  |  |  |  |  | 1girl, blue_bow, bracelet, hair_bow, solo, blush, looking_at_viewer, open_mouth, puffy_short_sleeves, orange_bow, bowtie, sailor_collar, star_hair_ornament, white_dress, frilled_dress, :d, collarbone, holding |
| 3 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, puffy_short_sleeves, solo, wrist_cuffs, apron, blue_dress, short_twintails, :d, alice_(alice_in_wonderland)_(cosplay), blue_ribbon, open_mouth, frilled_dress, hair_ribbon, simple_background, blue_bowtie, card, hair_bow, heart, low_twintails, white_background |
| 4 | 10 |  |  |  |  |  | 1girl, beret, blush, red_headwear, solo, white_shirt, pinafore_dress, long_sleeves, looking_at_viewer, bowtie, simple_background, upper_body, white_background, light_brown_hair, blunt_bangs, open_mouth, striped, wavy_hair |
| 5 | 7 |  |  |  |  |  | 1girl, blush, looking_at_viewer, maid_headdress, pink_bowtie, puffy_short_sleeves, solo, wrist_cuffs, enmaided, frilled_apron, simple_background, white_apron, pink_dress, white_background, :o, frilled_sleeves, heart_hands, open_mouth, skirt, upper_body, waist_apron, white_shirt |
| 6 | 6 |  |  |  |  |  | 1girl, black_gloves, blush, cat_ears, jingle_bell, mini_crown, solo, animal_ear_fluff, fur_trim, looking_at_viewer, puffy_short_sleeves, blue_bow, dress, epaulettes, neck_bell, open_mouth, :d, blurry, cat_tail, frilled_sleeves, gold_trim, holding, simple_background, striped_bowtie, upper_body |
| 7 | 6 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, solo, small_breasts, bow_bikini, simple_background, blue_bikini, sailor_bikini, smile, white_background, white_bikini |
| 8 | 15 |  |  |  |  |  | 1boy, blush, hetero, 1girl, nipples, small_breasts, solo_focus, open_mouth, penis, loli, navel, spread_legs, vaginal, sweat, bar_censor, completely_nude, cum_in_pussy, flower, saliva, tears, sex_from_behind, straddling |
| 9 | 9 |  |  |  |  |  | 1girl, solo, day, looking_at_viewer, outdoors, blue_sky, beach, cloud, blush, ocean, smile, barefoot, black_bikini, frilled_bikini, navel, open_mouth, small_breasts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | looking_at_viewer | solo | yellow_dress | floral_print | simple_background | white_background | long_sleeves | open_mouth | upper_body | short_sleeves | smile | v-shaped_eyebrows | white_flower | flower | collarbone | wavy_hair | blue_bow | bracelet | hair_bow | puffy_short_sleeves | orange_bow | bowtie | sailor_collar | star_hair_ornament | white_dress | frilled_dress | :d | holding | wrist_cuffs | apron | blue_dress | short_twintails | alice_(alice_in_wonderland)_(cosplay) | blue_ribbon | hair_ribbon | blue_bowtie | card | heart | low_twintails | beret | red_headwear | white_shirt | pinafore_dress | light_brown_hair | blunt_bangs | striped | maid_headdress | pink_bowtie | enmaided | frilled_apron | white_apron | pink_dress | :o | frilled_sleeves | heart_hands | skirt | waist_apron | black_gloves | cat_ears | jingle_bell | mini_crown | animal_ear_fluff | fur_trim | dress | epaulettes | neck_bell | blurry | cat_tail | gold_trim | striped_bowtie | navel | small_breasts | bow_bikini | blue_bikini | sailor_bikini | white_bikini | 1boy | hetero | nipples | solo_focus | penis | loli | spread_legs | vaginal | sweat | bar_censor | completely_nude | cum_in_pussy | saliva | tears | sex_from_behind | straddling | day | outdoors | blue_sky | beach | cloud | ocean | barefoot | black_bikini | frilled_bikini |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:--------------------|:-------|:---------------|:---------------|:--------------------|:-------------------|:---------------|:-------------|:-------------|:----------------|:--------|:--------------------|:---------------|:---------|:-------------|:------------|:-----------|:-----------|:-----------|:----------------------|:-------------|:---------|:----------------|:---------------------|:--------------|:----------------|:-----|:----------|:--------------|:--------|:-------------|:------------------|:----------------------------------------|:--------------|:--------------|:--------------|:-------|:--------|:----------------|:--------|:---------------|:--------------|:-----------------|:-------------------|:--------------|:----------|:-----------------|:--------------|:-----------|:----------------|:--------------|:-------------|:-----|:------------------|:--------------|:--------|:--------------|:---------------|:-----------|:--------------|:-------------|:-------------------|:-----------|:--------|:-------------|:------------|:---------|:-----------|:------------|:-----------------|:--------|:----------------|:-------------|:--------------|:----------------|:---------------|:-------|:---------|:----------|:-------------|:--------|:-------|:--------------|:----------|:--------|:-------------|:------------------|:---------------|:---------|:--------|:------------------|:-------------|:------|:-----------|:-----------|:--------|:--------|:--------|:-----------|:---------------|:-----------------|
| 0 | 9 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | 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 | 10 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | X | X | X | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 15 |  |  |  |  |  | X | X | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | X | X | X | X | X | 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 |
|
CyberHarem/suou_momoko_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T04:17:12+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T20:13:37+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of suou\_momoko/周防桃子 (THE iDOLM@STER: Million Live!)
============================================================
This is the dataset of suou\_momoko/周防桃子 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are 'blue\_eyes, brown\_hair, short\_hair, bangs, ahoge, hair\_ornament, hair\_flower', which are 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"
] |
b57a5642c4e618ec97bbd2c76021d81377a0b403
|
# Dataset Card for "babylm-100M-children-stories"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-children-stories
|
[
"region:us"
] |
2023-09-16T04:17:12+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17676869, "num_examples": 76758}, {"name": "valid", "num_bytes": 1425137, "num_examples": 5996}, {"name": "test", "num_bytes": 1804421, "num_examples": 7959}], "download_size": 12749002, "dataset_size": 20906427}}
|
2023-09-16T04:17:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-children-stories"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-children-stories\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-children-stories\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-children-stories\"\n\nMore Information needed"
] |
593efd964a46dc6f95521e73a6b54f8f5258e901
|
# Dataset Card for "babylm-100M-qed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-qed
|
[
"region:us"
] |
2023-09-16T04:17:49+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 59840684, "num_examples": 959659}, {"name": "valid", "num_bytes": 5678320, "num_examples": 94976}, {"name": "test", "num_bytes": 7027994, "num_examples": 114964}], "download_size": 44263522, "dataset_size": 72546998}}
|
2023-09-16T04:18:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-qed"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-qed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-qed\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-qed\"\n\nMore Information needed"
] |
a85166b12fe07ca2148882a7a00f1c31beb81b77
|
# Dataset Card for "babylm-100M-simple-wikipedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-simple-wikipedia
|
[
"region:us"
] |
2023-09-16T04:18:34+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 89553968, "num_examples": 567006}, {"name": "valid", "num_bytes": 9591764, "num_examples": 60977}, {"name": "test", "num_bytes": 11102812, "num_examples": 66392}], "download_size": 66257903, "dataset_size": 110248544}}
|
2023-09-16T04:19:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-simple-wikipedia"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-simple-wikipedia\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-simple-wikipedia\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-simple-wikipedia\"\n\nMore Information needed"
] |
cea80e48f553afa2ceed9c3a04517d672fff1a8e
|
# Dataset Card for "babylm-100M-switchboard"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
deven367/babylm-100M-switchboard
|
[
"region:us"
] |
2023-09-16T04:22:56+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "valid", "path": "data/valid-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6586033, "num_examples": 161740}, {"name": "valid", "num_bytes": 724013, "num_examples": 18000}, {"name": "test", "num_bytes": 823158, "num_examples": 20000}], "download_size": 4871927, "dataset_size": 8133204}}
|
2023-09-16T04:23:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "babylm-100M-switchboard"
More Information needed
|
[
"# Dataset Card for \"babylm-100M-switchboard\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"babylm-100M-switchboard\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"babylm-100M-switchboard\"\n\nMore Information needed"
] |
effe254821e8901bf863872c63887970eef9a589
|
# Dataset of satake_minako/佐竹美奈子 (THE iDOLM@STER: Million Live!)
This is the dataset of satake_minako/佐竹美奈子 (THE iDOLM@STER: Million Live!), containing 472 images and their tags.
The core tags of this character are `brown_hair, blue_eyes, ponytail, breasts, long_hair, bangs, bow, hair_bow, 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 | 472 | 463.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 472 | 315.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1121 | 653.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 472 | 427.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1121 | 840.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/satake_minako_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, :d, looking_at_viewer, open_mouth, solo, striped, character_name, hair_ribbon, shorts |
| 1 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, smile, open_mouth, large_breasts, cleavage, naked_apron |
| 2 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, cleavage, tasuki, collarbone, large_breasts, onsen, yukata |
| 3 | 6 |  |  |  |  |  | 1girl, blush, navel, nipples, nude, solo, female_pubic_hair, smile, collarbone, looking_at_viewer, open_mouth |
| 4 | 21 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, nipples, open_mouth, vaginal, smile, navel, penis, sweat, female_pubic_hair, looking_at_viewer, pussy, completely_nude, large_breasts, spread_legs, girl_on_top, happy_sex, cowgirl_position, mosaic_censoring, pov |
| 5 | 7 |  |  |  |  |  | 1girl, blush, open_mouth, solo, :d, collarbone, looking_at_viewer, upper_body, bare_shoulders, red_bow, sidelocks, simple_background, striped_shirt, holding, long_sleeves, upper_teeth_only, white_shirt, ladle, off-shoulder_shirt, white_background |
| 6 | 6 |  |  |  |  |  | 1girl, blush, smile, solo, sunflower, yellow_flower, blue_sky, cloud, day, flower_field, holding_flower, looking_at_viewer, outdoors, red_bow, blurry, dress, open_mouth, shirt, upper_body |
| 7 | 13 |  |  |  |  |  | 1girl, china_dress, looking_at_viewer, smile, solo, blush, cleavage_cutout, bun_cover, hair_bun, open_mouth, wrist_cuffs, baozi, blue_dress, large_breasts, white_background |
| 8 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, sleeveless_dress, solo, white_dress, bare_shoulders, blush, earrings, white_gloves, large_breasts, open_mouth, sidelocks, smile, arms_up, closed_mouth, dated, heart, upper_body |
| 9 | 10 |  |  |  |  |  | bikini, cleavage, day, smile, 1girl, cloud, looking_at_viewer, outdoors, solo, blue_sky, beach, navel, ocean, blush, collarbone, hair_ribbon, open_mouth |
| 10 | 6 |  |  |  |  |  | 1girl, china_dress, floral_print, side_slit, blush, cowboy_shot, looking_at_viewer, open_mouth, print_dress, red_dress, smile, solo, standing, short_dress, sidelocks, sleeveless_dress, folding_fan, hair_ornament, holding_fan, jewelry, medium_hair, simple_background, white_background |
| 11 | 9 |  |  |  |  |  | 1girl, hair_ribbon, looking_at_viewer, plaid_skirt, pleated_skirt, school_uniform, solo, miniskirt, smile, white_shirt, blush, striped, long_sleeves, polka_dot, red_skirt, holding, medium_hair, open_mouth, blazer, closed_mouth, collared_shirt, dress_shirt, red_necktie, simple_background, sweater_vest, white_background, wing_collar |
| 12 | 9 |  |  |  |  |  | 1girl, cleavage, detached_collar, playboy_bunny, rabbit_ears, solo, looking_at_viewer, smile, fake_animal_ears, strapless_leotard, wrist_cuffs, black_pantyhose, open_mouth, bare_shoulders, black_bowtie, black_leotard, rabbit_tail, simple_background, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :d | looking_at_viewer | open_mouth | solo | striped | character_name | hair_ribbon | shorts | blush | smile | large_breasts | cleavage | naked_apron | tasuki | collarbone | onsen | yukata | navel | nipples | nude | female_pubic_hair | hetero | solo_focus | 1boy | vaginal | penis | sweat | pussy | completely_nude | spread_legs | girl_on_top | happy_sex | cowgirl_position | mosaic_censoring | pov | upper_body | bare_shoulders | red_bow | sidelocks | simple_background | striped_shirt | holding | long_sleeves | upper_teeth_only | white_shirt | ladle | off-shoulder_shirt | white_background | sunflower | yellow_flower | blue_sky | cloud | day | flower_field | holding_flower | outdoors | blurry | dress | shirt | china_dress | cleavage_cutout | bun_cover | hair_bun | wrist_cuffs | baozi | blue_dress | sleeveless_dress | white_dress | earrings | white_gloves | arms_up | closed_mouth | dated | heart | bikini | beach | ocean | floral_print | side_slit | cowboy_shot | print_dress | red_dress | standing | short_dress | folding_fan | hair_ornament | holding_fan | jewelry | medium_hair | plaid_skirt | pleated_skirt | school_uniform | miniskirt | polka_dot | red_skirt | blazer | collared_shirt | dress_shirt | red_necktie | sweater_vest | wing_collar | detached_collar | playboy_bunny | rabbit_ears | fake_animal_ears | strapless_leotard | black_pantyhose | black_bowtie | black_leotard | rabbit_tail |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----|:--------------------|:-------------|:-------|:----------|:-----------------|:--------------|:---------|:--------|:--------|:----------------|:-----------|:--------------|:---------|:-------------|:--------|:---------|:--------|:----------|:-------|:--------------------|:---------|:-------------|:-------|:----------|:--------|:--------|:--------|:------------------|:--------------|:--------------|:------------|:-------------------|:-------------------|:------|:-------------|:-----------------|:----------|:------------|:--------------------|:----------------|:----------|:---------------|:-------------------|:--------------|:--------|:---------------------|:-------------------|:------------|:----------------|:-----------|:--------|:------|:---------------|:-----------------|:-----------|:---------|:--------|:--------|:--------------|:------------------|:------------|:-----------|:--------------|:--------|:-------------|:-------------------|:--------------|:-----------|:---------------|:----------|:---------------|:--------|:--------|:---------|:--------|:--------|:---------------|:------------|:--------------|:--------------|:------------|:-----------|:--------------|:--------------|:----------------|:--------------|:----------|:--------------|:--------------|:----------------|:-----------------|:------------|:------------|:------------|:---------|:-----------------|:--------------|:--------------|:---------------|:--------------|:------------------|:----------------|:--------------|:-------------------|:--------------------|:------------------|:---------------|:----------------|:--------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | X | X | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | | X | | | | | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | X | X | | | | | X | X | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 21 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | X | X | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 13 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 10 |  |  |  |  |  | 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 | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 11 | 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 | | | | | | | | | |
| 12 | 9 |  |  |  |  |  | X | | X | X | X | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
CyberHarem/satake_minako_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T04:39:58+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:06:52+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of satake\_minako/佐竹美奈子 (THE iDOLM@STER: Million Live!)
===============================================================
This is the dataset of satake\_minako/佐竹美奈子 (THE iDOLM@STER: Million Live!), containing 472 images and their tags.
The core tags of this character are 'brown\_hair, blue\_eyes, ponytail, breasts, long\_hair, bangs, bow, hair\_bow, 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"
] |
e5251cf8e4f1b8f5234ce854fc2749c8d33be896
|
# Dataset of tenkuubashi_tomoka/天空橋朋花 (THE iDOLM@STER: Million Live!)
This is the dataset of tenkuubashi_tomoka/天空橋朋花 (THE iDOLM@STER: Million Live!), containing 175 images and their tags.
The core tags of this character are `brown_hair, brown_eyes, long_hair, hair_bun, single_hair_bun, bangs, breasts, 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 | 175 | 205.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 175 | 129.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 393 | 266.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 175 | 185.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 393 | 357.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, smile, solo, heart, looking_at_viewer, fingerless_gloves, belt, hand_fan, open_mouth, sitting |
| 1 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, medium_breasts, simple_background, solo, white_background, blush, cleavage, collarbone, cowboy_shot, navel, bare_shoulders, babydoll, bikini, lingerie, panties, smile |
| 2 | 14 |  |  |  |  |  | 1girl, solo, looking_at_viewer, bare_shoulders, rose, smile, red_eyes, black_dress, cleavage, feathered_wings, medium_breasts, black_gloves, blue_flower, feathers, hair_flower, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | heart | looking_at_viewer | fingerless_gloves | belt | hand_fan | open_mouth | sitting | medium_breasts | simple_background | white_background | blush | cleavage | collarbone | cowboy_shot | navel | bare_shoulders | babydoll | bikini | lingerie | panties | rose | red_eyes | black_dress | feathered_wings | black_gloves | blue_flower | feathers | hair_flower |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------|:--------------------|:--------------------|:-------|:-----------|:-------------|:----------|:-----------------|:--------------------|:-------------------|:--------|:-----------|:-------------|:--------------|:--------|:-----------------|:-----------|:---------|:-----------|:----------|:-------|:-----------|:--------------|:------------------|:---------------|:--------------|:-----------|:--------------|
| 0 | 8 |  |  |  |  |  | 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 | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | X | X | | X | | | | X | | X | | | | X | | | | X | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/tenkuubashi_tomoka_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T05:06:49+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:37:54+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tenkuubashi\_tomoka/天空橋朋花 (THE iDOLM@STER: Million Live!)
====================================================================
This is the dataset of tenkuubashi\_tomoka/天空橋朋花 (THE iDOLM@STER: Million Live!), containing 175 images and their tags.
The core tags of this character are 'brown\_hair, brown\_eyes, long\_hair, hair\_bun, single\_hair\_bun, bangs, breasts, 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"
] |
77e86c92de38ddb3fdd7f96f3318e8e694b8f80e
|
###Human: Śrīla Prabhupāda: This movement is especially meant to enable a human being to reach the real goal of life.
###Assistant: The real goal... ?
###Human: Is the real goal of life to know God?
###Assistant: Yes. To go back home, back to Godhead. That is the real goal of life. The water that comes from the sea forms clouds, the clouds fall down as rain, and the actual goal is to flow down the river and again enter the sea. So, we have come from God, and now we are embarrassed by material life. Therefore, our aim should be to get out of this embarrassing situation and go back home, back to Godhead. This is the real goal of life.
###Human: mām upetya punar janma duḥkhālayam aśāśvatam nāpnuvanti mahātmānaḥ saṁsiddhiṁ paramāṁ gatāḥ ["After attaining Me, the great souls, who are yogīs in devotion, never return to this temporary world, which is full of miseries, because they have attained the highest perfection."]
###Assistant: That is the version of Bhagavad-gītā. If anyone comes to Me—mām upetya: he does not come back again. Where? To this place—duḥkhālayam aśāśvatam [Bg. 8.15]. This place is the abode of miseries. Everyone knows, but they have been befooled by so-called leaders. Material life is miserable life. Kṛṣṇa says, God says, that this place is duḥkhālayam—it is a place of miseries. And it is also aśāśvatam, temporary. You cannot make a compromise: “All right, let it be miserable. I shall remain here as an American or Indian.” No. That also you cannot do. You cannot remain an American. You may think that, having been born in America, you are very happy. But you cannot remain an American for long. You will have to be kicked out of that place. And your next life you do not know! Therefore, it is duḥkhālayam aśāśvatam [Bg. 8.15]—miserable and temporary. That is our philosophy.
###Human: But when you have some knowledge of God, then life is not so miserable?
###Assistant: No! Some knowledge will not do. You must have perfect knowledge.
###Human: janma karma ca me divyam evaṁ yo vetti tattvataḥ [Bg. 4.9] Tattvataḥ means “perfectly.” Perfect knowledge is being taught in Bhagavad-gītā. So, we are giving everyone in human society a chance to learn Bhagavad-gītā as it is and make his life perfect. That is the Kṛṣṇa consciousness movement. What does your science say about the transmigration of the soul?
###Assistant: Bob: I think... that science... cannot deny or affirm it. Science does not know it.
###Human: Therefore I say that science is imperfect.
###Assistant: Bob: Science may. though, say something. It is said in science that energy is never destroyed; it is changed.
###Human: That’s all right. But how the energy is working in the future—that science does not know. How is the energy diverted? How, by different manipulations, is the energy working differently? For instance, electrical energy. By different handling it is operating the heats and it is operating the refrigerator. They are just the opposite, but the electrical energy is the same. Similarly, this energy—living energy—how is it being directed? Which way is it going? How is it fructifying in the next life? That they do not know. But in Bhagavad-gītā it is very simply stated.
###Assistant: vāsāṁsi jīrṇāni yathā vihāya [Bg. 2.22] You are covered by a dress, by a shirt. When this shirt is unusable, you change it. Similarly, this body is just like a shirt and coat. When it is no longer workable, we have to change it.
###Human: What is the “we” that has to change? What is constant?
###Assistant: That is the soul.
###Human: From one life to the next?
###Assistant: That is the soul—I. What “you” is speaking? You! What “I” is speaking? Identity: ātmā, or soul.
###Human: My soul is different from your soul?
###Assistant: Yes. You are an individual soul, I am an individual soul.
###Human: You have removed yourself from karmic influences. If I was to remove myself from karmic influences, would our souls be the same or different?
###Assistant: The soul is of the same quality in all. You are under a certain conception of life at the present moment, and these countrymen of yours [the Kṛṣṇa conscious devotees] were under a certain conception of life, but by training they have taken to another conception of life. So the ultimate training is how to become Kṛṣṇa conscious. That is the perfection.
###Human: If two people are Kṛṣṇa conscious, is their soul the same?
###Assistant: The soul is always the same.
###Human: In each person? In each person is it the same?
###Assistant: Yes.
###Human: [pointing to two devotees] If these two are Kṛṣṇa conscious, are their souls the same?
###Assistant: The soul is the same but always individual, even if one is not Kṛṣṇa conscious. For instance, you are a human being, and I am a human being. Even if I am not a Christian, even if you are not a Hindu, still we are human beings. Similarly, the soul may not be Kṛṣṇa conscious, or he may be Kṛṣṇa conscious—it doesn’t matter. But the soul is the soul.
###Human: Can you tell me more about this?
###Assistant: Soul—as pure spirit, all souls are equal. Even in an animal. Therefore it is said, paṇḍitāḥ sama-darśinaḥ: [Bg. 5.18] those who are actually learned do not see the outward covering, either in a human being or in an animal.
###Human: If I may ask another question on this?
###Assistant: Yes.
###Human: I have considered the soul somewhat as part of God. At times I think I feel God. I’m here, and you may say God is here. So if the soul is inside me, then should I be able
to feel God inside me? Not all of God, I mean, but a...
###Assistant: Yes. God is inside also. God is everywhere. God is inside and outside also. This is to be known.
###Human: How do you feel God inside you?
###Assistant: Not in the beginning, but you have to know from the śāstras [scriptures], by the Vedic information. For example, in the Bhagavad-gītā it is said, īśvaraḥ sarva-bhūtānāṁ hṛd-deśe ’rjuna tiṣṭhati: [Bg. 18.61] God is there in everyone’s heart. Paramāṇu-cayāntara-stham: God is also within every atom. So this is the first information. And then, by the yogic process, you have to realize it.
###Human: Yogic process?
###Assistant: Yes.
###Human: Is chanting Hare Kṛṣṇa such a yogic process?
###Assistant: Yes, it is also a yogic process.
###Human: What kind of yogic process must I do to find out—to feel this information—to feel the soul inside?
###Assistant: Yes, there are many different yogic Processes, but for this age this process is very nice.
###Human: Chanting.
###Assistant: Yes.
###Human: Through this I can feel not only God outside but God inside?
###Assistant: You’ll understand everything of God—how God is inside, how God is outside, how God is working. Everything will be revealed. By this attitude of service, God will reveal Himself. You cannot understand God by your endeavor. Only if God reveals Himself. For instance, when the sun is out of your sight at night, you cannot see it by your torchlight, or any light. But in the morning you can see the sun automatically. without any torchlight. Similarly, you have to create a situation—you have to put yourself in a situation—in which God will be revealed. It is not that by some method you can ask God, “Please come. I will see You.” No, God is not your order carrier.
###Human: You must please God for Him to reveal Himself. Is that correct?
###Assistant: Yes.
###Human: How do you know when you are pleasing God?
###Assistant: When we see Him. Then you will understand. Just as, when you eat, you do not require to ask anyone whether you are feeling strength or your hunger is satisfied. If you eat, you understand that you are feeling energy. You don’t need to inquire from anyone. Similarly. if you actually serve God, then you will understand, “God is dictating to me. God is there. I am seeing God.”
###Human: Or God’s representative.
###Assistant: Yes.
###Human: It comes easier.
###Assistant: You have to go through God’s representative.
###Human: How to please God’s representative?
###Assistant: You have to carry out his orders, that’s all. God’s representative is the guru. He asks you to do this, to do that—if you do that, that is pleasing.
###Human: yasya prasādād bhagavat-prasādaḥ ** “By the mercy of the spiritual master one is benedicted by the mercy of Kṛṣṇa.” If you please God’s representative, then automatically God becomes pleased, and thus you can directly see Him.
###Assistant: An Indian gentleman: How to please God’s representative?
###Assistant: You have to carry out his orders, that’s all. God’s representative is the guru. He asks you to do this, to do that—if you do that, that is pleasing.
###Human: The guru should be accepted as a representative of God?
###Assistant: Yes, the guru is God’s representative. The guru is the external manifestation of Kṛṣṇa.
###Human: But different from the incarnations of Kṛṣṇa that come?
###Assistant: Yes.
###Human: In what way is the external manifestation of the guru different from the external manifestation of, let us say, Kṛṣṇa or Caitanya when They come to earth?
###Assistant: The guru is the representative of Kṛṣṇa. So there are symptoms of who is a guru. The general symptoms are described in the Vedas.
###Human: tad-vij�ānārthaṁ sa gurum evābhigacchet samit-pāṇiḥ śrotriyaṁ brahma-niṣṭham [MU 1.2.12] A guru must come in a disciplic succession, and he must have heard thoroughly about the Vedas from his spiritual master. Generally a guru’s symptom is that he is a perfect devotee, that’s all. And he serves Kṛṣṇa by preaching His message.
###Assistant: Bob: Lord Caitanya—He was a different type of guru than you are?
###Assistant: No, no. Gurus cannot be of different types. All gurus are of one type.
###Human: But He was—was He also an incarnation at the same time?
###Assistant: Yes, He is Kṛṣṇa Himself, but He is representing the guru.
###Human: I... I see.
###Assistant: Yes.
###Human: And then...
###Assistant: Because Kṛṣṇa was God, He demanded:
###Human: sarva-dharmān parityajya mām ekaṁ śaraṇaṁ vraja [Bg. 18.66] “Abandon all varieties of religion and just surrender unto Me.” But people misunderstood Him. Therefore Kṛṣṇa again came as a guru and taught people how to surrender to Kṛṣṇa.
###Assistant: Śyāmasundara: Doesn’t He say in Bhagavad-gītā, “I am the spiritual master”?
###Assistant: Yes, He is the original spiritual master because He was accepted as spiritual master by Arjuna. So what is the difficulty? Śiṣyas te ’haṁ śādhi māṁ tvāṁ prapannam. Arjuna told the Lord, “I am Your disciple, and a soul surrendered unto You. Please instruct me.” So unless He is a spiritual master how does Arjuna become His disciple? He is the original guru. Tene brahma hṛdā ya ādi-kavaye: “It is He only who first imparted Vedic knowledge unto the heart of Brahmā, the first created being.” Therefore He is the original guru.
###Human: Kṛṣṇa.
###Assistant: Yes. He is the original guru. Then His disciple Brahmā
is a guru, then his disciple Nārada is a guru, then his disciple Vyāsa is a guru—in this way there is a guru-paramparā [disciplic succession of gurus]. Evaṁ paramparā-prāptam: the transcendental knowledge is received through the disciplic succession.
###Human: So a guru receives his knowledge through the disciplic succession, not directly from Kṛṣṇa? Do you receive some knowledge directly from Kṛṣṇa?
###Assistant: Yes. Kṛṣṇa’s direct instruction is there: Bhagavad-gītā.
###Human: I see, but...
###Assistant: But you have to learn it through the disciplic succession, otherwise you will misunderstand it.
###Human: But presently you do not receive information directly from Kṛṣṇa? It comes through the disciplic succession from the books?
###Assistant: There is no difference. Suppose I say that this is a pencil. If you say to him, “There is a pencil,” and if he says to another man, “This is a pencil,” then what is the difference between his instruction and my instructions?
###Human: Kṛṣṇa’s mercy allows you to know this now?
###Assistant: You can take Kṛṣṇa’s mercy also, provided it is delivered as it is. Just as we are teaching Bhagavad-gītā In Bhagavad-gītā Kṛṣṇa says: sarva-dharmān parityajya mām ekaṁ śaraṇaṁ vraja [Bg. 18.66] “Just give up all other forms of religion and simply surrender unto Me.” Now we are saying that you should give up everything and surrender to Kṛṣṇa. Therefore, there is no difference between Kṛṣṇa’s instruction and our instruction. There is no deviation. So if you receive knowledge in that perfect way, that is as good as receiving instruction directly from Kṛṣṇa. But we don’t change anything.
###Human: When I pray reverently, faithfully, does Kṛṣṇa hear me?
###Assistant: Yes.
###Human: From me to Him?
###Assistant: Yes, because He is within your heat He is always hearing you—whether you are praying or not praying. When you are doing some nonsense, He is also hearing you. And when you pray, that is very good—welcome.
###Human: To Kṛṣṇa’s ear, is praying louder than nonsense?
###Assistant: No. He is all-perfect. He can hear everything. Even if you don’t speak, even if you simply think, “I shall do it,” then He hears you. Sarvasya cāhaṁ hṛdi sanniviṣṭaḥ: Kṛṣṇa is seated in everyone’s heart.
###Human: But one should pray—is that so?
###Assistant: That is his business—praying.
###Human: Whose business?
###Assistant: Every living entity’s. That is the only business. Eko bahūnāṁ yo vidadhāti kāmān. That is the statement of the Vedas.
###Human: What does that mean?
###Assistant: He supplies everything to everyone. He is supplying food to everyone. So He is the Father. So why should you not pray, “Father, give me this”? Just as in the Christian Bible there is, “Father, give us our daily bread.” That is good—they are accepting the Supreme Father. But grown-up children should not ask from the father; rather, they should be prepared to serve the father. That is bhakti [devotion].
###Human: My questions you solve so nicely. [Everyone laughs with affection.]
###Assistant: Thank you very much.
###Human: So, should I ask you another question now?
###Assistant: Oh, yes. Yes!
|
binod9/pqpa
|
[
"region:us"
] |
2023-09-16T05:31:47+00:00
|
{}
|
2023-09-16T05:32:13+00:00
|
[] |
[] |
TAGS
#region-us
|
###Human: Śrīla Prabhupāda: This movement is especially meant to enable a human being to reach the real goal of life.
###Assistant: The real goal... ?
###Human: Is the real goal of life to know God?
###Assistant: Yes. To go back home, back to Godhead. That is the real goal of life. The water that comes from the sea forms clouds, the clouds fall down as rain, and the actual goal is to flow down the river and again enter the sea. So, we have come from God, and now we are embarrassed by material life. Therefore, our aim should be to get out of this embarrassing situation and go back home, back to Godhead. This is the real goal of life.
###Human: mām upetya punar janma duḥkhālayam aśāśvatam nāpnuvanti mahātmānaḥ saṁsiddhiṁ paramāṁ gatāḥ ["After attaining Me, the great souls, who are yogīs in devotion, never return to this temporary world, which is full of miseries, because they have attained the highest perfection."]
###Assistant: That is the version of Bhagavad-gītā. If anyone comes to Me—mām upetya: he does not come back again. Where? To this place—duḥkhālayam aśāśvatam [Bg. 8.15]. This place is the abode of miseries. Everyone knows, but they have been befooled by so-called leaders. Material life is miserable life. Kṛṣṇa says, God says, that this place is duḥkhālayam—it is a place of miseries. And it is also aśāśvatam, temporary. You cannot make a compromise: “All right, let it be miserable. I shall remain here as an American or Indian.” No. That also you cannot do. You cannot remain an American. You may think that, having been born in America, you are very happy. But you cannot remain an American for long. You will have to be kicked out of that place. And your next life you do not know! Therefore, it is duḥkhālayam aśāśvatam [Bg. 8.15]—miserable and temporary. That is our philosophy.
###Human: But when you have some knowledge of God, then life is not so miserable?
###Assistant: No! Some knowledge will not do. You must have perfect knowledge.
###Human: janma karma ca me divyam evaṁ yo vetti tattvataḥ [Bg. 4.9] Tattvataḥ means “perfectly.” Perfect knowledge is being taught in Bhagavad-gītā. So, we are giving everyone in human society a chance to learn Bhagavad-gītā as it is and make his life perfect. That is the Kṛṣṇa consciousness movement. What does your science say about the transmigration of the soul?
###Assistant: Bob: I think... that science... cannot deny or affirm it. Science does not know it.
###Human: Therefore I say that science is imperfect.
###Assistant: Bob: Science may. though, say something. It is said in science that energy is never destroyed; it is changed.
###Human: That’s all right. But how the energy is working in the future—that science does not know. How is the energy diverted? How, by different manipulations, is the energy working differently? For instance, electrical energy. By different handling it is operating the heats and it is operating the refrigerator. They are just the opposite, but the electrical energy is the same. Similarly, this energy—living energy—how is it being directed? Which way is it going? How is it fructifying in the next life? That they do not know. But in Bhagavad-gītā it is very simply stated.
###Assistant: vāsāṁsi jīrṇāni yathā vihāya [Bg. 2.22] You are covered by a dress, by a shirt. When this shirt is unusable, you change it. Similarly, this body is just like a shirt and coat. When it is no longer workable, we have to change it.
###Human: What is the “we” that has to change? What is constant?
###Assistant: That is the soul.
###Human: From one life to the next?
###Assistant: That is the soul—I. What “you” is speaking? You! What “I” is speaking? Identity: ātmā, or soul.
###Human: My soul is different from your soul?
###Assistant: Yes. You are an individual soul, I am an individual soul.
###Human: You have removed yourself from karmic influences. If I was to remove myself from karmic influences, would our souls be the same or different?
###Assistant: The soul is of the same quality in all. You are under a certain conception of life at the present moment, and these countrymen of yours [the Kṛṣṇa conscious devotees] were under a certain conception of life, but by training they have taken to another conception of life. So the ultimate training is how to become Kṛṣṇa conscious. That is the perfection.
###Human: If two people are Kṛṣṇa conscious, is their soul the same?
###Assistant: The soul is always the same.
###Human: In each person? In each person is it the same?
###Assistant: Yes.
###Human: [pointing to two devotees] If these two are Kṛṣṇa conscious, are their souls the same?
###Assistant: The soul is the same but always individual, even if one is not Kṛṣṇa conscious. For instance, you are a human being, and I am a human being. Even if I am not a Christian, even if you are not a Hindu, still we are human beings. Similarly, the soul may not be Kṛṣṇa conscious, or he may be Kṛṣṇa conscious—it doesn’t matter. But the soul is the soul.
###Human: Can you tell me more about this?
###Assistant: Soul—as pure spirit, all souls are equal. Even in an animal. Therefore it is said, paṇḍitāḥ sama-darśinaḥ: [Bg. 5.18] those who are actually learned do not see the outward covering, either in a human being or in an animal.
###Human: If I may ask another question on this?
###Assistant: Yes.
###Human: I have considered the soul somewhat as part of God. At times I think I feel God. I’m here, and you may say God is here. So if the soul is inside me, then should I be able
to feel God inside me? Not all of God, I mean, but a...
###Assistant: Yes. God is inside also. God is everywhere. God is inside and outside also. This is to be known.
###Human: How do you feel God inside you?
###Assistant: Not in the beginning, but you have to know from the śāstras [scriptures], by the Vedic information. For example, in the Bhagavad-gītā it is said, īśvaraḥ sarva-bhūtānāṁ hṛd-deśe ’rjuna tiṣṭhati: [Bg. 18.61] God is there in everyone’s heart. Paramāṇu-cayāntara-stham: God is also within every atom. So this is the first information. And then, by the yogic process, you have to realize it.
###Human: Yogic process?
###Assistant: Yes.
###Human: Is chanting Hare Kṛṣṇa such a yogic process?
###Assistant: Yes, it is also a yogic process.
###Human: What kind of yogic process must I do to find out—to feel this information—to feel the soul inside?
###Assistant: Yes, there are many different yogic Processes, but for this age this process is very nice.
###Human: Chanting.
###Assistant: Yes.
###Human: Through this I can feel not only God outside but God inside?
###Assistant: You’ll understand everything of God—how God is inside, how God is outside, how God is working. Everything will be revealed. By this attitude of service, God will reveal Himself. You cannot understand God by your endeavor. Only if God reveals Himself. For instance, when the sun is out of your sight at night, you cannot see it by your torchlight, or any light. But in the morning you can see the sun automatically. without any torchlight. Similarly, you have to create a situation—you have to put yourself in a situation—in which God will be revealed. It is not that by some method you can ask God, “Please come. I will see You.” No, God is not your order carrier.
###Human: You must please God for Him to reveal Himself. Is that correct?
###Assistant: Yes.
###Human: How do you know when you are pleasing God?
###Assistant: When we see Him. Then you will understand. Just as, when you eat, you do not require to ask anyone whether you are feeling strength or your hunger is satisfied. If you eat, you understand that you are feeling energy. You don’t need to inquire from anyone. Similarly. if you actually serve God, then you will understand, “God is dictating to me. God is there. I am seeing God.”
###Human: Or God’s representative.
###Assistant: Yes.
###Human: It comes easier.
###Assistant: You have to go through God’s representative.
###Human: How to please God’s representative?
###Assistant: You have to carry out his orders, that’s all. God’s representative is the guru. He asks you to do this, to do that—if you do that, that is pleasing.
###Human: yasya prasādād bhagavat-prasādaḥ “By the mercy of the spiritual master one is benedicted by the mercy of Kṛṣṇa.” If you please God’s representative, then automatically God becomes pleased, and thus you can directly see Him.
###Assistant: An Indian gentleman: How to please God’s representative?
###Assistant: You have to carry out his orders, that’s all. God’s representative is the guru. He asks you to do this, to do that—if you do that, that is pleasing.
###Human: The guru should be accepted as a representative of God?
###Assistant: Yes, the guru is God’s representative. The guru is the external manifestation of Kṛṣṇa.
###Human: But different from the incarnations of Kṛṣṇa that come?
###Assistant: Yes.
###Human: In what way is the external manifestation of the guru different from the external manifestation of, let us say, Kṛṣṇa or Caitanya when They come to earth?
###Assistant: The guru is the representative of Kṛṣṇa. So there are symptoms of who is a guru. The general symptoms are described in the Vedas.
###Human: tad-vij�ānārthaṁ sa gurum evābhigacchet samit-pāṇiḥ śrotriyaṁ brahma-niṣṭham [MU 1.2.12] A guru must come in a disciplic succession, and he must have heard thoroughly about the Vedas from his spiritual master. Generally a guru’s symptom is that he is a perfect devotee, that’s all. And he serves Kṛṣṇa by preaching His message.
###Assistant: Bob: Lord Caitanya—He was a different type of guru than you are?
###Assistant: No, no. Gurus cannot be of different types. All gurus are of one type.
###Human: But He was—was He also an incarnation at the same time?
###Assistant: Yes, He is Kṛṣṇa Himself, but He is representing the guru.
###Human: I... I see.
###Assistant: Yes.
###Human: And then...
###Assistant: Because Kṛṣṇa was God, He demanded:
###Human: sarva-dharmān parityajya mām ekaṁ śaraṇaṁ vraja [Bg. 18.66] “Abandon all varieties of religion and just surrender unto Me.” But people misunderstood Him. Therefore Kṛṣṇa again came as a guru and taught people how to surrender to Kṛṣṇa.
###Assistant: Śyāmasundara: Doesn’t He say in Bhagavad-gītā, “I am the spiritual master”?
###Assistant: Yes, He is the original spiritual master because He was accepted as spiritual master by Arjuna. So what is the difficulty? Śiṣyas te ’haṁ śādhi māṁ tvāṁ prapannam. Arjuna told the Lord, “I am Your disciple, and a soul surrendered unto You. Please instruct me.” So unless He is a spiritual master how does Arjuna become His disciple? He is the original guru. Tene brahma hṛdā ya ādi-kavaye: “It is He only who first imparted Vedic knowledge unto the heart of Brahmā, the first created being.” Therefore He is the original guru.
###Human: Kṛṣṇa.
###Assistant: Yes. He is the original guru. Then His disciple Brahmā
is a guru, then his disciple Nārada is a guru, then his disciple Vyāsa is a guru—in this way there is a guru-paramparā [disciplic succession of gurus]. Evaṁ paramparā-prāptam: the transcendental knowledge is received through the disciplic succession.
###Human: So a guru receives his knowledge through the disciplic succession, not directly from Kṛṣṇa? Do you receive some knowledge directly from Kṛṣṇa?
###Assistant: Yes. Kṛṣṇa’s direct instruction is there: Bhagavad-gītā.
###Human: I see, but...
###Assistant: But you have to learn it through the disciplic succession, otherwise you will misunderstand it.
###Human: But presently you do not receive information directly from Kṛṣṇa? It comes through the disciplic succession from the books?
###Assistant: There is no difference. Suppose I say that this is a pencil. If you say to him, “There is a pencil,” and if he says to another man, “This is a pencil,” then what is the difference between his instruction and my instructions?
###Human: Kṛṣṇa’s mercy allows you to know this now?
###Assistant: You can take Kṛṣṇa’s mercy also, provided it is delivered as it is. Just as we are teaching Bhagavad-gītā In Bhagavad-gītā Kṛṣṇa says: sarva-dharmān parityajya mām ekaṁ śaraṇaṁ vraja [Bg. 18.66] “Just give up all other forms of religion and simply surrender unto Me.” Now we are saying that you should give up everything and surrender to Kṛṣṇa. Therefore, there is no difference between Kṛṣṇa’s instruction and our instruction. There is no deviation. So if you receive knowledge in that perfect way, that is as good as receiving instruction directly from Kṛṣṇa. But we don’t change anything.
###Human: When I pray reverently, faithfully, does Kṛṣṇa hear me?
###Assistant: Yes.
###Human: From me to Him?
###Assistant: Yes, because He is within your heat He is always hearing you—whether you are praying or not praying. When you are doing some nonsense, He is also hearing you. And when you pray, that is very good—welcome.
###Human: To Kṛṣṇa’s ear, is praying louder than nonsense?
###Assistant: No. He is all-perfect. He can hear everything. Even if you don’t speak, even if you simply think, “I shall do it,” then He hears you. Sarvasya cāhaṁ hṛdi sanniviṣṭaḥ: Kṛṣṇa is seated in everyone’s heart.
###Human: But one should pray—is that so?
###Assistant: That is his business—praying.
###Human: Whose business?
###Assistant: Every living entity’s. That is the only business. Eko bahūnāṁ yo vidadhāti kāmān. That is the statement of the Vedas.
###Human: What does that mean?
###Assistant: He supplies everything to everyone. He is supplying food to everyone. So He is the Father. So why should you not pray, “Father, give me this”? Just as in the Christian Bible there is, “Father, give us our daily bread.” That is good—they are accepting the Supreme Father. But grown-up children should not ask from the father; rather, they should be prepared to serve the father. That is bhakti [devotion].
###Human: My questions you solve so nicely. [Everyone laughs with affection.]
###Assistant: Thank you very much.
###Human: So, should I ask you another question now?
###Assistant: Oh, yes. Yes!
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
b616d29bf42e76fb16ead2c846ccbf6a24fb37b1
|
# Dataset of Shutaura Sequenzia
This is the dataset of Shutaura Sequenzia, containing 72 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 72 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 143 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 72 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 72 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 72 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 72 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 72 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 143 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 143 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 143 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/shutaura_sequenzia_toarumajutsunoindex
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T05:35:58+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:42:16+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Shutaura Sequenzia
=============================
This is the dataset of Shutaura Sequenzia, containing 72 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
d6f3e35a07e452314512a06cd0dcc5eba202aca8
|
# Dataset Card for "ads_corporate_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/ads_corporate_prompts
|
[
"region:us"
] |
2023-09-16T05:46:10+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2970085, "num_examples": 10000}], "download_size": 312601, "dataset_size": 2970085}}
|
2023-09-16T05:46:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ads_corporate_prompts"
More Information needed
|
[
"# Dataset Card for \"ads_corporate_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ads_corporate_prompts\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ads_corporate_prompts\"\n\nMore Information needed"
] |
4c7d6fa71e4c9145a411cbb982323c57a94f9fc8
|
# Dataset Card for "ads-fashion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/ads-fashion
|
[
"region:us"
] |
2023-09-16T05:51:46+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2666953, "num_examples": 10000}], "download_size": 272530, "dataset_size": 2666953}}
|
2023-09-16T05:51:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ads-fashion"
More Information needed
|
[
"# Dataset Card for \"ads-fashion\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ads-fashion\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ads-fashion\"\n\nMore Information needed"
] |
869f847e1de1b6f57c34cc120ce13a2e3492c6f1
|
# Dataset of kitazawa_shiho/北沢志保/키타자와시호 (THE iDOLM@STER: Million Live!)
This is the dataset of kitazawa_shiho/北沢志保/키타자와시호 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are `long_hair, brown_hair, brown_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 | 579.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 348.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1170 | 727.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 520.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1170 | 1014.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kitazawa_shiho_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kitazawa_shiho_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, cat_ears, cat_tail, jingle_bell, looking_at_viewer, solo, long_sleeves, paw_gloves, black_dress, blush, cleavage, fur_trim, neck_bell, purple_bow, cat_paws, simple_background, white_background, open_mouth, shiny_hair, blue_bowtie, closed_mouth, frilled_dress, paw_shoes, ribbon, yellow_eyes |
| 1 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, closed_mouth, collarbone, short_sleeves, simple_background, solo, striped_shirt, upper_body, blush, white_background |
| 2 | 9 |  |  |  |  |  | 1girl, smile, solo, blush, looking_at_viewer |
| 3 | 5 |  |  |  |  |  | 1girl, cleavage, collarbone, looking_at_viewer, medium_breasts, solo, blush, floral_print, lingerie, closed_mouth, shiny_hair, smile, yellow_eyes, babydoll, bare_shoulders, detached_sleeves, on_side, panties, underwear_only |
| 4 | 20 |  |  |  |  |  | 1girl, blush, solo, navel, looking_at_viewer, nipples, medium_breasts, female_pubic_hair, collarbone, closed_mouth, completely_nude, simple_background, stomach, upper_body |
| 5 | 8 |  |  |  |  |  | blush, day, looking_at_viewer, outdoors, 1girl, medium_breasts, ocean, solo, cleavage, collarbone, closed_mouth, cloud, navel, side-tie_bikini_bottom, blue_sky, cowboy_shot, lens_flare, standing, water, wet, yellow_eyes, bare_shoulders, beach, black_bikini, frills, front-tie_bikini_top, halterneck, hand_up, smile, wading, white_bikini |
| 6 | 6 |  |  |  |  |  | 1girl, black_serafuku, looking_at_viewer, solo, black_shirt, black_skirt, red_neckerchief, black_gloves, black_sailor_collar, pleated_skirt, short_sleeves, closed_mouth, fingerless_gloves, standing |
| 7 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, dress, holding_microphone, blurry, bow, open_mouth, frilled_sleeves, hair_ribbon, juliet_sleeves, upper_body, wrist_cuffs, yellow_eyes |
| 8 | 30 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, blush, nipples, penis, sex, sweat, vaginal, medium_breasts, open_mouth, navel, looking_at_viewer, pussy, completely_nude, female_pubic_hair, mosaic_censoring, straddling |
| 9 | 9 |  |  |  |  |  | 1girl, solo, closed_mouth, sleeveless, black_shorts, hat, looking_at_viewer, shiny_hair, short_shorts, blue_headwear, detached_sleeves, floating_hair, standing, dress, frills, striped, very_long_hair, black_sleeves, bow, cowboy_shot, long_sleeves, smile, thighhighs, yellow_eyes |
| 10 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, maid_apron, maid_headdress, solo, frills, wa_maid, long_sleeves, wide_sleeves, medical_eyepatch, outdoors, black_kimono, holding_weapon, knife, night, parted_lips, sky, upper_body, white_apron |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cat_ears | cat_tail | jingle_bell | looking_at_viewer | solo | long_sleeves | paw_gloves | black_dress | blush | cleavage | fur_trim | neck_bell | purple_bow | cat_paws | simple_background | white_background | open_mouth | shiny_hair | blue_bowtie | closed_mouth | frilled_dress | paw_shoes | ribbon | yellow_eyes | collarbone | short_sleeves | striped_shirt | upper_body | smile | medium_breasts | floral_print | lingerie | babydoll | bare_shoulders | detached_sleeves | on_side | panties | underwear_only | navel | nipples | female_pubic_hair | completely_nude | stomach | day | outdoors | ocean | cloud | side-tie_bikini_bottom | blue_sky | cowboy_shot | lens_flare | standing | water | wet | beach | black_bikini | frills | front-tie_bikini_top | halterneck | hand_up | wading | white_bikini | black_serafuku | black_shirt | black_skirt | red_neckerchief | black_gloves | black_sailor_collar | pleated_skirt | fingerless_gloves | dress | holding_microphone | blurry | bow | frilled_sleeves | hair_ribbon | juliet_sleeves | wrist_cuffs | 1boy | hetero | solo_focus | penis | sex | sweat | vaginal | pussy | mosaic_censoring | straddling | sleeveless | black_shorts | hat | short_shorts | blue_headwear | floating_hair | striped | very_long_hair | black_sleeves | thighhighs | maid_apron | maid_headdress | wa_maid | wide_sleeves | medical_eyepatch | black_kimono | holding_weapon | knife | night | parted_lips | sky | white_apron |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------|:-----------|:--------------|:--------------------|:-------|:---------------|:-------------|:--------------|:--------|:-----------|:-----------|:------------|:-------------|:-----------|:--------------------|:-------------------|:-------------|:-------------|:--------------|:---------------|:----------------|:------------|:---------|:--------------|:-------------|:----------------|:----------------|:-------------|:--------|:-----------------|:---------------|:-----------|:-----------|:-----------------|:-------------------|:----------|:----------|:-----------------|:--------|:----------|:--------------------|:------------------|:----------|:------|:-----------|:--------|:--------|:-------------------------|:-----------|:--------------|:-------------|:-----------|:--------|:------|:--------|:---------------|:---------|:-----------------------|:-------------|:----------|:---------|:---------------|:-----------------|:--------------|:--------------|:------------------|:---------------|:----------------------|:----------------|:--------------------|:--------|:---------------------|:---------|:------|:------------------|:--------------|:-----------------|:--------------|:-------|:---------|:-------------|:--------|:------|:--------|:----------|:--------|:-------------------|:-------------|:-------------|:---------------|:------|:---------------|:----------------|:----------------|:----------|:-----------------|:----------------|:-------------|:-------------|:-----------------|:----------|:---------------|:-------------------|:---------------|:-----------------|:--------|:--------|:--------------|:------|:--------------|
| 0 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | | | | X | X | | | | X | | | | | | X | X | | | | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | 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 | 20 |  |  |  |  |  | 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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | | X | X | | | | X | | | | | | | | X | | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 30 |  |  |  |  |  | X | | | | X | | | | | X | | | | | | | | X | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 | X | X | X | | | | | | | | | | | | |
| 10 | 8 |  |  |  |  |  | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/kitazawa_shiho_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T06:02:05+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T20:58:02+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kitazawa\_shiho/北沢志保/키타자와시호 (THE iDOLM@STER: Million Live!)
======================================================================
This is the dataset of kitazawa\_shiho/北沢志保/키타자와시호 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are 'long\_hair, brown\_hair, brown\_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"
] |
d6c9023f017b564f7b3ccccf5348166bce8fdbcd
|
# Evaluation Dataset for MME
|
Otter-AI/MME
|
[
"size_categories:1K<n<10K",
"region:us"
] |
2023-09-16T06:11:55+00:00
|
{"size_categories": ["1K<n<10K"], "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question_id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "category", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 1733070098.024, "num_examples": 2374}], "download_size": 864018279, "dataset_size": 1733070098.024}}
|
2023-12-23T09:13:53+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #region-us
|
# Evaluation Dataset for MME
|
[
"# Evaluation Dataset for MME"
] |
[
"TAGS\n#size_categories-1K<n<10K #region-us \n",
"# Evaluation Dataset for MME"
] |
[
18,
8
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #region-us \n# Evaluation Dataset for MME"
] |
5bf816df4bfbe27ae280cb537d26a0097dd61c20
|
# Dataset of Vento of the Front
This is the dataset of Vento of the Front, containing 89 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 89 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 198 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 89 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 89 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 89 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 89 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 89 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 198 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 198 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 198 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/vento_of_the_front_toarumajutsunoindex
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T06:30:18+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:42:20+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Vento of the Front
=============================
This is the dataset of Vento of the Front, containing 89 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
4f4d63a42537a971e887eae5392976e304caf89e
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
hematank/cat
|
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"license:bsd",
"region:us"
] |
2023-09-16T06:33:09+00:00
|
{"license": "bsd", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"]}
|
2023-09-16T06:54:26+00:00
|
[] |
[] |
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #license-bsd #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #license-bsd #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
35,
8,
24,
32,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #license-bsd #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
3328f0ba575e30549c78b6cd035f0f4a919c8fc6
|
# Dataset Card for "food_photography"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/food_photography
|
[
"region:us"
] |
2023-09-16T06:34:00+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1988005, "num_examples": 10000}], "download_size": 64949, "dataset_size": 1988005}}
|
2023-09-16T06:34:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "food_photography"
More Information needed
|
[
"# Dataset Card for \"food_photography\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"food_photography\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"food_photography\"\n\nMore Information needed"
] |
2f6d7ac35149160b44f4f78eff012f19893a7d1b
|
# Dataset Card for "luxury_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/luxury_prompts
|
[
"region:us"
] |
2023-09-16T06:36:11+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1944817, "num_examples": 10000}], "download_size": 64348, "dataset_size": 1944817}}
|
2023-09-16T06:36:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "luxury_prompts"
More Information needed
|
[
"# Dataset Card for \"luxury_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"luxury_prompts\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"luxury_prompts\"\n\nMore Information needed"
] |
9c6290b958a2591fa811d2cd31909b8937048009
|
# Dataset Card for "ads-real_estate"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/ads-real_estate
|
[
"region:us"
] |
2023-09-16T06:37:52+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2183922, "num_examples": 10000}], "download_size": 142348, "dataset_size": 2183922}}
|
2023-09-16T06:37:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ads-real_estate"
More Information needed
|
[
"# Dataset Card for \"ads-real_estate\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ads-real_estate\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ads-real_estate\"\n\nMore Information needed"
] |
6d818cf13fc0f494155a0db5fc14b31b46d1dba6
|
# Dataset of toyokawa_fuuka/豊川風花/토요카와후카 (THE iDOLM@STER: Million Live!)
This is the dataset of toyokawa_fuuka/豊川風花/토요카와후카 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are `blue_hair, short_hair, breasts, brown_eyes, antenna_hair, large_breasts, bangs, wavy_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 559.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/toyokawa_fuuka_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 343.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/toyokawa_fuuka_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1173 | 728.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/toyokawa_fuuka_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 504.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/toyokawa_fuuka_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1173 | 999.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/toyokawa_fuuka_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/toyokawa_fuuka_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, bikini, cleavage, navel, open_mouth, simple_background, white_background, smile |
| 1 | 8 |  |  |  |  |  | 1girl, day, looking_at_viewer, navel, ocean, outdoors, smile, solo, blue_sky, cloud, cleavage, cowboy_shot, open_mouth, beach, collarbone, blue_bikini, blush, covered_nipples, halterneck |
| 2 | 5 |  |  |  |  |  | 1girl, :d, looking_at_viewer, open_mouth, solo, dress, polka_dot, character_name, character_signature, hat |
| 3 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, nipples, solo, blush, navel, open_mouth, smile, female_pubic_hair, collarbone, completely_nude |
| 4 | 7 |  |  |  |  |  | 1girl, female_pubic_hair, nipples, navel, pussy, spread_legs, sweat, looking_at_viewer, 1boy, anus, blush, hetero, mosaic_censoring, on_bed, completely_nude, on_back, open_mouth, pillow, sex, solo_focus, vaginal |
| 5 | 9 |  |  |  |  |  | 1boy, 1girl, blush, hetero, penis, solo_focus, nipples, paizuri, sweat, breasts_squeezed_together, mosaic_censoring, open_mouth, collarbone, completely_nude, cum_in_mouth, fellatio |
| 6 | 8 |  |  |  |  |  | smile, 1girl, blush, hair_flower, pearl_necklace, solo, earrings, looking_at_viewer, medium_breasts, purple_dress, bare_shoulders, collarbone, strapless_dress, black_gloves, character_name, hair_between_eyes, open_mouth, pink_flower, rose, sparkle, upper_body |
| 7 | 9 |  |  |  |  |  | 1girl, long_sleeves, solo, white_shirt, blush, pleated_skirt, ponytail, serafuku, white_background, looking_at_viewer, simple_background, yellow_neckerchief, blue_skirt, white_sailor_collar, collarbone, smile |
| 8 | 10 |  |  |  |  |  | 1girl, playboy_bunny, rabbit_ears, cleavage, detached_collar, fake_animal_ears, solo, bare_shoulders, looking_at_viewer, strapless_leotard, wrist_cuffs, blush, rabbit_tail, black_pantyhose, bowtie, cowboy_shot, black_leotard, fake_tail, fishnet_pantyhose, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | looking_at_viewer | bikini | cleavage | navel | open_mouth | simple_background | white_background | smile | day | ocean | outdoors | blue_sky | cloud | cowboy_shot | beach | collarbone | blue_bikini | covered_nipples | halterneck | :d | dress | polka_dot | character_name | character_signature | hat | nipples | female_pubic_hair | completely_nude | pussy | spread_legs | sweat | 1boy | anus | hetero | mosaic_censoring | on_bed | on_back | pillow | sex | solo_focus | vaginal | penis | paizuri | breasts_squeezed_together | cum_in_mouth | fellatio | hair_flower | pearl_necklace | earrings | medium_breasts | purple_dress | bare_shoulders | strapless_dress | black_gloves | hair_between_eyes | pink_flower | rose | sparkle | upper_body | long_sleeves | white_shirt | pleated_skirt | ponytail | serafuku | yellow_neckerchief | blue_skirt | white_sailor_collar | playboy_bunny | rabbit_ears | detached_collar | fake_animal_ears | strapless_leotard | wrist_cuffs | rabbit_tail | black_pantyhose | bowtie | black_leotard | fake_tail | fishnet_pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:---------|:-----------|:--------|:-------------|:--------------------|:-------------------|:--------|:------|:--------|:-----------|:-----------|:--------|:--------------|:--------|:-------------|:--------------|:------------------|:-------------|:-----|:--------|:------------|:-----------------|:----------------------|:------|:----------|:--------------------|:------------------|:--------|:--------------|:--------|:-------|:-------|:---------|:-------------------|:---------|:----------|:---------|:------|:-------------|:----------|:--------|:----------|:----------------------------|:---------------|:-----------|:--------------|:-----------------|:-----------|:-----------------|:---------------|:-----------------|:------------------|:---------------|:--------------------|:--------------|:-------|:----------|:-------------|:---------------|:--------------|:----------------|:-----------|:-----------|:---------------------|:-------------|:----------------------|:----------------|:--------------|:------------------|:-------------------|:--------------------|:--------------|:--------------|:------------------|:---------|:----------------|:------------|:--------------------|
| 0 | 22 |  |  |  |  |  | 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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | X | | | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | | X | | | | | X | | | | | | | | | | | X | | | | | | | | | | X | | X | | | X | X | | X | X | | | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | X | X | X | X | | | | X | | | X | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | X | X | X | X | | | | | X | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 8 | 10 |  |  |  |  |  | X | X | X | X | | X | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/toyokawa_fuuka_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T06:39:28+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T03:30:00+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of toyokawa\_fuuka/豊川風花/토요카와후카 (THE iDOLM@STER: Million Live!)
======================================================================
This is the dataset of toyokawa\_fuuka/豊川風花/토요카와후카 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are 'blue\_hair, short\_hair, breasts, brown\_eyes, antenna\_hair, large\_breasts, bangs, wavy\_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"
] |
a7ffd054a1e99c368cfaa85537e27643fb5a4726
|
# Dataset Card for "ads-retail"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/ads-retail
|
[
"region:us"
] |
2023-09-16T06:39:34+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1695016, "num_examples": 10000}], "download_size": 133142, "dataset_size": 1695016}}
|
2023-09-16T06:39:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ads-retail"
More Information needed
|
[
"# Dataset Card for \"ads-retail\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ads-retail\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ads-retail\"\n\nMore Information needed"
] |
3b9fd0282db12cb9df0e1e09b416fc8b09c743d2
|
# Dataset of Oriana Thomson
This is the dataset of Oriana Thomson, containing 98 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 98 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 234 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 98 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 98 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 98 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 98 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 98 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 234 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 234 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 234 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/oriana_thomson_toarumajutsunoindex
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T06:42:28+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:42:24+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Oriana Thomson
=========================
This is the dataset of Oriana Thomson, containing 98 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
3012b49b5f200ffb93d307fb736aa950d2137c0d
|
# Dataset of Laura Stuart
This is the dataset of Laura Stuart, containing 78 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 78 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 160 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 78 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 78 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 78 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 78 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 78 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 160 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 160 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 160 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/laura_stuart_toarumajutsunoindex
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T06:48:25+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:42:26+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Laura Stuart
=======================
This is the dataset of Laura Stuart, containing 78 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
d9974dc371dd4b25cd4e6fcb734a2531e55dcbff
|
# Dataset Card for "data_aug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
linhqyy/data_aug
|
[
"region:us"
] |
2023-09-16T06:50:02+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "sentence_annotation", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 330965, "num_examples": 1273}], "download_size": 95261, "dataset_size": 330965}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T06:50:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_aug"
More Information needed
|
[
"# Dataset Card for \"data_aug\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_aug\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_aug\"\n\nMore Information needed"
] |
cfee2dc4f6d24b9e259ded9c5a9ccd04e8ae26aa
|
# Dataset of Meigo Arisa
This is the dataset of Meigo Arisa, containing 73 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 73 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 176 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 73 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 73 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 73 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 73 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 73 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 176 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 176 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 176 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/meigo_arisa_toarumajutsunoindex
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T07:12:05+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:42:28+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Meigo Arisa
======================
This is the dataset of Meigo Arisa, containing 73 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
424c771099c404350b92554cc16e6a4a784fa7b1
|
# Dataset of Carissa
This is the dataset of Carissa, containing 129 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 129 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 284 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 129 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 129 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 129 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 129 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 129 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 284 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 284 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 284 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/carissa_toarumajutsunoindex
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T07:24:18+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:42:30+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Carissa
==================
This is the dataset of Carissa, containing 129 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
fe5e91a31979343f20222cc2497301591ea2d2d7
|
# Dataset of sakuramori_kaori/桜守歌織/사쿠라모리카오리 (THE iDOLM@STER: Million Live!)
This is the dataset of sakuramori_kaori/桜守歌織/사쿠라모리카오리 (THE iDOLM@STER: Million Live!), containing 416 images and their tags.
The core tags of this character are `green_eyes, ahoge, braid, short_hair, brown_hair, breasts, bangs, light_brown_hair, medium_breasts, 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 | 416 | 542.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakuramori_kaori_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 416 | 311.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakuramori_kaori_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 999 | 658.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakuramori_kaori_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 416 | 480.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakuramori_kaori_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 999 | 927.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakuramori_kaori_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sakuramori_kaori_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, frilled_sleeves, open_mouth, blush, pantyhose, sitting, skirt, treble_clef, underbust |
| 1 | 35 |  |  |  |  |  | 1girl, blush, solo, long_sleeves, smile, looking_at_viewer, red_ribbon, center_frills, frilled_sleeves, suspender_skirt, neck_ribbon, white_shirt, buttons, simple_background, high-waist_skirt, open_mouth, white_background, treble_clef, upper_body, necklace, swept_bangs, underbust, collared_shirt, hair_between_eyes, frilled_shirt_collar, red_bow |
| 2 | 19 |  |  |  |  |  | 1girl, smile, solo, white_gloves, looking_at_viewer, open_mouth, blush, earrings, necklace, treble_clef, musical_note, beret, short_sleeves, feathers |
| 3 | 17 |  |  |  |  |  | 1girl, blush, solo, looking_at_viewer, navel, open_mouth, smile, cleavage, hair_between_eyes, white_bikini, collarbone, simple_background, white_background, sailor_bikini |
| 4 | 7 |  |  |  |  |  | 1girl, dress, looking_at_viewer, puffy_sleeves, solo, white_gloves, smile, blush, choker, collarbone, fur_trim, hair_flower, upper_body, hair_between_eyes, long_sleeves, necklace, open_mouth |
| 5 | 6 |  |  |  |  |  | 1girl, blush, bra, cleavage, looking_at_viewer, panties, solo, underwear_only, white_background, collarbone, simple_background, bare_shoulders, lingerie, navel, thighs |
| 6 | 11 |  |  |  |  |  | 1boy, 1girl, hetero, sex, solo_focus, vaginal, blush, pussy, spread_legs, sweat, looking_at_viewer, nipples, penis, mosaic_censoring, pov, girl_on_top, smile, completely_nude, hair_between_eyes, navel, open_mouth, condom, cowgirl_position, female_pubic_hair, on_back |
| 7 | 9 |  |  |  |  |  | 1girl, white_shirt, office_lady, solo, black_bra, black_pantyhose, cleavage, looking_at_viewer, pencil_skirt, black_footwear, black_skirt, collarbone, collared_shirt, high_heels, squatting, blue_nails, blush, hair_between_eyes, indoors, long_sleeves, nail_polish |
| 8 | 8 |  |  |  |  |  | 1girl, bare_shoulders, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, blush, fake_tail, looking_at_viewer, rabbit_tail, wrist_cuffs, ass, black_leotard, black_pantyhose, looking_back, simple_background, strapless_leotard, black_footwear, from_behind, high_heels, condom_wrapper, tongue_out, grey_background, holding_condom, open_mouth, squatting, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | smile | solo | frilled_sleeves | open_mouth | blush | pantyhose | sitting | skirt | treble_clef | underbust | long_sleeves | red_ribbon | center_frills | suspender_skirt | neck_ribbon | white_shirt | buttons | simple_background | high-waist_skirt | white_background | upper_body | necklace | swept_bangs | collared_shirt | hair_between_eyes | frilled_shirt_collar | red_bow | white_gloves | earrings | musical_note | beret | short_sleeves | feathers | navel | cleavage | white_bikini | collarbone | sailor_bikini | dress | puffy_sleeves | choker | fur_trim | hair_flower | bra | panties | underwear_only | bare_shoulders | lingerie | thighs | 1boy | hetero | sex | solo_focus | vaginal | pussy | spread_legs | sweat | nipples | penis | mosaic_censoring | pov | girl_on_top | completely_nude | condom | cowgirl_position | female_pubic_hair | on_back | office_lady | black_bra | black_pantyhose | pencil_skirt | black_footwear | black_skirt | high_heels | squatting | blue_nails | indoors | nail_polish | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | fake_tail | rabbit_tail | wrist_cuffs | ass | black_leotard | looking_back | strapless_leotard | from_behind | condom_wrapper | tongue_out | grey_background | holding_condom |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:--------|:-------|:------------------|:-------------|:--------|:------------|:----------|:--------|:--------------|:------------|:---------------|:-------------|:----------------|:------------------|:--------------|:--------------|:----------|:--------------------|:-------------------|:-------------------|:-------------|:-----------|:--------------|:-----------------|:--------------------|:-----------------------|:----------|:---------------|:-----------|:---------------|:--------|:----------------|:-----------|:--------|:-----------|:---------------|:-------------|:----------------|:--------|:----------------|:---------|:-----------|:--------------|:------|:----------|:-----------------|:-----------------|:-----------|:---------|:-------|:---------|:------|:-------------|:----------|:--------|:--------------|:--------|:----------|:--------|:-------------------|:------|:--------------|:------------------|:---------|:-------------------|:--------------------|:----------|:--------------|:------------|:------------------|:---------------|:-----------------|:--------------|:-------------|:------------|:-------------|:----------|:--------------|:------------------|:-------------------|:----------------|:--------------|:------------|:--------------|:--------------|:------|:----------------|:---------------|:--------------------|:--------------|:-----------------|:-------------|:------------------|:-----------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 35 |  |  |  |  |  | X | X | 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 | 19 |  |  |  |  |  | X | X | X | X | | X | X | | | | X | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 17 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | | X | | | X | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | X | X | | X | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | 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 | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/sakuramori_kaori_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T07:32:31+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:49:53+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of sakuramori\_kaori/桜守歌織/사쿠라모리카오리 (THE iDOLM@STER: Million Live!)
==========================================================================
This is the dataset of sakuramori\_kaori/桜守歌織/사쿠라모리카오리 (THE iDOLM@STER: Million Live!), containing 416 images and their tags.
The core tags of this character are 'green\_eyes, ahoge, braid, short\_hair, brown\_hair, breasts, bangs, light\_brown\_hair, medium\_breasts, 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"
] |
fadde9eb0a83cd236291f5811849bb605e4fc4e1
|
# Dataset Card for "Madoka_memes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WDong/Madoka_memes
|
[
"region:us"
] |
2023-09-16T07:35:15+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 237621.0, "num_examples": 14}], "download_size": 235259, "dataset_size": 237621.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T07:35:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Madoka_memes"
More Information needed
|
[
"# Dataset Card for \"Madoka_memes\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Madoka_memes\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Madoka_memes\"\n\nMore Information needed"
] |
18d7cd64ea0543ee7dd0bad8accb24a2cf511162
|
# Dataset Card for "test_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
him009/test_dataset
|
[
"region:us"
] |
2023-09-16T07:36:13+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11384, "num_examples": 6}], "download_size": 21140, "dataset_size": 11384}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T07:36:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test_dataset"
More Information needed
|
[
"# Dataset Card for \"test_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test_dataset\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test_dataset\"\n\nMore Information needed"
] |
3e08072703ee772aae4310577ade9908a5a19b3f
|
# Dataset Card for "ads-automotive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/ads-automotive
|
[
"region:us"
] |
2023-09-16T08:31:34+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1761280, "num_examples": 10000}], "download_size": 125999, "dataset_size": 1761280}}
|
2023-09-16T08:31:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ads-automotive"
More Information needed
|
[
"# Dataset Card for \"ads-automotive\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ads-automotive\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ads-automotive\"\n\nMore Information needed"
] |
6398438d7b401f672f086643332f535b7f6797a3
|
# Dataset of hakozaki_serika/箱崎星梨花 (THE iDOLM@STER: Million Live!)
This is the dataset of hakozaki_serika/箱崎星梨花 (THE iDOLM@STER: Million Live!), containing 396 images and their tags.
The core tags of this character are `brown_hair, twintails, long_hair, brown_eyes, ahoge, bangs, ribbon, hair_ribbon, bow, 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 | 396 | 441.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 396 | 276.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 915 | 562.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 396 | 399.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 915 | 767.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hakozaki_serika_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hakozaki_serika_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, green_ribbon, looking_at_viewer, solo, :d, blush, open_mouth, sitting, white_background, green_dress, simple_background, short_sleeves |
| 1 | 15 |  |  |  |  |  | 1girl, solo, :d, open_mouth, looking_at_viewer, dress |
| 2 | 9 |  |  |  |  |  | 1girl, navel, solo, open_mouth, smile, white_bikini, blush, sailor_bikini, simple_background, white_background, looking_at_viewer, collarbone, small_breasts |
| 3 | 5 |  |  |  |  |  | 1girl, detached_collar, playboy_bunny, rabbit_ears, wrist_cuffs, solo, black_leotard, fake_animal_ears, looking_at_viewer, bare_shoulders, black_bowtie, blush, rabbit_tail, simple_background, small_breasts, strapless_leotard, thighband_pantyhose |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | green_ribbon | looking_at_viewer | solo | :d | blush | open_mouth | sitting | white_background | green_dress | simple_background | short_sleeves | dress | navel | smile | white_bikini | sailor_bikini | collarbone | small_breasts | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | black_leotard | fake_animal_ears | bare_shoulders | black_bowtie | rabbit_tail | strapless_leotard | thighband_pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:-----|:--------|:-------------|:----------|:-------------------|:--------------|:--------------------|:----------------|:--------|:--------|:--------|:---------------|:----------------|:-------------|:----------------|:------------------|:----------------|:--------------|:--------------|:----------------|:-------------------|:-----------------|:---------------|:--------------|:--------------------|:----------------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | | X | X | X | | X | | | | | | X | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | 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 |
|
CyberHarem/hakozaki_serika_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T08:33:07+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:14:46+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of hakozaki\_serika/箱崎星梨花 (THE iDOLM@STER: Million Live!)
=================================================================
This is the dataset of hakozaki\_serika/箱崎星梨花 (THE iDOLM@STER: Million Live!), containing 396 images and their tags.
The core tags of this character are 'brown\_hair, twintails, long\_hair, brown\_eyes, ahoge, bangs, ribbon, hair\_ribbon, bow, 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"
] |
f448c1487910b2dbdefa498241f7243f92fe2751
|
# Dataset Card for "wikipedia-zh-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
indiejoseph/wikipedia-zh-filtered
|
[
"region:us"
] |
2023-09-16T08:44:19+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": 258992903, "num_examples": 44344}], "download_size": 164712496, "dataset_size": 258992903}}
|
2023-09-16T08:46:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wikipedia-zh-filtered"
More Information needed
|
[
"# Dataset Card for \"wikipedia-zh-filtered\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia-zh-filtered\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia-zh-filtered\"\n\nMore Information needed"
] |
6c69c190d255327e9345120626f79a9ee4fc60c4
|
# Dataset Card for "summary_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Andyrasika/summary_qa
|
[
"region:us"
] |
2023-09-16T08:48:37+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 294050.25, "num_examples": 420}, {"name": "test", "num_bytes": 98016.75, "num_examples": 140}], "download_size": 211064, "dataset_size": 392067.0}}
|
2023-09-16T08:48:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "summary_qa"
More Information needed
|
[
"# Dataset Card for \"summary_qa\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"summary_qa\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"summary_qa\"\n\nMore Information needed"
] |
34972960b84a13d08d8ed2f6fced5f9574862abe
|
# Dataset of tanaka_kotoha/田中琴葉/타나카코토하 (THE iDOLM@STER: Million Live!)
This is the dataset of tanaka_kotoha/田中琴葉/타나카코토하 (THE iDOLM@STER: Million Live!), containing 355 images and their tags.
The core tags of this character are `long_hair, brown_hair, brown_eyes, hairband, bangs, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 355 | 414.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_kotoha_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 355 | 255.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_kotoha_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 818 | 528.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_kotoha_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 355 | 374.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_kotoha_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 818 | 724.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tanaka_kotoha_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tanaka_kotoha_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, solo, looking_at_viewer, open_mouth, blush, skirt, cleavage, medium_breasts, navel, necklace, :d, hair_ornament |
| 1 | 7 |  |  |  |  |  | 1girl, solo, skirt, blush, open_mouth, :d, looking_at_viewer, bow, crown, microphone, necktie |
| 2 | 7 |  |  |  |  |  | navel, 1girl, blush, looking_at_viewer, sailor_bikini, smile, solo, white_bikini, cowboy_shot, open_mouth, standing, bare_arms, bare_shoulders, blue_neckerchief, blue_sky, blunt_bangs, collarbone, day, hat, medium_breasts, outdoors, arms_up, beach, blue_sailor_collar, bow_bikini, cleavage, cloud, white_hairband, white_headwear |
| 3 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, simple_background, solo, white_background, closed_mouth, smile, upper_body, yellow_hairband, white_shirt, arms_up, long_sleeves, short_sleeves, tying_hair, very_long_hair |
| 4 | 10 |  |  |  |  |  | plaid_skirt, pleated_skirt, white_shirt, 1girl, school_uniform, solo, white_background, simple_background, blush, hair_ribbon, looking_at_viewer, miniskirt, socks, long_sleeves, blazer, blue_skirt, bow, closed_mouth, diagonal-striped_necktie, diagonal_stripes, dress_shirt, green_ribbon, red_skirt, wing_collar, brown_footwear, crotch_seam, loafers, short_sleeves, standing, sweater_vest, white_panties |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, penis, solo_focus, vaginal, female_pubic_hair, looking_at_viewer, pussy, sweat, bar_censor, clothed_sex, navel, nipples, pov, spread_legs, :d, lying, m_legs, no_panties, nude, serafuku, skirt_lift |
| 6 | 9 |  |  |  |  |  | 1girl, solo, belt, epaulettes, looking_at_viewer, red_eyes, smile, dark_persona, red_jacket, black_gloves, cape, long_sleeves, open_mouth, pleated_skirt, white_shirt, black_hairband, black_thighhighs, boots, collared_shirt, holding_sword, uniform |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | open_mouth | blush | skirt | cleavage | medium_breasts | navel | necklace | :d | hair_ornament | bow | crown | microphone | necktie | sailor_bikini | smile | white_bikini | cowboy_shot | standing | bare_arms | bare_shoulders | blue_neckerchief | blue_sky | blunt_bangs | collarbone | day | hat | outdoors | arms_up | beach | blue_sailor_collar | bow_bikini | cloud | white_hairband | white_headwear | simple_background | white_background | closed_mouth | upper_body | yellow_hairband | white_shirt | long_sleeves | short_sleeves | tying_hair | very_long_hair | plaid_skirt | pleated_skirt | school_uniform | hair_ribbon | miniskirt | socks | blazer | blue_skirt | diagonal-striped_necktie | diagonal_stripes | dress_shirt | green_ribbon | red_skirt | wing_collar | brown_footwear | crotch_seam | loafers | sweater_vest | white_panties | 1boy | hetero | penis | solo_focus | vaginal | female_pubic_hair | pussy | sweat | bar_censor | clothed_sex | nipples | pov | spread_legs | lying | m_legs | no_panties | nude | serafuku | skirt_lift | belt | epaulettes | red_eyes | dark_persona | red_jacket | black_gloves | cape | black_hairband | black_thighhighs | boots | collared_shirt | holding_sword | uniform |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:--------|:--------|:-----------|:-----------------|:--------|:-----------|:-----|:----------------|:------|:--------|:-------------|:----------|:----------------|:--------|:---------------|:--------------|:-----------|:------------|:-----------------|:-------------------|:-----------|:--------------|:-------------|:------|:------|:-----------|:----------|:--------|:---------------------|:-------------|:--------|:-----------------|:-----------------|:--------------------|:-------------------|:---------------|:-------------|:------------------|:--------------|:---------------|:----------------|:-------------|:-----------------|:--------------|:----------------|:-----------------|:--------------|:------------|:--------|:---------|:-------------|:---------------------------|:-------------------|:--------------|:---------------|:------------|:--------------|:-----------------|:--------------|:----------|:---------------|:----------------|:-------|:---------|:--------|:-------------|:----------|:--------------------|:--------|:--------|:-------------|:--------------|:----------|:------|:--------------|:--------|:---------|:-------------|:-------|:-----------|:-------------|:-------|:-------------|:-----------|:---------------|:-------------|:---------------|:-------|:-----------------|:-------------------|:--------|:-----------------|:----------------|:----------|
| 0 | 21 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | 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 | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | X | X | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/tanaka_kotoha_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T08:56:37+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:29:15+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tanaka\_kotoha/田中琴葉/타나카코토하 (THE iDOLM@STER: Million Live!)
=====================================================================
This is the dataset of tanaka\_kotoha/田中琴葉/타나카코토하 (THE iDOLM@STER: Million Live!), containing 355 images and their tags.
The core tags of this character are 'long\_hair, brown\_hair, brown\_eyes, hairband, bangs, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
5e3ce7cad729b9c6f7652e67d6f881208c305a96
|
# Dataset Card for Evaluation run of ethzanalytics/pythia-31m
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ethzanalytics/pythia-31m
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ethzanalytics/pythia-31m](https://huggingface.co/ethzanalytics/pythia-31m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_ethzanalytics__pythia-31m_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-13T13:01:31.225551](https://huggingface.co/datasets/open-llm-leaderboard/details_ethzanalytics__pythia-31m_public/blob/main/results_2023-11-13T13-01-31.225551.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": 0.2486090533214635,
"acc_stderr": 0.030580280893238346,
"acc_norm": 0.24951095231696532,
"acc_norm_stderr": 0.031375786973211,
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015023,
"mc2": 0.49102256781530107,
"mc2_stderr": 0.015750842651440947,
"em": 0.0006291946308724832,
"em_stderr": 0.0002568002749723811,
"f1": 0.013650377516778552,
"f1_stderr": 0.0006539918270891778
},
"harness|arc:challenge|25": {
"acc": 0.1697952218430034,
"acc_stderr": 0.010971775157784212,
"acc_norm": 0.21843003412969283,
"acc_norm_stderr": 0.012074291605700985
},
"harness|hellaswag|10": {
"acc": 0.26309500099581756,
"acc_stderr": 0.004394136724172986,
"acc_norm": 0.26996614220274845,
"acc_norm_stderr": 0.00443034623465038
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.03785714465066655,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.03785714465066655
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21132075471698114,
"acc_stderr": 0.025125766484827842,
"acc_norm": 0.21132075471698114,
"acc_norm_stderr": 0.025125766484827842
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.21,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.21,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2138728323699422,
"acc_stderr": 0.03126511206173044,
"acc_norm": 0.2138728323699422,
"acc_norm_stderr": 0.03126511206173044
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.041583075330832865,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.041583075330832865
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2851063829787234,
"acc_stderr": 0.029513196625539355,
"acc_norm": 0.2851063829787234,
"acc_norm_stderr": 0.029513196625539355
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.04049339297748141,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.04049339297748141
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.23448275862068965,
"acc_stderr": 0.035306258743465914,
"acc_norm": 0.23448275862068965,
"acc_norm_stderr": 0.035306258743465914
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2566137566137566,
"acc_stderr": 0.022494510767503154,
"acc_norm": 0.2566137566137566,
"acc_norm_stderr": 0.022494510767503154
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.1746031746031746,
"acc_stderr": 0.0339549002085611,
"acc_norm": 0.1746031746031746,
"acc_norm_stderr": 0.0339549002085611
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.24193548387096775,
"acc_stderr": 0.024362599693031103,
"acc_norm": 0.24193548387096775,
"acc_norm_stderr": 0.024362599693031103
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.0317852971064275,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.0317852971064275
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909281,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909281
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.20606060606060606,
"acc_stderr": 0.031584153240477086,
"acc_norm": 0.20606060606060606,
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"harness|hendrycksTest-high_school_macroeconomics|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-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"acc_norm_stderr": 0.042365112580946336
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm_stderr": 0.023468429832451156
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|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|truthfulqa:mc|0": {
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"harness|winogrande|5": {
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"harness|drop|3": {
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"f1": 0.013650377516778552,
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},
"harness|gsm8k|5": {
"acc": 0.002274450341167551,
"acc_stderr": 0.0013121578148674168
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_ethzanalytics__pythia-31m
|
[
"region:us"
] |
2023-09-16T09:00:07+00:00
|
{"pretty_name": "Evaluation run of ethzanalytics/pythia-31m", "dataset_summary": "Dataset automatically created during the evaluation run of model [ethzanalytics/pythia-31m](https://huggingface.co/ethzanalytics/pythia-31m) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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_ethzanalytics__pythia-31m_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-13T13:01:31.225551](https://huggingface.co/datasets/open-llm-leaderboard/details_ethzanalytics__pythia-31m_public/blob/main/results_2023-11-13T13-01-31.225551.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\": 0.2486090533214635,\n \"acc_stderr\": 0.030580280893238346,\n \"acc_norm\": 0.24951095231696532,\n \"acc_norm_stderr\": 0.031375786973211,\n \"mc1\": 0.26193390452876375,\n \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.49102256781530107,\n \"mc2_stderr\": 0.015750842651440947,\n \"em\": 0.0006291946308724832,\n \"em_stderr\": 0.0002568002749723811,\n \"f1\": 0.013650377516778552,\n \"f1_stderr\": 0.0006539918270891778\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.1697952218430034,\n \"acc_stderr\": 0.010971775157784212,\n \"acc_norm\": 0.21843003412969283,\n \"acc_norm_stderr\": 0.012074291605700985\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.26309500099581756,\n \"acc_stderr\": 0.004394136724172986,\n \"acc_norm\": 0.26996614220274845,\n \"acc_norm_stderr\": 0.00443034623465038\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.03785714465066655,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.03785714465066655\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827842,\n \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827842\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2138728323699422,\n \"acc_stderr\": 0.03126511206173044,\n \"acc_norm\": 0.2138728323699422,\n \"acc_norm_stderr\": 0.03126511206173044\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\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2851063829787234,\n \"acc_stderr\": 0.029513196625539355,\n \"acc_norm\": 0.2851063829787234,\n \"acc_norm_stderr\": 0.029513196625539355\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.23448275862068965,\n \"acc_stderr\": 0.035306258743465914,\n \"acc_norm\": 0.23448275862068965,\n \"acc_norm_stderr\": 0.035306258743465914\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1746031746031746,\n \"acc_stderr\": 0.0339549002085611,\n \"acc_norm\": 0.1746031746031746,\n \"acc_norm_stderr\": 0.0339549002085611\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n \"acc_stderr\": 0.024362599693031103,\n \"acc_norm\": 0.24193548387096775,\n \"acc_norm_stderr\": 0.024362599693031103\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.20606060606060606,\n \"acc_stderr\": 0.031584153240477086,\n \"acc_norm\": 0.20606060606060606,\n \"acc_norm_stderr\": 0.031584153240477086\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.30303030303030304,\n \"acc_stderr\": 0.032742879140268674,\n \"acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.032742879140268674\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.22797927461139897,\n \"acc_stderr\": 0.030276909945178263,\n \"acc_norm\": 0.22797927461139897,\n \"acc_norm_stderr\": 0.030276909945178263\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2230769230769231,\n \"acc_stderr\": 0.02110773012724398,\n \"acc_norm\": 0.2230769230769231,\n \"acc_norm_stderr\": 0.02110773012724398\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275794,\n \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275794\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.36554621848739494,\n \"acc_stderr\": 0.03128217706368461,\n \"acc_norm\": 0.36554621848739494,\n \"acc_norm_stderr\": 0.03128217706368461\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.24503311258278146,\n \"acc_stderr\": 0.035118075718047245,\n \"acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.035118075718047245\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22935779816513763,\n \"acc_stderr\": 0.018025349724618684,\n \"acc_norm\": 0.22935779816513763,\n \"acc_norm_stderr\": 0.018025349724618684\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.36574074074074076,\n \"acc_stderr\": 0.03284738857647206,\n \"acc_norm\": 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[{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["**/details_harness|winogrande|5_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-13T13-01-31.225551.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_13T13_01_31.225551", "path": ["results_2023-11-13T13-01-31.225551.parquet"]}, {"split": "latest", "path": ["results_2023-11-13T13-01-31.225551.parquet"]}]}]}
|
2023-12-01T14:57:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of ethzanalytics/pythia-31m
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ethzanalytics/pythia-31m on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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 2023-11-13T13:01:31.225551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of ethzanalytics/pythia-31m",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ethzanalytics/pythia-31m on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-11-13T13:01:31.225551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of ethzanalytics/pythia-31m",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ethzanalytics/pythia-31m on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-11-13T13:01:31.225551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
19,
31,
168,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ethzanalytics/pythia-31m## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ethzanalytics/pythia-31m on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-11-13T13:01:31.225551(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
396fec4e386cc6a9558e6ef366506e657cef5932
|
# Dataset Card for "wikipedia-en-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
indiejoseph/wikipedia-en-filtered
|
[
"language:en",
"region:us"
] |
2023-09-16T09:01:00+00:00
|
{"language": ["en"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49741517, "num_examples": 17260}], "download_size": 27011805, "dataset_size": 49741517}}
|
2023-10-02T19:50:06+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #region-us
|
# Dataset Card for "wikipedia-en-filtered"
More Information needed
|
[
"# Dataset Card for \"wikipedia-en-filtered\"\n\nMore Information needed"
] |
[
"TAGS\n#language-English #region-us \n",
"# Dataset Card for \"wikipedia-en-filtered\"\n\nMore Information needed"
] |
[
10,
16
] |
[
"passage: TAGS\n#language-English #region-us \n# Dataset Card for \"wikipedia-en-filtered\"\n\nMore Information needed"
] |
f36642ac71a9471b2d0902384a3365fa6514f988
|
# Dataset Card for "a48196ad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/a48196ad
|
[
"region:us"
] |
2023-09-16T09:13:25+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 155, "num_examples": 10}], "download_size": 1306, "dataset_size": 155}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T09:13:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "a48196ad"
More Information needed
|
[
"# Dataset Card for \"a48196ad\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"a48196ad\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"a48196ad\"\n\nMore Information needed"
] |
4909a04803e5d982387c424aaeae49691205e9ed
|
# Dataset Card for Evaluation run of player1537/dolphinette
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/player1537/dolphinette
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [player1537/dolphinette](https://huggingface.co/player1537/dolphinette) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_player1537__dolphinette",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T20:59:16.214990](https://huggingface.co/datasets/open-llm-leaderboard/details_player1537__dolphinette/blob/main/results_2023-10-23T20-59-16.214990.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.04194630872483222,
"em_stderr": 0.0020529645493585565,
"f1": 0.0809899328859061,
"f1_stderr": 0.002298583656346721,
"acc": 0.27111286503551696,
"acc_stderr": 0.007001142252211221
},
"harness|drop|3": {
"em": 0.04194630872483222,
"em_stderr": 0.0020529645493585565,
"f1": 0.0809899328859061,
"f1_stderr": 0.002298583656346721
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5422257300710339,
"acc_stderr": 0.014002284504422442
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_player1537__dolphinette
|
[
"region:us"
] |
2023-09-16T09:14:11+00:00
|
{"pretty_name": "Evaluation run of player1537/dolphinette", "dataset_summary": "Dataset automatically created during the evaluation run of model [player1537/dolphinette](https://huggingface.co/player1537/dolphinette) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_player1537__dolphinette\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T20:59:16.214990](https://huggingface.co/datasets/open-llm-leaderboard/details_player1537__dolphinette/blob/main/results_2023-10-23T20-59-16.214990.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.04194630872483222,\n \"em_stderr\": 0.0020529645493585565,\n \"f1\": 0.0809899328859061,\n \"f1_stderr\": 0.002298583656346721,\n \"acc\": 0.27111286503551696,\n \"acc_stderr\": 0.007001142252211221\n },\n \"harness|drop|3\": {\n \"em\": 0.04194630872483222,\n \"em_stderr\": 0.0020529645493585565,\n \"f1\": 0.0809899328859061,\n \"f1_stderr\": 0.002298583656346721\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5422257300710339,\n \"acc_stderr\": 0.014002284504422442\n }\n}\n```", "repo_url": "https://huggingface.co/player1537/dolphinette", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_16T10_13_56.989865", "path": ["**/details_harness|arc:challenge|25_2023-09-16T10-13-56.989865.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-16T10-13-56.989865.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T20_59_16.214990", "path": ["**/details_harness|drop|3_2023-10-23T20-59-16.214990.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T20-59-16.214990.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T20_59_16.214990", "path": ["**/details_harness|gsm8k|5_2023-10-23T20-59-16.214990.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T20-59-16.214990.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_16T10_13_56.989865", "path": ["**/details_harness|hellaswag|10_2023-09-16T10-13-56.989865.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-16T10-13-56.989865.parquet"]}]}, 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|
2023-10-23T19:59:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of player1537/dolphinette
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model player1537/dolphinette on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T20:59:16.214990(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of player1537/dolphinette",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model player1537/dolphinette on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T20:59:16.214990(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of player1537/dolphinette",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model player1537/dolphinette on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T20:59:16.214990(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of player1537/dolphinette## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model player1537/dolphinette on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T20:59:16.214990(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
acfd3c18fa23b11e50ad191de5dfaaea8f7ffd96
|
Trucks Detection - v1
==============================
This dataset was exported via roboflow.com on September 11, 2023 at 8:38 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
The dataset includes 746 images.
Trucks are annotated in YOLOv8 format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
* Grayscale (CRT phosphor)
The following augmentation was applied to create 3 versions of each source image:
* Random Gaussian blur of between 0 and 1.5 pixels
* Salt and pepper noise was applied to 5 percent of pixels
|
Beetho/Trucks-Detection-Yolov8
|
[
"task_categories:object-detection",
"size_categories:n<1K",
"language:en",
"language:fr",
"language:de",
"language:it",
"language:es",
"language:ca",
"license:cc-by-3.0",
"region:us"
] |
2023-09-16T09:30:56+00:00
|
{"language": ["en", "fr", "de", "it", "es", "ca"], "license": "cc-by-3.0", "size_categories": ["n<1K"], "task_categories": ["object-detection"]}
|
2023-09-16T10:19:48+00:00
|
[] |
[
"en",
"fr",
"de",
"it",
"es",
"ca"
] |
TAGS
#task_categories-object-detection #size_categories-n<1K #language-English #language-French #language-German #language-Italian #language-Spanish #language-Catalan #license-cc-by-3.0 #region-us
|
Trucks Detection - v1
==============================
This dataset was exported via URL on September 11, 2023 at 8:38 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
The dataset includes 746 images.
Trucks are annotated in YOLOv8 format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
* Grayscale (CRT phosphor)
The following augmentation was applied to create 3 versions of each source image:
* Random Gaussian blur of between 0 and 1.5 pixels
* Salt and pepper noise was applied to 5 percent of pixels
|
[] |
[
"TAGS\n#task_categories-object-detection #size_categories-n<1K #language-English #language-French #language-German #language-Italian #language-Spanish #language-Catalan #license-cc-by-3.0 #region-us \n"
] |
[
65
] |
[
"passage: TAGS\n#task_categories-object-detection #size_categories-n<1K #language-English #language-French #language-German #language-Italian #language-Spanish #language-Catalan #license-cc-by-3.0 #region-us \n"
] |
b48ce875c15c39bfa13e1da288d52782d345cf33
|
# Dataset Card for "NLU_aug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/NLU_aug
|
[
"region:us"
] |
2023-09-16T09:37:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "intent", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 163690, "num_examples": 1299}], "download_size": 51331, "dataset_size": 163690}}
|
2023-09-16T09:37:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "NLU_aug"
More Information needed
|
[
"# Dataset Card for \"NLU_aug\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"NLU_aug\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"NLU_aug\"\n\nMore Information needed"
] |
258567068600b5fbf7d792c7e7641a1da72230e6
|
# Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue
This is the dataset of the article in the title. It contains 85 patches of 1024x1024 pixels from H&E stained WSIs of 9 different patients. It contains two main classes: tumoural (2) and non tumoural (1). Due to the difficulty of the problem, 153 cells were labelled as uncertain. For technical reasons, we decided to eliminate them in the train and validation set and we carefully chose the test set so that it included no uncertain cell. In total there are 21255 cells in the train set, 4114 in the validation set and 5533 in the test set. We manually reviewed that no patient is in two splits at the same time, ensuring that the split has no data leakage in any way.
## Structure
The data is provided in several ways. In the orig folder you have the images without any annotation. Later in overlay the same images with the cells overlayed on top are provided for visualization purposes being red healthy cells and green the tumoural ones. Annotations were made using a software called QuPath, the raw geojson files extracted from the application are in raw_geojson. However, bear in mind that it may contain duplicated cells and uncertain cells. We are releasing it together with the scripts in the scripts folder so that any interested researcher can load the annotations back into QuPath and review the labels. If you, as an expert, believe we have incorrectly labelled some cells, please, feel free to contact us. The rest of the folders (train, test, validation) contain the data ready to use and with the same structure as specified in the [tumourkit package documentation](https://lung-tumour-study.readthedocs.io/en/latest/usage.html#make-dirs). Just move them into the data folder. Notice you will need to move the orig folder too.
Any pred or hov folder is provided as an example. They contain predictions from one of our models. If you were to train your own models, you should delete them. Also, the npy folders are crops of the original images of size 518x518. You can train Hovernet with other shapes if you want by modifying the code provided by the [Tumourkit library](https://github.com/Jerry-Master/lung-tumour-study).
|
Jerry-Master/lung-tumour-study
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-09-16T09:39:33+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-09-16T11:04:37+00:00
|
[] |
[] |
TAGS
#license-cc-by-nc-4.0 #region-us
|
# Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue
This is the dataset of the article in the title. It contains 85 patches of 1024x1024 pixels from H&E stained WSIs of 9 different patients. It contains two main classes: tumoural (2) and non tumoural (1). Due to the difficulty of the problem, 153 cells were labelled as uncertain. For technical reasons, we decided to eliminate them in the train and validation set and we carefully chose the test set so that it included no uncertain cell. In total there are 21255 cells in the train set, 4114 in the validation set and 5533 in the test set. We manually reviewed that no patient is in two splits at the same time, ensuring that the split has no data leakage in any way.
## Structure
The data is provided in several ways. In the orig folder you have the images without any annotation. Later in overlay the same images with the cells overlayed on top are provided for visualization purposes being red healthy cells and green the tumoural ones. Annotations were made using a software called QuPath, the raw geojson files extracted from the application are in raw_geojson. However, bear in mind that it may contain duplicated cells and uncertain cells. We are releasing it together with the scripts in the scripts folder so that any interested researcher can load the annotations back into QuPath and review the labels. If you, as an expert, believe we have incorrectly labelled some cells, please, feel free to contact us. The rest of the folders (train, test, validation) contain the data ready to use and with the same structure as specified in the tumourkit package documentation. Just move them into the data folder. Notice you will need to move the orig folder too.
Any pred or hov folder is provided as an example. They contain predictions from one of our models. If you were to train your own models, you should delete them. Also, the npy folders are crops of the original images of size 518x518. You can train Hovernet with other shapes if you want by modifying the code provided by the Tumourkit library.
|
[
"# Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue\n\nThis is the dataset of the article in the title. It contains 85 patches of 1024x1024 pixels from H&E stained WSIs of 9 different patients. It contains two main classes: tumoural (2) and non tumoural (1). Due to the difficulty of the problem, 153 cells were labelled as uncertain. For technical reasons, we decided to eliminate them in the train and validation set and we carefully chose the test set so that it included no uncertain cell. In total there are 21255 cells in the train set, 4114 in the validation set and 5533 in the test set. We manually reviewed that no patient is in two splits at the same time, ensuring that the split has no data leakage in any way.",
"## Structure\n\nThe data is provided in several ways. In the orig folder you have the images without any annotation. Later in overlay the same images with the cells overlayed on top are provided for visualization purposes being red healthy cells and green the tumoural ones. Annotations were made using a software called QuPath, the raw geojson files extracted from the application are in raw_geojson. However, bear in mind that it may contain duplicated cells and uncertain cells. We are releasing it together with the scripts in the scripts folder so that any interested researcher can load the annotations back into QuPath and review the labels. If you, as an expert, believe we have incorrectly labelled some cells, please, feel free to contact us. The rest of the folders (train, test, validation) contain the data ready to use and with the same structure as specified in the tumourkit package documentation. Just move them into the data folder. Notice you will need to move the orig folder too. \n\nAny pred or hov folder is provided as an example. They contain predictions from one of our models. If you were to train your own models, you should delete them. Also, the npy folders are crops of the original images of size 518x518. You can train Hovernet with other shapes if you want by modifying the code provided by the Tumourkit library."
] |
[
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"# Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue\n\nThis is the dataset of the article in the title. It contains 85 patches of 1024x1024 pixels from H&E stained WSIs of 9 different patients. It contains two main classes: tumoural (2) and non tumoural (1). Due to the difficulty of the problem, 153 cells were labelled as uncertain. For technical reasons, we decided to eliminate them in the train and validation set and we carefully chose the test set so that it included no uncertain cell. In total there are 21255 cells in the train set, 4114 in the validation set and 5533 in the test set. We manually reviewed that no patient is in two splits at the same time, ensuring that the split has no data leakage in any way.",
"## Structure\n\nThe data is provided in several ways. In the orig folder you have the images without any annotation. Later in overlay the same images with the cells overlayed on top are provided for visualization purposes being red healthy cells and green the tumoural ones. Annotations were made using a software called QuPath, the raw geojson files extracted from the application are in raw_geojson. However, bear in mind that it may contain duplicated cells and uncertain cells. We are releasing it together with the scripts in the scripts folder so that any interested researcher can load the annotations back into QuPath and review the labels. If you, as an expert, believe we have incorrectly labelled some cells, please, feel free to contact us. The rest of the folders (train, test, validation) contain the data ready to use and with the same structure as specified in the tumourkit package documentation. Just move them into the data folder. Notice you will need to move the orig folder too. \n\nAny pred or hov folder is provided as an example. They contain predictions from one of our models. If you were to train your own models, you should delete them. Also, the npy folders are crops of the original images of size 518x518. You can train Hovernet with other shapes if you want by modifying the code provided by the Tumourkit library."
] |
[
17,
198,
322
] |
[
"passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n# Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue\n\nThis is the dataset of the article in the title. It contains 85 patches of 1024x1024 pixels from H&E stained WSIs of 9 different patients. It contains two main classes: tumoural (2) and non tumoural (1). Due to the difficulty of the problem, 153 cells were labelled as uncertain. For technical reasons, we decided to eliminate them in the train and validation set and we carefully chose the test set so that it included no uncertain cell. In total there are 21255 cells in the train set, 4114 in the validation set and 5533 in the test set. We manually reviewed that no patient is in two splits at the same time, ensuring that the split has no data leakage in any way."
] |
eda51f8fbf62f430b65995c62ddd4b7ac5bab988
|
# Dataset Card for "airoboros-2.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TinyPixel/airoboros-2.2
|
[
"region:us"
] |
2023-09-16T09:42:02+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 171010250, "num_examples": 88240}], "download_size": 94788763, "dataset_size": 171010250}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T09:42:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "airoboros-2.2"
More Information needed
|
[
"# Dataset Card for \"airoboros-2.2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"airoboros-2.2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"airoboros-2.2\"\n\nMore Information needed"
] |
de7f423b0105ae76d521d70bac24dfee01d9cdbf
|
# Dataset Card for "Drone_RCS_Image"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Goorm-AI-04/Drone_RCS_Image
|
[
"region:us"
] |
2023-09-16T09:43:18+00:00
|
{"dataset_info": {"features": [{"name": "rcs_image", "dtype": "image"}, {"name": "drone_type", "dtype": "string"}, {"name": "frequency", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 31214190.0, "num_examples": 240}], "download_size": 31215528, "dataset_size": 31214190.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T09:50:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Drone_RCS_Image"
More Information needed
|
[
"# Dataset Card for \"Drone_RCS_Image\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Drone_RCS_Image\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Drone_RCS_Image\"\n\nMore Information needed"
] |
8f7c9c2198b6f6e531139a2bb6dbf678c06503c3
|
# Dataset Card for Evaluation run of aqweteddy/Tulpar-tv_marcoroni-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/aqweteddy/Tulpar-tv_marcoroni-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [aqweteddy/Tulpar-tv_marcoroni-7b](https://huggingface.co/aqweteddy/Tulpar-tv_marcoroni-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_aqweteddy__Tulpar-tv_marcoroni-7b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T11:05:38.004815](https://huggingface.co/datasets/open-llm-leaderboard/details_aqweteddy__Tulpar-tv_marcoroni-7b/blob/main/results_2023-09-16T11-05-38.004815.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": 0.3312563883942805,
"acc_stderr": 0.03372195940077684,
"acc_norm": 0.33458244613980964,
"acc_norm_stderr": 0.0337194423696009,
"mc1": 0.30354957160342716,
"mc1_stderr": 0.016095884155386847,
"mc2": 0.4937561621069656,
"mc2_stderr": 0.016106089320397136
},
"harness|arc:challenge|25": {
"acc": 0.38993174061433444,
"acc_stderr": 0.014252959848892877,
"acc_norm": 0.41638225255972694,
"acc_norm_stderr": 0.01440561827943617
},
"harness|hellaswag|10": {
"acc": 0.5012945628360884,
"acc_stderr": 0.0049897646867388306,
"acc_norm": 0.671081457876917,
"acc_norm_stderr": 0.004688601416815203
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3925925925925926,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.3925925925925926,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.29605263157894735,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.29605263157894735,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3660377358490566,
"acc_stderr": 0.02964781353936524,
"acc_norm": 0.3660377358490566,
"acc_norm_stderr": 0.02964781353936524
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.039420826399272135,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.039420826399272135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.14,
"acc_stderr": 0.03487350880197772,
"acc_norm": 0.14,
"acc_norm_stderr": 0.03487350880197772
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.24,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.24,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.26011560693641617,
"acc_stderr": 0.03345036916788991,
"acc_norm": 0.26011560693641617,
"acc_norm_stderr": 0.03345036916788991
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.04158307533083286,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.04158307533083286
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3446808510638298,
"acc_stderr": 0.03106898596312215,
"acc_norm": 0.3446808510638298,
"acc_norm_stderr": 0.03106898596312215
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.04142439719489362,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.04142439719489362
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.03695183311650232,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.03695183311650232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.02256989707491842,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.02256989707491842
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.20634920634920634,
"acc_stderr": 0.036196045241242515,
"acc_norm": 0.20634920634920634,
"acc_norm_stderr": 0.036196045241242515
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.36451612903225805,
"acc_stderr": 0.02737987122994324,
"acc_norm": 0.36451612903225805,
"acc_norm_stderr": 0.02737987122994324
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2955665024630542,
"acc_stderr": 0.032104944337514575,
"acc_norm": 0.2955665024630542,
"acc_norm_stderr": 0.032104944337514575
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.44242424242424244,
"acc_stderr": 0.03878372113711275,
"acc_norm": 0.44242424242424244,
"acc_norm_stderr": 0.03878372113711275
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.30303030303030304,
"acc_stderr": 0.03274287914026868,
"acc_norm": 0.30303030303030304,
"acc_norm_stderr": 0.03274287914026868
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.3160621761658031,
"acc_stderr": 0.03355397369686174,
"acc_norm": 0.3160621761658031,
"acc_norm_stderr": 0.03355397369686174
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|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|truthfulqa:mc|0": {
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"mc2": 0.4937561621069656,
"mc2_stderr": 0.016106089320397136
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_aqweteddy__Tulpar-tv_marcoroni-7b
|
[
"region:us"
] |
2023-09-16T10:05:54+00:00
|
{"pretty_name": "Evaluation run of aqweteddy/Tulpar-tv_marcoroni-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [aqweteddy/Tulpar-tv_marcoroni-7b](https://huggingface.co/aqweteddy/Tulpar-tv_marcoroni-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_aqweteddy__Tulpar-tv_marcoroni-7b\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-16T11:05:38.004815](https://huggingface.co/datasets/open-llm-leaderboard/details_aqweteddy__Tulpar-tv_marcoroni-7b/blob/main/results_2023-09-16T11-05-38.004815.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\": 0.3312563883942805,\n \"acc_stderr\": 0.03372195940077684,\n \"acc_norm\": 0.33458244613980964,\n \"acc_norm_stderr\": 0.0337194423696009,\n \"mc1\": 0.30354957160342716,\n \"mc1_stderr\": 0.016095884155386847,\n \"mc2\": 0.4937561621069656,\n \"mc2_stderr\": 0.016106089320397136\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.38993174061433444,\n \"acc_stderr\": 0.014252959848892877,\n \"acc_norm\": 0.41638225255972694,\n \"acc_norm_stderr\": 0.01440561827943617\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5012945628360884,\n \"acc_stderr\": 0.0049897646867388306,\n \"acc_norm\": 0.671081457876917,\n \"acc_norm_stderr\": 0.004688601416815203\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.3925925925925926,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.3925925925925926,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.29605263157894735,\n \"acc_stderr\": 0.03715062154998904,\n \"acc_norm\": 0.29605263157894735,\n \"acc_norm_stderr\": 0.03715062154998904\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.3660377358490566,\n \"acc_stderr\": 0.02964781353936524,\n \"acc_norm\": 0.3660377358490566,\n \"acc_norm_stderr\": 0.02964781353936524\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.039420826399272135,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.039420826399272135\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.14,\n \"acc_stderr\": 0.03487350880197772,\n \"acc_norm\": 0.14,\n \"acc_norm_stderr\": 0.03487350880197772\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.26011560693641617,\n \"acc_stderr\": 0.03345036916788991,\n \"acc_norm\": 0.26011560693641617,\n \"acc_norm_stderr\": 0.03345036916788991\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\": {\n \"acc\": 0.3446808510638298,\n \"acc_stderr\": 0.03106898596312215,\n \"acc_norm\": 0.3446808510638298,\n \"acc_norm_stderr\": 0.03106898596312215\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n \"acc_stderr\": 0.04142439719489362,\n \"acc_norm\": 0.2631578947368421,\n \"acc_norm_stderr\": 0.04142439719489362\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.02256989707491842,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02256989707491842\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.20634920634920634,\n \"acc_stderr\": 0.036196045241242515,\n \"acc_norm\": 0.20634920634920634,\n \"acc_norm_stderr\": 0.036196045241242515\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.36451612903225805,\n \"acc_stderr\": 0.02737987122994324,\n \"acc_norm\": 0.36451612903225805,\n \"acc_norm_stderr\": 0.02737987122994324\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\": {\n \"acc\": 0.44242424242424244,\n \"acc_stderr\": 0.03878372113711275,\n \"acc_norm\": 0.44242424242424244,\n \"acc_norm_stderr\": 0.03878372113711275\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.30303030303030304,\n \"acc_stderr\": 0.03274287914026868,\n \"acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.03274287914026868\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.3160621761658031,\n \"acc_stderr\": 0.03355397369686174,\n \"acc_norm\": 0.3160621761658031,\n \"acc_norm_stderr\": 0.03355397369686174\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24358974358974358,\n \"acc_stderr\": 0.021763733684173933,\n \"acc_norm\": 0.24358974358974358,\n \"acc_norm_stderr\": 0.021763733684173933\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.22592592592592592,\n \"acc_stderr\": 0.02549753263960955,\n \"acc_norm\": 0.22592592592592592,\n \"acc_norm_stderr\": 0.02549753263960955\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.23949579831932774,\n \"acc_stderr\": 0.02772206549336127,\n \"acc_norm\": 0.23949579831932774,\n \"acc_norm_stderr\": 0.02772206549336127\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804724,\n \"acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804724\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.3504587155963303,\n \"acc_stderr\": 0.020456077599824457,\n \"acc_norm\": 0.3504587155963303,\n \"acc_norm_stderr\": 0.020456077599824457\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.19444444444444445,\n \"acc_stderr\": 0.02699145450203673,\n \"acc_norm\": 0.19444444444444445,\n \"acc_norm_stderr\": 0.02699145450203673\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.44607843137254904,\n \"acc_stderr\": 0.03488845451304974,\n \"acc_norm\": 0.44607843137254904,\n \"acc_norm_stderr\": 0.03488845451304974\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5316455696202531,\n \"acc_stderr\": 0.032481974005110756,\n \"acc_norm\": 0.5316455696202531,\n \"acc_norm_stderr\": 0.032481974005110756\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.38565022421524664,\n \"acc_stderr\": 0.03266842214289202,\n \"acc_norm\": 0.38565022421524664,\n \"acc_norm_stderr\": 0.03266842214289202\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.33587786259541985,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.33587786259541985,\n \"acc_norm_stderr\": 0.041423137719966634\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.36363636363636365,\n \"acc_stderr\": 0.043913262867240704,\n \"acc_norm\": 0.36363636363636365,\n \"acc_norm_stderr\": 0.043913262867240704\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 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["**/details_harness|hendrycksTest-virology|5_2023-09-16T11-05-38.004815.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_16T11_05_38.004815", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-16T11-05-38.004815.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-16T11-05-38.004815.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_16T11_05_38.004815", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-16T11-05-38.004815.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-16T11-05-38.004815.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_16T11_05_38.004815", "path": ["results_2023-09-16T11-05-38.004815.parquet"]}, {"split": "latest", "path": ["results_2023-09-16T11-05-38.004815.parquet"]}]}]}
|
2023-09-16T10:06:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of aqweteddy/Tulpar-tv_marcoroni-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model aqweteddy/Tulpar-tv_marcoroni-7b on the Open LLM Leaderboard.
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-09-16T11:05:38.004815(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of aqweteddy/Tulpar-tv_marcoroni-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model aqweteddy/Tulpar-tv_marcoroni-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T11:05:38.004815(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of aqweteddy/Tulpar-tv_marcoroni-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model aqweteddy/Tulpar-tv_marcoroni-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T11:05:38.004815(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of aqweteddy/Tulpar-tv_marcoroni-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model aqweteddy/Tulpar-tv_marcoroni-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-16T11:05:38.004815(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
864683cfab66cb2ddb83856b44832521688791be
|
# Dataset of tokugawa_matsuri/徳川まつり (THE iDOLM@STER: Million Live!)
This is the dataset of tokugawa_matsuri/徳川まつり (THE iDOLM@STER: Million Live!), containing 322 images and their tags.
The core tags of this character are `green_hair, brown_eyes, bangs, parted_bangs, curly_hair, ribbon, bow, breasts, hairband, medium_hair, hair_ribbon`, which are 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 | 322 | 339.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokugawa_matsuri_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 322 | 217.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokugawa_matsuri_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 708 | 439.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokugawa_matsuri_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 322 | 311.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokugawa_matsuri_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 708 | 591.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokugawa_matsuri_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tokugawa_matsuri_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | open_mouth, puffy_short_sleeves, 1girl, blush, looking_at_viewer, solo, smile, aqua_hair, simple_background, white_background, white_gloves, ;d, blue_dress, hair_bow, one_eye_closed, earrings, frilled_dress, star_(symbol), bracelet, polka_dot, short_hair |
| 1 | 19 |  |  |  |  |  | 1girl, solo, looking_at_viewer, puffy_short_sleeves, polka_dot_ribbon, choker, blush, polka_dot_bow, white_background, simple_background, hair_bow, red_dress, white_shirt, open_mouth, frills, red_hairband, aqua_hair, heart, smile |
| 2 | 9 |  |  |  |  |  | 1girl, dress, looking_at_viewer, open_mouth, solo, :o, choker, smile |
| 3 | 6 |  |  |  |  |  | 1girl, blush, collared_shirt, looking_at_viewer, open_mouth, simple_background, solo, white_background, white_shirt, red_bowtie, school_uniform, sweater_vest, :o, blue_vest, long_sleeves, polka_dot_ribbon, upper_body, aqua_hair, red_ribbon |
| 4 | 15 |  |  |  |  |  | 1girl, blush, cleavage, looking_at_viewer, solo, medium_breasts, smile, navel, open_mouth, red_bikini, frilled_bikini, bare_shoulders, collarbone, simple_background, aqua_hair, polka_dot, white_background |
| 5 | 7 |  |  |  |  |  | 1girl, fake_animal_ears, playboy_bunny, rabbit_ears, solo, cleavage, looking_at_viewer, blush, detached_collar, large_breasts, strapless_leotard, wrist_cuffs, bare_shoulders, bowtie, fishnet_pantyhose, medium_breasts, open_mouth, simple_background, white_background, black_leotard, covered_navel, rabbit_tail, smile |
| 6 | 6 |  |  |  |  |  | aiguillette, aqua_hair, epaulettes, long_sleeves, white_ascot, blue_jacket, short_hair, upper_body, white_background, frilled_sleeves, hair_between_eyes, looking_at_viewer, multiple_girls, open_mouth, orange_eyes, solo_focus, 1girl, black_gloves, blush, border |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | open_mouth | puffy_short_sleeves | 1girl | blush | looking_at_viewer | solo | smile | aqua_hair | simple_background | white_background | white_gloves | ;d | blue_dress | hair_bow | one_eye_closed | earrings | frilled_dress | star_(symbol) | bracelet | polka_dot | short_hair | polka_dot_ribbon | choker | polka_dot_bow | red_dress | white_shirt | frills | red_hairband | heart | dress | :o | collared_shirt | red_bowtie | school_uniform | sweater_vest | blue_vest | long_sleeves | upper_body | red_ribbon | cleavage | medium_breasts | navel | red_bikini | frilled_bikini | bare_shoulders | collarbone | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | large_breasts | strapless_leotard | wrist_cuffs | bowtie | fishnet_pantyhose | black_leotard | covered_navel | rabbit_tail | aiguillette | epaulettes | white_ascot | blue_jacket | frilled_sleeves | hair_between_eyes | multiple_girls | orange_eyes | solo_focus | black_gloves | border |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------|:----------------------|:--------|:--------|:--------------------|:-------|:--------|:------------|:--------------------|:-------------------|:---------------|:-----|:-------------|:-----------|:-----------------|:-----------|:----------------|:----------------|:-----------|:------------|:-------------|:-------------------|:---------|:----------------|:------------|:--------------|:---------|:---------------|:--------|:--------|:-----|:-----------------|:-------------|:-----------------|:---------------|:------------|:---------------|:-------------|:-------------|:-----------|:-----------------|:--------|:-------------|:-----------------|:-----------------|:-------------|:-------------------|:----------------|:--------------|:------------------|:----------------|:--------------------|:--------------|:---------|:--------------------|:----------------|:----------------|:--------------|:--------------|:-------------|:--------------|:--------------|:------------------|:--------------------|:-----------------|:--------------|:-------------|:---------------|:---------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 19 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | 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 | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | X | X | X | | | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/tokugawa_matsuri_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T10:10:28+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:22:34+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tokugawa\_matsuri/徳川まつり (THE iDOLM@STER: Million Live!)
==================================================================
This is the dataset of tokugawa\_matsuri/徳川まつり (THE iDOLM@STER: Million Live!), containing 322 images and their tags.
The core tags of this character are 'green\_hair, brown\_eyes, bangs, parted\_bangs, curly\_hair, ribbon, bow, breasts, hairband, medium\_hair, hair\_ribbon', which are 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"
] |
212468b2a138c8cb462507e55ef06c73ea15bd41
|
# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0](https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ-V2.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T22:40:41.083519](https://huggingface.co/datasets/open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ-V2.0/blob/main/results_2023-10-23T22-40-41.083519.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.10864093959731544,
"em_stderr": 0.0031868582704839116,
"f1": 0.15248846476509997,
"f1_stderr": 0.0032781428140160563,
"acc": 0.4430471890103538,
"acc_stderr": 0.011315335280186417
},
"harness|drop|3": {
"em": 0.10864093959731544,
"em_stderr": 0.0031868582704839116,
"f1": 0.15248846476509997,
"f1_stderr": 0.0032781428140160563
},
"harness|gsm8k|5": {
"acc": 0.1599696739954511,
"acc_stderr": 0.010097377827752538
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620296
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ-V2.0
|
[
"region:us"
] |
2023-09-16T10:13:33+00:00
|
{"pretty_name": "Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0](https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ-V2.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T22:40:41.083519](https://huggingface.co/datasets/open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ-V2.0/blob/main/results_2023-10-23T22-40-41.083519.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.10864093959731544,\n \"em_stderr\": 0.0031868582704839116,\n \"f1\": 0.15248846476509997,\n \"f1_stderr\": 0.0032781428140160563,\n \"acc\": 0.4430471890103538,\n \"acc_stderr\": 0.011315335280186417\n },\n \"harness|drop|3\": {\n \"em\": 0.10864093959731544,\n \"em_stderr\": 0.0031868582704839116,\n \"f1\": 0.15248846476509997,\n \"f1_stderr\": 0.0032781428140160563\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1599696739954511,\n \"acc_stderr\": 0.010097377827752538\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620296\n }\n}\n```", "repo_url": "https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_16T11_13_20.345757", "path": ["**/details_harness|arc:challenge|25_2023-09-16T11-13-20.345757.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-16T11-13-20.345757.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T22_40_41.083519", "path": ["**/details_harness|drop|3_2023-10-23T22-40-41.083519.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T22-40-41.083519.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T22_40_41.083519", "path": ["**/details_harness|gsm8k|5_2023-10-23T22-40-41.083519.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T22-40-41.083519.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_16T11_13_20.345757", "path": ["**/details_harness|hellaswag|10_2023-09-16T11-13-20.345757.parquet"]}, {"split": "latest", "path": 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2023-10-23T21:40:53+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T22:40:41.083519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T22:40:41.083519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T22:40:41.083519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
31,
31,
179,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model elliotthwang/Elliott-Chinese-LLaMa-GPTQ-V2.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T22:40:41.083519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
9831da317db48161c23f6f5d2f3f686a6aaee3c7
|
# Bangumi Image Base of Toaru Kagaku No Railgun
This is the image base of bangumi Toaru Kagaku no Railgun, we detected 165 characters, 18219 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 82 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 145 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 24 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1375 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 14 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 38 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 83 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 27 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 38 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 40 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 4044 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 94 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 65 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 38 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 18 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 38 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 44 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 38 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 43 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 118 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 217 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 98 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 56 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 136 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 31 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 33 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 46 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 421 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 86 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 116 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 27 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 126 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 85 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 316 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 102 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 19 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 38 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 75 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 28 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 82 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 41 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 42 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 61 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 42 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 37 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 24 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 70 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 154 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 82 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 61 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 102 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 1930 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 45 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 21 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 35 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 36 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 35 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 51 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 231 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 13 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 36 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 14 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 25 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 231 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 80 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 21 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 11 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 79 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 31 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 33 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 18 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 356 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 23 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 16 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 16 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 27 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 25 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 18 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 12 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 1443 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 67 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 35 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 46 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 73 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 18 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 22 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 64 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 40 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 26 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 20 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 17 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 15 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 365 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 16 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 34 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 11 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 168 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 28 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 17 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 38 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 21 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 16 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 22 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 65 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 10 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 120 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 27 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 17 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 17 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 15 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 36 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 17 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 16 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 16 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 20 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 199 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 26 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 18 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 7 | [Download](119/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 120 | 149 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 41 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 40 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 16 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 67 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 12 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 40 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 15 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 9 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 15 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 14 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 50 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 8 | [Download](132/dataset.zip) |  |  |  |  |  |  |  |  |
| 133 | 18 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 57 | [Download](134/dataset.zip) |  |  |  |  |  |  |  |  |
| 135 | 28 | [Download](135/dataset.zip) |  |  |  |  |  |  |  |  |
| 136 | 13 | [Download](136/dataset.zip) |  |  |  |  |  |  |  |  |
| 137 | 47 | [Download](137/dataset.zip) |  |  |  |  |  |  |  |  |
| 138 | 16 | [Download](138/dataset.zip) |  |  |  |  |  |  |  |  |
| 139 | 12 | [Download](139/dataset.zip) |  |  |  |  |  |  |  |  |
| 140 | 12 | [Download](140/dataset.zip) |  |  |  |  |  |  |  |  |
| 141 | 19 | [Download](141/dataset.zip) |  |  |  |  |  |  |  |  |
| 142 | 423 | [Download](142/dataset.zip) |  |  |  |  |  |  |  |  |
| 143 | 20 | [Download](143/dataset.zip) |  |  |  |  |  |  |  |  |
| 144 | 15 | [Download](144/dataset.zip) |  |  |  |  |  |  |  |  |
| 145 | 154 | [Download](145/dataset.zip) |  |  |  |  |  |  |  |  |
| 146 | 53 | [Download](146/dataset.zip) |  |  |  |  |  |  |  |  |
| 147 | 14 | [Download](147/dataset.zip) |  |  |  |  |  |  |  |  |
| 148 | 13 | [Download](148/dataset.zip) |  |  |  |  |  |  |  |  |
| 149 | 115 | [Download](149/dataset.zip) |  |  |  |  |  |  |  |  |
| 150 | 35 | [Download](150/dataset.zip) |  |  |  |  |  |  |  |  |
| 151 | 41 | [Download](151/dataset.zip) |  |  |  |  |  |  |  |  |
| 152 | 12 | [Download](152/dataset.zip) |  |  |  |  |  |  |  |  |
| 153 | 17 | [Download](153/dataset.zip) |  |  |  |  |  |  |  |  |
| 154 | 13 | [Download](154/dataset.zip) |  |  |  |  |  |  |  |  |
| 155 | 14 | [Download](155/dataset.zip) |  |  |  |  |  |  |  |  |
| 156 | 88 | [Download](156/dataset.zip) |  |  |  |  |  |  |  |  |
| 157 | 13 | [Download](157/dataset.zip) |  |  |  |  |  |  |  |  |
| 158 | 9 | [Download](158/dataset.zip) |  |  |  |  |  |  |  |  |
| 159 | 13 | [Download](159/dataset.zip) |  |  |  |  |  |  |  |  |
| 160 | 6 | [Download](160/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 161 | 7 | [Download](161/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 162 | 67 | [Download](162/dataset.zip) |  |  |  |  |  |  |  |  |
| 163 | 5 | [Download](163/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 385 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/toarukagakunorailgun
|
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] |
2023-09-16T10:24:48+00:00
|
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
|
2023-09-29T07:19:15+00:00
|
[] |
[] |
TAGS
#size_categories-10K<n<100K #license-mit #art #region-us
|
Bangumi Image Base of Toaru Kagaku No Railgun
=============================================
This is the image base of bangumi Toaru Kagaku no Railgun, we detected 165 characters, 18219 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-10K<n<100K #license-mit #art #region-us \n"
] |
a30cd4b714b2888cc978d6e63536cc64a1700799
|
# NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
[](https://opensource.org/licenses/MIT)<img src="demo/Pytorch_logo.png" width="10%">
This repository is the pytorch implementation of our paper:
<a href="https://www.tri.global/" target="_blank">
<img align="right" src="demo/tri-logo.png" width="25%"/>
</a>
**NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes**<br>
[__***Muhammad Zubair Irshad***__](https://zubairirshad.com), [Sergey Zakharov](https://zakharos.github.io/), [Katherine Liu](https://www.thekatherineliu.com/), [Vitor Guizilini](https://www.linkedin.com/in/vitorguizilini), [Thomas Kollar](http://www.tkollar.com/site/), [Adrien Gaidon](https://adriengaidon.com/), [Zsolt Kira](https://faculty.cc.gatech.edu/~zk15/), [Rares Ambrus](https://www.tri.global/about-us/dr-rares-ambrus) <br>
International Conference on Computer Vision (ICCV), 2023<br>
[[Project Page](https://zubair-irshad.github.io/projects/neo360.html)] [[arXiv](https://arxiv.org/abs/2308.12967)] [[PDF](https://arxiv.org/pdf/2308.12967.pdf)] [[Video](https://youtu.be/avmylyL_V8c?si=eeTPhl0xJxM3fSF7)]
<p align="center">
<img src="demo/NEO_Website_1.jpg" width="100%">
</p>
<p align="center">
<img src="demo/NEO_Architecture.JPG" width="100%">
</p>
### Code Coming Soon!
## 📊 Dataset
### NERDS 360 Multi-View dataset for Outdoor Scenes
NeRDS 360: "NeRF for Reconstruction, Decomposition and Scene Synthesis of 360° outdoor scenes” dataset comprising 75 unbounded scenes with full multi-view annotations and diverse scenes for generalizable NeRF training and evaluation.
<p align="center">
<img src="demo/github_dataset.gif" width="100%">
</p>
#### Download the dataset:
* [NERDS360 Training Set](https://tri-ml-public.s3.amazonaws.com/github/neo360/datasets/PDMultiObjv6.tar.gz) - 75 Scenes (19.5 GB)
* [NERDS360 Test Set](https://tri-ml-public.s3.amazonaws.com/github/neo360/datasets/PD_v6_test.tar.gz) - 5 Scenes (2.1 GB)
#### Visualizing the dataset (Coming Soon):
We will release our visualization scripts to generate visualizations like below i.e. plot accumulated pointclouds, multi-view camera annotations etc.
<p align="center">
<img src="demo/cameras.gif" width="100%">
</p>
## Citation
If you find this repository or our NERDS 360 dataset useful, please consider citing:
```
@inproceedings{irshad2023neo360,
title={NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes},
author={Muhammad Zubair Irshad and Sergey Zakharov and Katherine Liu and Vitor Guizilini and Thomas Kollar and Adrien Gaidon and Zsolt Kira and Rares Ambrus},
journal={Interntaional Conference on Computer Vision (ICCV)},
year={2023},
url={https://arxiv.org/abs/2308.12967},
}
```
|
mirshad7/NERDS360
|
[
"license:cc-by-nc-4.0",
"arxiv:2308.12967",
"region:us"
] |
2023-09-16T10:26:47+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-09-18T14:35:29+00:00
|
[
"2308.12967"
] |
[] |
TAGS
#license-cc-by-nc-4.0 #arxiv-2308.12967 #region-us
|
# NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
![License: MIT](URL src="demo/Pytorch_logo.png" width="10%">
This repository is the pytorch implementation of our paper:
<a href="URL target="_blank">
<img align="right" src="demo/URL" width="25%"/>
</a>
NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes<br>
__*Muhammad Zubair Irshad*__, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus <br>
International Conference on Computer Vision (ICCV), 2023<br>
[Project Page] [arXiv] [PDF] [Video]
<p align="center">
<img src="demo/NEO_Website_1.jpg" width="100%">
</p>
<p align="center">
<img src="demo/NEO_Architecture.JPG" width="100%">
</p>
### Code Coming Soon!
## Dataset
### NERDS 360 Multi-View dataset for Outdoor Scenes
NeRDS 360: "NeRF for Reconstruction, Decomposition and Scene Synthesis of 360° outdoor scenes” dataset comprising 75 unbounded scenes with full multi-view annotations and diverse scenes for generalizable NeRF training and evaluation.
<p align="center">
<img src="demo/github_dataset.gif" width="100%">
</p>
#### Download the dataset:
* NERDS360 Training Set - 75 Scenes (19.5 GB)
* NERDS360 Test Set - 5 Scenes (2.1 GB)
#### Visualizing the dataset (Coming Soon):
We will release our visualization scripts to generate visualizations like below i.e. plot accumulated pointclouds, multi-view camera annotations etc.
<p align="center">
<img src="demo/URL" width="100%">
</p>
If you find this repository or our NERDS 360 dataset useful, please consider citing:
|
[
"# NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes\n\n![License: MIT](URL src=\"demo/Pytorch_logo.png\" width=\"10%\">\n\nThis repository is the pytorch implementation of our paper:\n<a href=\"URL target=\"_blank\">\n <img align=\"right\" src=\"demo/URL\" width=\"25%\"/>\n</a>\n\nNEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes<br>\n__*Muhammad Zubair Irshad*__, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus <br>\nInternational Conference on Computer Vision (ICCV), 2023<br>\n\n[Project Page] [arXiv] [PDF] [Video]\n\n\n<p align=\"center\">\n<img src=\"demo/NEO_Website_1.jpg\" width=\"100%\">\n</p>\n\n<p align=\"center\">\n<img src=\"demo/NEO_Architecture.JPG\" width=\"100%\">\n</p>",
"### Code Coming Soon!",
"## Dataset",
"### NERDS 360 Multi-View dataset for Outdoor Scenes\n\nNeRDS 360: \"NeRF for Reconstruction, Decomposition and Scene Synthesis of 360° outdoor scenes” dataset comprising 75 unbounded scenes with full multi-view annotations and diverse scenes for generalizable NeRF training and evaluation.\n\n<p align=\"center\">\n<img src=\"demo/github_dataset.gif\" width=\"100%\">\n</p>",
"#### Download the dataset:\n* NERDS360 Training Set - 75 Scenes (19.5 GB)\n* NERDS360 Test Set - 5 Scenes (2.1 GB)",
"#### Visualizing the dataset (Coming Soon):\nWe will release our visualization scripts to generate visualizations like below i.e. plot accumulated pointclouds, multi-view camera annotations etc. \n\n<p align=\"center\">\n<img src=\"demo/URL\" width=\"100%\">\n</p>\n\nIf you find this repository or our NERDS 360 dataset useful, please consider citing:"
] |
[
"TAGS\n#license-cc-by-nc-4.0 #arxiv-2308.12967 #region-us \n",
"# NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes\n\n![License: MIT](URL src=\"demo/Pytorch_logo.png\" width=\"10%\">\n\nThis repository is the pytorch implementation of our paper:\n<a href=\"URL target=\"_blank\">\n <img align=\"right\" src=\"demo/URL\" width=\"25%\"/>\n</a>\n\nNEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes<br>\n__*Muhammad Zubair Irshad*__, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus <br>\nInternational Conference on Computer Vision (ICCV), 2023<br>\n\n[Project Page] [arXiv] [PDF] [Video]\n\n\n<p align=\"center\">\n<img src=\"demo/NEO_Website_1.jpg\" width=\"100%\">\n</p>\n\n<p align=\"center\">\n<img src=\"demo/NEO_Architecture.JPG\" width=\"100%\">\n</p>",
"### Code Coming Soon!",
"## Dataset",
"### NERDS 360 Multi-View dataset for Outdoor Scenes\n\nNeRDS 360: \"NeRF for Reconstruction, Decomposition and Scene Synthesis of 360° outdoor scenes” dataset comprising 75 unbounded scenes with full multi-view annotations and diverse scenes for generalizable NeRF training and evaluation.\n\n<p align=\"center\">\n<img src=\"demo/github_dataset.gif\" width=\"100%\">\n</p>",
"#### Download the dataset:\n* NERDS360 Training Set - 75 Scenes (19.5 GB)\n* NERDS360 Test Set - 5 Scenes (2.1 GB)",
"#### Visualizing the dataset (Coming Soon):\nWe will release our visualization scripts to generate visualizations like below i.e. plot accumulated pointclouds, multi-view camera annotations etc. \n\n<p align=\"center\">\n<img src=\"demo/URL\" width=\"100%\">\n</p>\n\nIf you find this repository or our NERDS 360 dataset useful, please consider citing:"
] |
[
25,
262,
8,
3,
106,
38,
96
] |
[
"passage: TAGS\n#license-cc-by-nc-4.0 #arxiv-2308.12967 #region-us \n# NEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes\n\n![License: MIT](URL src=\"demo/Pytorch_logo.png\" width=\"10%\">\n\nThis repository is the pytorch implementation of our paper:\n<a href=\"URL target=\"_blank\">\n <img align=\"right\" src=\"demo/URL\" width=\"25%\"/>\n</a>\n\nNEO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes<br>\n__*Muhammad Zubair Irshad*__, Sergey Zakharov, Katherine Liu, Vitor Guizilini, Thomas Kollar, Adrien Gaidon, Zsolt Kira, Rares Ambrus <br>\nInternational Conference on Computer Vision (ICCV), 2023<br>\n\n[Project Page] [arXiv] [PDF] [Video]\n\n\n<p align=\"center\">\n<img src=\"demo/NEO_Website_1.jpg\" width=\"100%\">\n</p>\n\n<p align=\"center\">\n<img src=\"demo/NEO_Architecture.JPG\" width=\"100%\">\n</p>### Code Coming Soon!## Dataset### NERDS 360 Multi-View dataset for Outdoor Scenes\n\nNeRDS 360: \"NeRF for Reconstruction, Decomposition and Scene Synthesis of 360° outdoor scenes” dataset comprising 75 unbounded scenes with full multi-view annotations and diverse scenes for generalizable NeRF training and evaluation.\n\n<p align=\"center\">\n<img src=\"demo/github_dataset.gif\" width=\"100%\">\n</p>#### Download the dataset:\n* NERDS360 Training Set - 75 Scenes (19.5 GB)\n* NERDS360 Test Set - 5 Scenes (2.1 GB)"
] |
0e531ca48ea85b2a8178148c95e70487ac8054e5
|
1. Dataset Description:
Purpose: The dataset aims to train models to solve math word problems, providing step-by-step calculations with expected output.
2. Data Collection and Processing:
Source: GPT 4
Processing: The dataset is structured with math problems given as "instruction" and their step-by-step solutions as "output".
3. Data Attributes:
instruction (String): A textual representation of the math word problem.
output (String): Detailed step-by-step calculations leading to the solution. It appears that placeholders like <<>> are used to indicate calculations, and "####" is used to present the final answer.
4. Sample Data Point:
{
"instruction": "Rohit is saving money for a new wallet which costs $100. Rohit has only half of the money he needs. His parents decided to give him $15 for that purpose, and his grandparents twice as much as his parents. How much more money does Rohit need to buy the wallet?",
"output": "Rohit has only 100 * 0.5 = $<<100*0.5=50>>50.\nRohit's grandparents gave him 15 * 2 = $<<15*2=30>>30.\nIn total, Rohit needs 100 - 50 - 30 - 15 = $<<100-50-30-15=5>>5 more.\n#### 5"
}
5. Potential Uses:
Training models to comprehend and solve math word problems.
Evaluating models' ability to perform mathematical operations based on textual context.
6. Potential Biases, Ethical Considerations, and Limitations:
Scope: The provided samples seem to revolve around basic arithmetic. If this pattern holds for the entire dataset, it might not cover more complex math problems or higher-level mathematics.
Simplicity: Some real-world math problems might require more advanced problem-solving techniques than simple arithmetic.
7. Dataset Maintenance and Updates:
will try to keep in loop
offers several merits for LLMs:
1. Structured Problem Solving:
Merit: The dataset encourages structured problem-solving. Each solution is broken down into steps, reinforcing the idea that problems often need a sequential approach.
Learning: Transformers excel at learning sequences and patterns. By observing structured step-by-step solutions, they can learn the logical progression of tackling mathematical problems.
2. Varied Expression:
Merit: The dataset offers multiple ways to solve the same problem, emphasizing that there's often more than one way to approach a solution.
Learning: This can enhance the generalization capabilities of transformers. They can learn that while different paths may be taken, they can still lead to the same solution. This reduces overfitting to a singular method of problem-solving.
3. Explicit Arithmetic Computations:
Merit: The use of placeholders like <<>> clearly indicates where arithmetic operations occur, making it explicit what computations are being performed.
Learning: Transformers can utilize such explicit markers to better identify and learn arithmetic patterns, focusing on these sections for numeric computations.
4. Clear Answer Indication:
Merit: The "####" tag provides a clear indication of the final answer, differentiating it from the intermediate steps.
Learning: This can help the model discern between intermediate computations and final outcomes. When queried, the model can then prioritize presenting such clear answers.
5. Contextual Comprehension:
Merit: Math problems are embedded in worded instructions, demanding not just mathematical ability but also linguistic comprehension.
Learning: Transformers can fine-tune their contextual understanding by discerning relevant information from word problems, integrating their language model training with arithmetic capabilities.
In essence, the dataset's design provides a comprehensive approach to training transformers on mathematical problem-solving, offering both linguistic comprehension and arithmetic execution.
|
knowrohit07/ArithmeLogic
|
[
"license:openrail",
"region:us"
] |
2023-09-16T10:28:00+00:00
|
{"license": "openrail"}
|
2023-09-16T10:53:47+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
1. Dataset Description:
Purpose: The dataset aims to train models to solve math word problems, providing step-by-step calculations with expected output.
2. Data Collection and Processing:
Source: GPT 4
Processing: The dataset is structured with math problems given as "instruction" and their step-by-step solutions as "output".
3. Data Attributes:
instruction (String): A textual representation of the math word problem.
output (String): Detailed step-by-step calculations leading to the solution. It appears that placeholders like <<>> are used to indicate calculations, and "####" is used to present the final answer.
4. Sample Data Point:
{
"instruction": "Rohit is saving money for a new wallet which costs $100. Rohit has only half of the money he needs. His parents decided to give him $15 for that purpose, and his grandparents twice as much as his parents. How much more money does Rohit need to buy the wallet?",
"output": "Rohit has only 100 * 0.5 = $<<100*0.5=50>>50.\nRohit's grandparents gave him 15 * 2 = $<<15*2=30>>30.\nIn total, Rohit needs 100 - 50 - 30 - 15 = $<<100-50-30-15=5>>5 more.\n#### 5"
}
5. Potential Uses:
Training models to comprehend and solve math word problems.
Evaluating models' ability to perform mathematical operations based on textual context.
6. Potential Biases, Ethical Considerations, and Limitations:
Scope: The provided samples seem to revolve around basic arithmetic. If this pattern holds for the entire dataset, it might not cover more complex math problems or higher-level mathematics.
Simplicity: Some real-world math problems might require more advanced problem-solving techniques than simple arithmetic.
7. Dataset Maintenance and Updates:
will try to keep in loop
offers several merits for LLMs:
1. Structured Problem Solving:
Merit: The dataset encourages structured problem-solving. Each solution is broken down into steps, reinforcing the idea that problems often need a sequential approach.
Learning: Transformers excel at learning sequences and patterns. By observing structured step-by-step solutions, they can learn the logical progression of tackling mathematical problems.
2. Varied Expression:
Merit: The dataset offers multiple ways to solve the same problem, emphasizing that there's often more than one way to approach a solution.
Learning: This can enhance the generalization capabilities of transformers. They can learn that while different paths may be taken, they can still lead to the same solution. This reduces overfitting to a singular method of problem-solving.
3. Explicit Arithmetic Computations:
Merit: The use of placeholders like <<>> clearly indicates where arithmetic operations occur, making it explicit what computations are being performed.
Learning: Transformers can utilize such explicit markers to better identify and learn arithmetic patterns, focusing on these sections for numeric computations.
4. Clear Answer Indication:
Merit: The "####" tag provides a clear indication of the final answer, differentiating it from the intermediate steps.
Learning: This can help the model discern between intermediate computations and final outcomes. When queried, the model can then prioritize presenting such clear answers.
5. Contextual Comprehension:
Merit: Math problems are embedded in worded instructions, demanding not just mathematical ability but also linguistic comprehension.
Learning: Transformers can fine-tune their contextual understanding by discerning relevant information from word problems, integrating their language model training with arithmetic capabilities.
In essence, the dataset's design provides a comprehensive approach to training transformers on mathematical problem-solving, offering both linguistic comprehension and arithmetic execution.
|
[
"#### 5\"\n }\n \n5. Potential Uses:\n\nTraining models to comprehend and solve math word problems.\nEvaluating models' ability to perform mathematical operations based on textual context.\n\n6. Potential Biases, Ethical Considerations, and Limitations:\n\nScope: The provided samples seem to revolve around basic arithmetic. If this pattern holds for the entire dataset, it might not cover more complex math problems or higher-level mathematics.\nSimplicity: Some real-world math problems might require more advanced problem-solving techniques than simple arithmetic.\n\n7. Dataset Maintenance and Updates:\n\nwill try to keep in loop\n\n\n\noffers several merits for LLMs:\n\n1. Structured Problem Solving:\n\nMerit: The dataset encourages structured problem-solving. Each solution is broken down into steps, reinforcing the idea that problems often need a sequential approach.\nLearning: Transformers excel at learning sequences and patterns. By observing structured step-by-step solutions, they can learn the logical progression of tackling mathematical problems.\n\n2. Varied Expression:\n\nMerit: The dataset offers multiple ways to solve the same problem, emphasizing that there's often more than one way to approach a solution.\nLearning: This can enhance the generalization capabilities of transformers. They can learn that while different paths may be taken, they can still lead to the same solution. This reduces overfitting to a singular method of problem-solving.\n\n3. Explicit Arithmetic Computations:\n\nMerit: The use of placeholders like <<>> clearly indicates where arithmetic operations occur, making it explicit what computations are being performed.\nLearning: Transformers can utilize such explicit markers to better identify and learn arithmetic patterns, focusing on these sections for numeric computations.\n\n4. Clear Answer Indication:\n\nMerit: The \"####\" tag provides a clear indication of the final answer, differentiating it from the intermediate steps.\nLearning: This can help the model discern between intermediate computations and final outcomes. When queried, the model can then prioritize presenting such clear answers.\n\n5. Contextual Comprehension:\n\nMerit: Math problems are embedded in worded instructions, demanding not just mathematical ability but also linguistic comprehension.\nLearning: Transformers can fine-tune their contextual understanding by discerning relevant information from word problems, integrating their language model training with arithmetic capabilities.\n\n\n\nIn essence, the dataset's design provides a comprehensive approach to training transformers on mathematical problem-solving, offering both linguistic comprehension and arithmetic execution."
] |
[
"TAGS\n#license-openrail #region-us \n",
"#### 5\"\n }\n \n5. Potential Uses:\n\nTraining models to comprehend and solve math word problems.\nEvaluating models' ability to perform mathematical operations based on textual context.\n\n6. Potential Biases, Ethical Considerations, and Limitations:\n\nScope: The provided samples seem to revolve around basic arithmetic. If this pattern holds for the entire dataset, it might not cover more complex math problems or higher-level mathematics.\nSimplicity: Some real-world math problems might require more advanced problem-solving techniques than simple arithmetic.\n\n7. Dataset Maintenance and Updates:\n\nwill try to keep in loop\n\n\n\noffers several merits for LLMs:\n\n1. Structured Problem Solving:\n\nMerit: The dataset encourages structured problem-solving. Each solution is broken down into steps, reinforcing the idea that problems often need a sequential approach.\nLearning: Transformers excel at learning sequences and patterns. By observing structured step-by-step solutions, they can learn the logical progression of tackling mathematical problems.\n\n2. Varied Expression:\n\nMerit: The dataset offers multiple ways to solve the same problem, emphasizing that there's often more than one way to approach a solution.\nLearning: This can enhance the generalization capabilities of transformers. They can learn that while different paths may be taken, they can still lead to the same solution. This reduces overfitting to a singular method of problem-solving.\n\n3. Explicit Arithmetic Computations:\n\nMerit: The use of placeholders like <<>> clearly indicates where arithmetic operations occur, making it explicit what computations are being performed.\nLearning: Transformers can utilize such explicit markers to better identify and learn arithmetic patterns, focusing on these sections for numeric computations.\n\n4. Clear Answer Indication:\n\nMerit: The \"####\" tag provides a clear indication of the final answer, differentiating it from the intermediate steps.\nLearning: This can help the model discern between intermediate computations and final outcomes. When queried, the model can then prioritize presenting such clear answers.\n\n5. Contextual Comprehension:\n\nMerit: Math problems are embedded in worded instructions, demanding not just mathematical ability but also linguistic comprehension.\nLearning: Transformers can fine-tune their contextual understanding by discerning relevant information from word problems, integrating their language model training with arithmetic capabilities.\n\n\n\nIn essence, the dataset's design provides a comprehensive approach to training transformers on mathematical problem-solving, offering both linguistic comprehension and arithmetic execution."
] |
[
12,
591
] |
[
"passage: TAGS\n#license-openrail #region-us \n"
] |
73a1ebb41adfbfbaea2344d71dfb75c6991fdf55
|
### 🩺 Description:
The knowrohit07/know_medical_dialogues dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.
### 🎯 Intended Use:
This dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.
❗ Limitations:
While this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.
📌 Data Source:
Conversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.
📋 Collection Methodology:
The data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.
### Data Fields:
description: A brief summary of the conversation's context.
utterances: Contains a sequential list of statements between the patient and doctor.
### 🌟 Advantages of the Dataset:
Broad Spectrum: The dataset encompasses a wide array of medical queries and advice, making it valuable for general medical conversational AI.
Diverse Interactions: It captures everything from symptom queries to post-care instructions.
Training Potential for LLMs: Specifically tailored for fine-tuning LLMs for medical conversations, enhancing the resultant model's capability in this domain.
⚖️ Ethical and Impact Considerations:
Positive Impact: Utilizing LLMs trained on this dataset can be invaluable for healthcare professionals, especially in regions with limited medical datasets. When deployed on affordable local devices, doctors can leverage an AI-assisted tool, enhancing their consultation and decision-making processes.
Potential Risks: There's an inherent risk of the model providing guidance that may not match the latest medical guidelines or specific patient requirements. It's crucial to clarify to users that outputs from the LLM should complement professional medical opinions.
Recommendation: Encourage healthcare professionals to use this tool as an initial point of reference and not as the primary foundation for medical decisions.
|
knowrohit07/know_medical_dialogues
|
[
"license:openrail",
"region:us"
] |
2023-09-16T10:40:38+00:00
|
{"license": "openrail"}
|
2023-10-12T09:50:50+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
### Description:
The knowrohit07/know_medical_dialogues dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.
### Intended Use:
This dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.
Limitations:
While this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.
Data Source:
Conversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.
Collection Methodology:
The data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.
### Data Fields:
description: A brief summary of the conversation's context.
utterances: Contains a sequential list of statements between the patient and doctor.
### Advantages of the Dataset:
Broad Spectrum: The dataset encompasses a wide array of medical queries and advice, making it valuable for general medical conversational AI.
Diverse Interactions: It captures everything from symptom queries to post-care instructions.
Training Potential for LLMs: Specifically tailored for fine-tuning LLMs for medical conversations, enhancing the resultant model's capability in this domain.
️ Ethical and Impact Considerations:
Positive Impact: Utilizing LLMs trained on this dataset can be invaluable for healthcare professionals, especially in regions with limited medical datasets. When deployed on affordable local devices, doctors can leverage an AI-assisted tool, enhancing their consultation and decision-making processes.
Potential Risks: There's an inherent risk of the model providing guidance that may not match the latest medical guidelines or specific patient requirements. It's crucial to clarify to users that outputs from the LLM should complement professional medical opinions.
Recommendation: Encourage healthcare professionals to use this tool as an initial point of reference and not as the primary foundation for medical decisions.
|
[
"### Description:\nThe knowrohit07/know_medical_dialogues dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.",
"### Intended Use:\nThis dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.\n\n Limitations:\nWhile this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.\n\n Data Source:\nConversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.\n\n Collection Methodology:\nThe data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.",
"### Data Fields:\ndescription: A brief summary of the conversation's context.\nutterances: Contains a sequential list of statements between the patient and doctor.",
"### Advantages of the Dataset:\nBroad Spectrum: The dataset encompasses a wide array of medical queries and advice, making it valuable for general medical conversational AI.\n\nDiverse Interactions: It captures everything from symptom queries to post-care instructions.\n\nTraining Potential for LLMs: Specifically tailored for fine-tuning LLMs for medical conversations, enhancing the resultant model's capability in this domain.\n\n️ Ethical and Impact Considerations:\nPositive Impact: Utilizing LLMs trained on this dataset can be invaluable for healthcare professionals, especially in regions with limited medical datasets. When deployed on affordable local devices, doctors can leverage an AI-assisted tool, enhancing their consultation and decision-making processes.\n\nPotential Risks: There's an inherent risk of the model providing guidance that may not match the latest medical guidelines or specific patient requirements. It's crucial to clarify to users that outputs from the LLM should complement professional medical opinions.\n\nRecommendation: Encourage healthcare professionals to use this tool as an initial point of reference and not as the primary foundation for medical decisions."
] |
[
"TAGS\n#license-openrail #region-us \n",
"### Description:\nThe knowrohit07/know_medical_dialogues dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.",
"### Intended Use:\nThis dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.\n\n Limitations:\nWhile this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.\n\n Data Source:\nConversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.\n\n Collection Methodology:\nThe data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.",
"### Data Fields:\ndescription: A brief summary of the conversation's context.\nutterances: Contains a sequential list of statements between the patient and doctor.",
"### Advantages of the Dataset:\nBroad Spectrum: The dataset encompasses a wide array of medical queries and advice, making it valuable for general medical conversational AI.\n\nDiverse Interactions: It captures everything from symptom queries to post-care instructions.\n\nTraining Potential for LLMs: Specifically tailored for fine-tuning LLMs for medical conversations, enhancing the resultant model's capability in this domain.\n\n️ Ethical and Impact Considerations:\nPositive Impact: Utilizing LLMs trained on this dataset can be invaluable for healthcare professionals, especially in regions with limited medical datasets. When deployed on affordable local devices, doctors can leverage an AI-assisted tool, enhancing their consultation and decision-making processes.\n\nPotential Risks: There's an inherent risk of the model providing guidance that may not match the latest medical guidelines or specific patient requirements. It's crucial to clarify to users that outputs from the LLM should complement professional medical opinions.\n\nRecommendation: Encourage healthcare professionals to use this tool as an initial point of reference and not as the primary foundation for medical decisions."
] |
[
12,
69,
191,
37,
264
] |
[
"passage: TAGS\n#license-openrail #region-us \n### Description:\nThe knowrohit07/know_medical_dialogues dataset is a collection of conversational exchanges between patients and doctors on various medical topics. It aims to capture the intricacies, uncertainties, and questions posed by individuals regarding their health and the medical guidance provided in response.### Intended Use:\nThis dataset is crafted for training Large Language Models (LLMs) with a focus on understanding and generating medically-informed dialogue. It's ideal for LLM applications aiming to provide medical information or insights, especially for scenarios with limited access to healthcare resources.\n\n Limitations:\nWhile this dataset includes diverse interactions, it doesn't cover every medical scenario. Models trained on this data should be viewed as an additional resource, not a substitute for professional medical consultation.\n\n Data Source:\nConversational seed tasks or exchanges were collected from anonymized patient-doctor interactions and synthetically made using GPT4.\n\n Collection Methodology:\nThe data was meticulously curated to ensure no personally identifiable information remained. All conversations are representative of general concerns and advice, without specific case details.### Data Fields:\ndescription: A brief summary of the conversation's context.\nutterances: Contains a sequential list of statements between the patient and doctor."
] |
a80fd9ea3972a2e2366d94d4b872ed067a49f7d0
|
# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu](https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T20:24:24.496556](https://huggingface.co/datasets/open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu/blob/main/results_2023-10-27T20-24-24.496556.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0017827181208053692,
"em_stderr": 0.00043200973460388544,
"f1": 0.06179635067114092,
"f1_stderr": 0.0013805757246305051,
"acc": 0.4386580563472483,
"acc_stderr": 0.010104640124560183
},
"harness|drop|3": {
"em": 0.0017827181208053692,
"em_stderr": 0.00043200973460388544,
"f1": 0.06179635067114092,
"f1_stderr": 0.0013805757246305051
},
"harness|gsm8k|5": {
"acc": 0.10462471569370735,
"acc_stderr": 0.00843066808202928
},
"harness|winogrande|5": {
"acc": 0.7726913970007893,
"acc_stderr": 0.011778612167091087
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu
|
[
"region:us"
] |
2023-09-16T10:42:03+00:00
|
{"pretty_name": "Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu", "dataset_summary": "Dataset automatically created during the evaluation run of model [NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu](https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T20:24:24.496556](https://huggingface.co/datasets/open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu/blob/main/results_2023-10-27T20-24-24.496556.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.00043200973460388544,\n \"f1\": 0.06179635067114092,\n \"f1_stderr\": 0.0013805757246305051,\n \"acc\": 0.4386580563472483,\n \"acc_stderr\": 0.010104640124560183\n },\n \"harness|drop|3\": {\n \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.00043200973460388544,\n \"f1\": 0.06179635067114092,\n \"f1_stderr\": 0.0013805757246305051\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10462471569370735,\n \"acc_stderr\": 0.00843066808202928\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7726913970007893,\n \"acc_stderr\": 0.011778612167091087\n }\n}\n```", "repo_url": "https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_16T11_41_48.010953", "path": ["**/details_harness|arc:challenge|25_2023-09-16T11-41-48.010953.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-16T11-41-48.010953.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T20_24_24.496556", "path": ["**/details_harness|drop|3_2023-10-27T20-24-24.496556.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T20-24-24.496556.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T20_24_24.496556", "path": ["**/details_harness|gsm8k|5_2023-10-27T20-24-24.496556.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T20-24-24.496556.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_16T11_41_48.010953", "path": ["**/details_harness|hellaswag|10_2023-09-16T11-41-48.010953.parquet"]}, {"split": "latest", "path": 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|
2023-10-27T19:24:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-27T20:24:24.496556(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T20:24:24.496556(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-27T20:24:24.496556(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-QLoRA-multigpu on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-27T20:24:24.496556(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
c21c84c4b0cc680e40b81a8460681cae32413b14
|
# Dataset Card for Evaluation run of Brouz/Slerpeno
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Brouz/Slerpeno
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Brouz/Slerpeno](https://huggingface.co/Brouz/Slerpeno) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Brouz__Slerpeno",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T06:06:21.749347](https://huggingface.co/datasets/open-llm-leaderboard/details_Brouz__Slerpeno/blob/main/results_2023-10-25T06-06-21.749347.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.2197986577181208,
"em_stderr": 0.004240875430094279,
"f1": 0.28002936241610793,
"f1_stderr": 0.004218038263995875,
"acc": 0.4445521199780035,
"acc_stderr": 0.010523215145062624
},
"harness|drop|3": {
"em": 0.2197986577181208,
"em_stderr": 0.004240875430094279,
"f1": 0.28002936241610793,
"f1_stderr": 0.004218038263995875
},
"harness|gsm8k|5": {
"acc": 0.12509476876421532,
"acc_stderr": 0.009112601439849625
},
"harness|winogrande|5": {
"acc": 0.7640094711917916,
"acc_stderr": 0.011933828850275625
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Brouz__Slerpeno
|
[
"region:us"
] |
2023-09-16T10:43:21+00:00
|
{"pretty_name": "Evaluation run of Brouz/Slerpeno", "dataset_summary": "Dataset automatically created during the evaluation run of model [Brouz/Slerpeno](https://huggingface.co/Brouz/Slerpeno) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Brouz__Slerpeno\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T06:06:21.749347](https://huggingface.co/datasets/open-llm-leaderboard/details_Brouz__Slerpeno/blob/main/results_2023-10-25T06-06-21.749347.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.2197986577181208,\n \"em_stderr\": 0.004240875430094279,\n \"f1\": 0.28002936241610793,\n \"f1_stderr\": 0.004218038263995875,\n \"acc\": 0.4445521199780035,\n \"acc_stderr\": 0.010523215145062624\n },\n \"harness|drop|3\": {\n \"em\": 0.2197986577181208,\n \"em_stderr\": 0.004240875430094279,\n \"f1\": 0.28002936241610793,\n \"f1_stderr\": 0.004218038263995875\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12509476876421532,\n \"acc_stderr\": 0.009112601439849625\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7640094711917916,\n \"acc_stderr\": 0.011933828850275625\n }\n}\n```", "repo_url": "https://huggingface.co/Brouz/Slerpeno", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_16T11_43_04.528052", "path": ["**/details_harness|arc:challenge|25_2023-09-16T11-43-04.528052.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-16T11-43-04.528052.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T06_06_21.749347", "path": ["**/details_harness|drop|3_2023-10-25T06-06-21.749347.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T06-06-21.749347.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T06_06_21.749347", "path": ["**/details_harness|gsm8k|5_2023-10-25T06-06-21.749347.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T06-06-21.749347.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_16T11_43_04.528052", "path": ["**/details_harness|hellaswag|10_2023-09-16T11-43-04.528052.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-16T11-43-04.528052.parquet"]}]}, 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|
2023-10-25T05:06:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Brouz/Slerpeno
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Brouz/Slerpeno on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T06:06:21.749347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Brouz/Slerpeno",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Brouz/Slerpeno on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T06:06:21.749347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Brouz/Slerpeno",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Brouz/Slerpeno on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T06:06:21.749347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Brouz/Slerpeno## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Brouz/Slerpeno on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T06:06:21.749347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
767468f04995b65b54d82cd920eb7d128f58f870
|
# Dataset Card for "cs323_densepred_seg256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
shariqfarooq/cs323_densepred_seg256
|
[
"region:us"
] |
2023-09-16T11:02:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 187512341.0, "num_examples": 1464}, {"name": "val", "num_bytes": 187805177.75, "num_examples": 1449}], "download_size": 375496804, "dataset_size": 375317518.75}}
|
2023-09-16T11:07:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cs323_densepred_seg256"
More Information needed
|
[
"# Dataset Card for \"cs323_densepred_seg256\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cs323_densepred_seg256\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cs323_densepred_seg256\"\n\nMore Information needed"
] |
6cc3327ac8ad369d24c632e36c6becdf0f80812c
|
# Dataset Card for "707d50d0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-muse256-muse512-wuerst-sdv15/707d50d0
|
[
"region:us"
] |
2023-09-16T11:04:33+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 150, "num_examples": 10}], "download_size": 1285, "dataset_size": 150}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T11:04:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "707d50d0"
More Information needed
|
[
"# Dataset Card for \"707d50d0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"707d50d0\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"707d50d0\"\n\nMore Information needed"
] |
462370fb643bd4a478ef93e6e1fa24cf34c1edc4
|
# Dataset of mochizuki_anna/望月杏奈/모치즈키안나 (THE iDOLM@STER: Million Live!)
This is the dataset of mochizuki_anna/望月杏奈/모치즈키안나 (THE iDOLM@STER: Million Live!), containing 494 images and their tags.
The core tags of this character are `long_hair, purple_hair, ahoge, hair_between_eyes, bangs, green_eyes, aqua_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 | 494 | 581.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mochizuki_anna_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 494 | 345.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mochizuki_anna_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1155 | 737.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mochizuki_anna_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 494 | 512.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mochizuki_anna_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1155 | 1.01 GiB | [Download](https://huggingface.co/datasets/CyberHarem/mochizuki_anna_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mochizuki_anna_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 20 |  |  |  |  |  | 1girl, solo, blush, open_mouth, looking_at_viewer, skirt, :d, hoodie, jewelry, thighhighs, white_background |
| 1 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, blush, double_v, headphones, open_mouth, rabbit_ears, white_background, detached_sleeves, simple_background, striped, thighhighs, asymmetrical_legwear, one_eye_closed, skirt |
| 2 | 5 |  |  |  |  |  | 1girl, blush, closed_mouth, simple_background, solo, hood_down, long_sleeves, looking_at_viewer, pink_hoodie, upper_body, white_background, smile, heart, holding, pink_jacket |
| 3 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, shirt, long_sleeves, simple_background, solo, white_background, black_bow, blush, sailor_collar, serafuku, bowtie, closed_mouth, pleated_skirt, red_jacket, collarbone, grey_skirt, smile, upper_body, very_long_hair |
| 4 | 14 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, open_mouth, penis, nipples, small_breasts, navel, sex, vaginal, spread_legs, on_back, bar_censor, female_pubic_hair, looking_at_viewer, thighhighs, blue_eyes, cum_in_pussy, pov |
| 5 | 7 |  |  |  |  |  | blue_sky, day, 1girl, blush, cloud, collarbone, looking_at_viewer, navel, outdoors, solo, water, bare_shoulders, open_mouth, small_breasts, smile, standing, bare_arms, ocean, frilled_bikini, medium_breasts, very_long_hair, wet |
| 6 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, white_gloves, bare_shoulders, open_mouth, purple_bow, purple_skirt, white_shirt, sleeveless_shirt, tilted_headwear, very_long_hair, blue_bow, fur-trimmed_gloves, blue_eyes, bowtie, mini_top_hat, sparkle, :d, ;d, blue_skirt, blurry, center_frills, holding_microphone, layered_skirt, one_eye_closed, outstretched_arm, rabbit_ears, striped, white_thighhighs |
| 7 | 15 |  |  |  |  |  | 1girl, rabbit_ears, playboy_bunny, solo, blush, bare_shoulders, detached_collar, fake_animal_ears, rabbit_tail, looking_at_viewer, wrist_cuffs, small_breasts, black_leotard, bowtie, strapless_leotard, sitting, very_long_hair, cleavage, fake_tail, fishnet_pantyhose, hairband, white_background |
| 8 | 18 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, smile, maid_headdress, bow, white_apron, enmaided, frilled_apron, puffy_short_sleeves, very_long_hair, open_mouth, black_dress, heart, long_sleeves, one_eye_closed, wrist_cuffs, closed_mouth, frilled_dress, shirt |
| 9 | 6 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blue_one-piece_swimsuit, small_breasts, collarbone, covered_navel, cowboy_shot, bare_arms, bare_shoulders, blue_hair, blush, school_swimsuit, smile, standing |
| 10 | 6 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, censored, gloved_handjob, hat, 2boys, double_handjob, flat_color, multiple_penises, breasts_out, medium_breasts, mmf_threesome, nipples, sketch |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | open_mouth | looking_at_viewer | skirt | :d | hoodie | jewelry | thighhighs | white_background | smile | double_v | headphones | rabbit_ears | detached_sleeves | simple_background | striped | asymmetrical_legwear | one_eye_closed | closed_mouth | hood_down | long_sleeves | pink_hoodie | upper_body | heart | holding | pink_jacket | shirt | black_bow | sailor_collar | serafuku | bowtie | pleated_skirt | red_jacket | collarbone | grey_skirt | very_long_hair | 1boy | hetero | solo_focus | penis | nipples | small_breasts | navel | sex | vaginal | spread_legs | on_back | bar_censor | female_pubic_hair | blue_eyes | cum_in_pussy | pov | blue_sky | day | cloud | outdoors | water | bare_shoulders | standing | bare_arms | ocean | frilled_bikini | medium_breasts | wet | white_gloves | purple_bow | purple_skirt | white_shirt | sleeveless_shirt | tilted_headwear | blue_bow | fur-trimmed_gloves | mini_top_hat | sparkle | ;d | blue_skirt | blurry | center_frills | holding_microphone | layered_skirt | outstretched_arm | white_thighhighs | playboy_bunny | detached_collar | fake_animal_ears | rabbit_tail | wrist_cuffs | black_leotard | strapless_leotard | sitting | cleavage | fake_tail | fishnet_pantyhose | hairband | maid_headdress | bow | white_apron | enmaided | frilled_apron | puffy_short_sleeves | black_dress | frilled_dress | blue_one-piece_swimsuit | covered_navel | cowboy_shot | blue_hair | school_swimsuit | censored | gloved_handjob | hat | 2boys | double_handjob | flat_color | multiple_penises | breasts_out | mmf_threesome | sketch |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:--------|:-------------|:--------------------|:--------|:-----|:---------|:----------|:-------------|:-------------------|:--------|:-----------|:-------------|:--------------|:-------------------|:--------------------|:----------|:-----------------------|:-----------------|:---------------|:------------|:---------------|:--------------|:-------------|:--------|:----------|:--------------|:--------|:------------|:----------------|:-----------|:---------|:----------------|:-------------|:-------------|:-------------|:-----------------|:-------|:---------|:-------------|:--------|:----------|:----------------|:--------|:------|:----------|:--------------|:----------|:-------------|:--------------------|:------------|:---------------|:------|:-----------|:------|:--------|:-----------|:--------|:-----------------|:-----------|:------------|:--------|:-----------------|:-----------------|:------|:---------------|:-------------|:---------------|:--------------|:-------------------|:------------------|:-----------|:---------------------|:---------------|:----------|:-----|:-------------|:---------|:----------------|:---------------------|:----------------|:-------------------|:-------------------|:----------------|:------------------|:-------------------|:--------------|:--------------|:----------------|:--------------------|:----------|:-----------|:------------|:--------------------|:-----------|:-----------------|:------|:--------------|:-----------|:----------------|:----------------------|:--------------|:----------------|:--------------------------|:----------------|:--------------|:------------|:------------------|:-----------|:-----------------|:------|:--------|:-----------------|:-------------|:-------------------|:--------------|:----------------|:---------|
| 0 | 20 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | 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 | 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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 15 |  |  |  |  |  | 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 | X | | | | | | | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | 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 |
|
CyberHarem/mochizuki_anna_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T11:07:24+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T21:24:49+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of mochizuki\_anna/望月杏奈/모치즈키안나 (THE iDOLM@STER: Million Live!)
======================================================================
This is the dataset of mochizuki\_anna/望月杏奈/모치즈키안나 (THE iDOLM@STER: Million Live!), containing 494 images and their tags.
The core tags of this character are 'long\_hair, purple\_hair, ahoge, hair\_between\_eyes, bangs, green\_eyes, aqua\_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"
] |
de48028d458b9a51a641e72b7c255a287e9ee63a
|
please use the val ign file for training, its much cleaner. thanks :)
|
knowrohit07/know_sql
|
[
"license:openrail",
"region:us"
] |
2023-09-16T11:18:52+00:00
|
{"license": "openrail"}
|
2023-09-20T19:13:06+00:00
|
[] |
[] |
TAGS
#license-openrail #region-us
|
please use the val ign file for training, its much cleaner. thanks :)
|
[] |
[
"TAGS\n#license-openrail #region-us \n"
] |
[
12
] |
[
"passage: TAGS\n#license-openrail #region-us \n"
] |
aeab570abd14b5b57500ca916d1fa2d5d6e9f55f
|
# Dataset of makabe_mizuki/真壁瑞希 (THE iDOLM@STER: Million Live!)
This is the dataset of makabe_mizuki/真壁瑞希 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are `purple_hair, short_hair, yellow_eyes, bangs, 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 | 567.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makabe_mizuki_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 344.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makabe_mizuki_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1133 | 704.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makabe_mizuki_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 509.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makabe_mizuki_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1133 | 971.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/makabe_mizuki_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/makabe_mizuki_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, beret, long_sleeves, solo, hairclip, jacket, looking_at_viewer, shirt, blush, sweater, upper_body, x_hair_ornament, black_headwear, black_ribbon, neck_ribbon, open_clothes, skirt, smile |
| 1 | 6 |  |  |  |  |  | 1girl, blue_shirt, looking_at_viewer, short_sleeves, white_background, blue_skirt, expressionless, pleated_skirt, simple_background, solo, small_breasts, black_skirt, checkered_necktie, green_necktie, light_blush, wavy_hair |
| 2 | 5 |  |  |  |  |  | 1girl, blue_shirt, looking_at_viewer, short_sleeves, solo, upper_body, collared_shirt, green_necktie, simple_background, white_background, wing_collar, blush, plaid_necktie, smile |
| 3 | 5 |  |  |  |  |  | 1girl, blush, floral_print, hair_flower, looking_at_viewer, obi, solo, blue_kimono, upper_body, print_kimono, white_background, yukata, festival, holding_stuffed_toy, object_hug, simple_background, stuffed_shark, wide_sleeves |
| 4 | 7 |  |  |  |  |  | bare_shoulders, looking_at_viewer, blush, frilled_dress, hairband, heart, white_gloves, 1girl, bow, hair_flower, holding_card, playing_card, puffy_short_sleeves, solo, detached_collar, expressionless, hands_up, orange_dress, white_collar, buttons, ribbon, smile, wavy_hair, wrist_cuffs |
| 5 | 6 |  |  |  |  |  | navel, nipples, small_breasts, 1girl, female_pubic_hair, solo, blush, completely_nude, looking_at_viewer, open_mouth |
| 6 | 6 |  |  |  |  |  | fake_mustache, long_sleeves, 1girl, black_jacket, black_pants, red_bowtie, solo, white_shirt, buttons, glasses, monocle, center_frills, frilled_sleeves, hat, holding, looking_at_viewer |
| 7 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, blush, small_breasts, simple_background, white_bikini, frilled_bikini, white_background, armpits, necklace |
| 8 | 7 |  |  |  |  |  | 1boy, 1girl, blush, female_pubic_hair, hetero, solo_focus, nipples, small_breasts, necktie, penis, pussy, spread_legs, sweat, anus, bar_censor, blue_skirt, kneehighs, navel, no_bra, one_eye_closed, underwear |
| 9 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, detached_sleeves, frills, small_breasts, white_background, blue_headwear, closed_mouth, mini_hat, simple_background, upper_body, black_sleeves, blue_dress, blue_sleeves, blush, bow, collarbone, parted_lips, serious, shorts, wavy_hair |
| 10 | 5 |  |  |  |  |  | 1girl, black_dress, frills, looking_at_viewer, solo, string, hair_flower, juliet_sleeves, lolita_fashion, parted_lips, ribbon, black_bow, black_pantyhose, black_rose, blue_bow, blue_flower, petals, simple_background, small_breasts, white_background |
| 11 | 6 |  |  |  |  |  | 1girl, athletic_leotard, simple_background, small_breasts, two-tone_leotard, white_background, white_leotard, white_pantyhose, looking_at_viewer, solo, star_print, gymnastics, split, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | beret | long_sleeves | solo | hairclip | jacket | looking_at_viewer | shirt | blush | sweater | upper_body | x_hair_ornament | black_headwear | black_ribbon | neck_ribbon | open_clothes | skirt | smile | blue_shirt | short_sleeves | white_background | blue_skirt | expressionless | pleated_skirt | simple_background | small_breasts | black_skirt | checkered_necktie | green_necktie | light_blush | wavy_hair | collared_shirt | wing_collar | plaid_necktie | floral_print | hair_flower | obi | blue_kimono | print_kimono | yukata | festival | holding_stuffed_toy | object_hug | stuffed_shark | wide_sleeves | bare_shoulders | frilled_dress | hairband | heart | white_gloves | bow | holding_card | playing_card | puffy_short_sleeves | detached_collar | hands_up | orange_dress | white_collar | buttons | ribbon | wrist_cuffs | navel | nipples | female_pubic_hair | completely_nude | open_mouth | fake_mustache | black_jacket | black_pants | red_bowtie | white_shirt | glasses | monocle | center_frills | frilled_sleeves | hat | holding | white_bikini | frilled_bikini | armpits | necklace | 1boy | hetero | solo_focus | necktie | penis | pussy | spread_legs | sweat | anus | bar_censor | kneehighs | no_bra | one_eye_closed | underwear | detached_sleeves | frills | blue_headwear | closed_mouth | mini_hat | black_sleeves | blue_dress | blue_sleeves | collarbone | parted_lips | serious | shorts | black_dress | string | juliet_sleeves | lolita_fashion | black_bow | black_pantyhose | black_rose | blue_bow | blue_flower | petals | athletic_leotard | two-tone_leotard | white_leotard | white_pantyhose | star_print | gymnastics | split | standing |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:---------------|:-------|:-----------|:---------|:--------------------|:--------|:--------|:----------|:-------------|:------------------|:-----------------|:---------------|:--------------|:---------------|:--------|:--------|:-------------|:----------------|:-------------------|:-------------|:-----------------|:----------------|:--------------------|:----------------|:--------------|:--------------------|:----------------|:--------------|:------------|:-----------------|:--------------|:----------------|:---------------|:--------------|:------|:--------------|:---------------|:---------|:-----------|:----------------------|:-------------|:----------------|:---------------|:-----------------|:----------------|:-----------|:--------|:---------------|:------|:---------------|:---------------|:----------------------|:------------------|:-----------|:---------------|:---------------|:----------|:---------|:--------------|:--------|:----------|:--------------------|:------------------|:-------------|:----------------|:---------------|:--------------|:-------------|:--------------|:----------|:----------|:----------------|:------------------|:------|:----------|:---------------|:-----------------|:----------|:-----------|:-------|:---------|:-------------|:----------|:--------|:--------|:--------------|:--------|:-------|:-------------|:------------|:---------|:-----------------|:------------|:-------------------|:---------|:----------------|:---------------|:-----------|:----------------|:-------------|:---------------|:-------------|:--------------|:----------|:---------|:--------------|:---------|:-----------------|:-----------------|:------------|:------------------|:-------------|:-----------|:--------------|:---------|:-------------------|:-------------------|:----------------|:------------------|:-------------|:-------------|:--------|:-----------|
| 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 | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | 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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | 5 |  |  |  |  |  | X | | | X | | | X | | | | | | | | | | | | | | X | | | | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 11 | 6 |  |  |  |  |  | X | | | X | | | X | | | | | | | | | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/makabe_mizuki_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T11:39:27+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:39:08+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of makabe\_mizuki/真壁瑞希 (THE iDOLM@STER: Million Live!)
==============================================================
This is the dataset of makabe\_mizuki/真壁瑞希 (THE iDOLM@STER: Million Live!), containing 500 images and their tags.
The core tags of this character are 'purple\_hair, short\_hair, yellow\_eyes, bangs, 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"
] |
4d3fd84d5504a6a0a72124ad20b0e420a5ff13a1
|
# Dataset Card for "no_of_employee_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
m-aliabbas1/no_of_employee_ner
|
[
"region:us"
] |
2023-09-16T12:27:29+00:00
|
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 41599, "num_examples": 353}], "download_size": 5008, "dataset_size": 41599}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T12:27:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "no_of_employee_ner"
More Information needed
|
[
"# Dataset Card for \"no_of_employee_ner\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"no_of_employee_ner\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"no_of_employee_ner\"\n\nMore Information needed"
] |
2d51c0644ad72f05682e555e4a97f38389ec83e1
|
# Monero (XMR) Q&A Dataset
## Overview
The Monero (XMR) Q&A Dataset is a meticulously curated compilation of questions and answers focused on the Monero cryptocurrency. This dataset is designed to serve as a resource for machine learning practitioners, data scientists, cryptocurrency enthusiasts, and researchers aiming to build models that can understand, interact with, or analyze the Monero ecosystem.
## Features
- **Comprehensive Coverage**: The dataset covers a wide array of topics, ranging from basic concepts like "What is Monero?" to more complex subjects such as ring signatures, stealth addresses, and privacy mechanisms.
- **Quality Assurance**: Each entry has undergone thorough validation to ensure factual accuracy and relevance to the evolving landscape of Monero.
- **Machine Learning Ready**: Formatted to be readily used in a variety of machine learning models, including NLP algorithms for chatbots.
## Applications
- **Chatbots**: Enhance the conversational capabilities of bots focused on cryptocurrency topics.
## Format
The dataset is structured as pairs of questions and answers, you will need to process further in case your model is expecting a particular format.
|
enrdur/monero_xmr_question_answer
|
[
"language:en",
"license:wtfpl",
"finance",
"region:us"
] |
2023-09-16T12:35:54+00:00
|
{"language": ["en"], "license": "wtfpl", "pretty_name": "XMR questions & answers", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "asnwer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 118480, "num_examples": 236}], "download_size": 73482, "dataset_size": 118480}, "tags": ["finance"]}
|
2023-10-11T19:57:37+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #license-wtfpl #finance #region-us
|
# Monero (XMR) Q&A Dataset
## Overview
The Monero (XMR) Q&A Dataset is a meticulously curated compilation of questions and answers focused on the Monero cryptocurrency. This dataset is designed to serve as a resource for machine learning practitioners, data scientists, cryptocurrency enthusiasts, and researchers aiming to build models that can understand, interact with, or analyze the Monero ecosystem.
## Features
- Comprehensive Coverage: The dataset covers a wide array of topics, ranging from basic concepts like "What is Monero?" to more complex subjects such as ring signatures, stealth addresses, and privacy mechanisms.
- Quality Assurance: Each entry has undergone thorough validation to ensure factual accuracy and relevance to the evolving landscape of Monero.
- Machine Learning Ready: Formatted to be readily used in a variety of machine learning models, including NLP algorithms for chatbots.
## Applications
- Chatbots: Enhance the conversational capabilities of bots focused on cryptocurrency topics.
## Format
The dataset is structured as pairs of questions and answers, you will need to process further in case your model is expecting a particular format.
|
[
"# Monero (XMR) Q&A Dataset",
"## Overview\n\nThe Monero (XMR) Q&A Dataset is a meticulously curated compilation of questions and answers focused on the Monero cryptocurrency. This dataset is designed to serve as a resource for machine learning practitioners, data scientists, cryptocurrency enthusiasts, and researchers aiming to build models that can understand, interact with, or analyze the Monero ecosystem.",
"## Features\n\n- Comprehensive Coverage: The dataset covers a wide array of topics, ranging from basic concepts like \"What is Monero?\" to more complex subjects such as ring signatures, stealth addresses, and privacy mechanisms.\n\n- Quality Assurance: Each entry has undergone thorough validation to ensure factual accuracy and relevance to the evolving landscape of Monero.\n\n- Machine Learning Ready: Formatted to be readily used in a variety of machine learning models, including NLP algorithms for chatbots.",
"## Applications\n\n- Chatbots: Enhance the conversational capabilities of bots focused on cryptocurrency topics.",
"## Format\n\nThe dataset is structured as pairs of questions and answers, you will need to process further in case your model is expecting a particular format."
] |
[
"TAGS\n#language-English #license-wtfpl #finance #region-us \n",
"# Monero (XMR) Q&A Dataset",
"## Overview\n\nThe Monero (XMR) Q&A Dataset is a meticulously curated compilation of questions and answers focused on the Monero cryptocurrency. This dataset is designed to serve as a resource for machine learning practitioners, data scientists, cryptocurrency enthusiasts, and researchers aiming to build models that can understand, interact with, or analyze the Monero ecosystem.",
"## Features\n\n- Comprehensive Coverage: The dataset covers a wide array of topics, ranging from basic concepts like \"What is Monero?\" to more complex subjects such as ring signatures, stealth addresses, and privacy mechanisms.\n\n- Quality Assurance: Each entry has undergone thorough validation to ensure factual accuracy and relevance to the evolving landscape of Monero.\n\n- Machine Learning Ready: Formatted to be readily used in a variety of machine learning models, including NLP algorithms for chatbots.",
"## Applications\n\n- Chatbots: Enhance the conversational capabilities of bots focused on cryptocurrency topics.",
"## Format\n\nThe dataset is structured as pairs of questions and answers, you will need to process further in case your model is expecting a particular format."
] |
[
21,
12,
89,
124,
25,
34
] |
[
"passage: TAGS\n#language-English #license-wtfpl #finance #region-us \n# Monero (XMR) Q&A Dataset## Overview\n\nThe Monero (XMR) Q&A Dataset is a meticulously curated compilation of questions and answers focused on the Monero cryptocurrency. This dataset is designed to serve as a resource for machine learning practitioners, data scientists, cryptocurrency enthusiasts, and researchers aiming to build models that can understand, interact with, or analyze the Monero ecosystem.## Features\n\n- Comprehensive Coverage: The dataset covers a wide array of topics, ranging from basic concepts like \"What is Monero?\" to more complex subjects such as ring signatures, stealth addresses, and privacy mechanisms.\n\n- Quality Assurance: Each entry has undergone thorough validation to ensure factual accuracy and relevance to the evolving landscape of Monero.\n\n- Machine Learning Ready: Formatted to be readily used in a variety of machine learning models, including NLP algorithms for chatbots.## Applications\n\n- Chatbots: Enhance the conversational capabilities of bots focused on cryptocurrency topics.## Format\n\nThe dataset is structured as pairs of questions and answers, you will need to process further in case your model is expecting a particular format."
] |
b1bfb275a639783f79457681135aabe32f2b9757
|
# Dataset Card for "cars-for-sale"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Binaryy/cars-for-sale
|
[
"region:us"
] |
2023-09-16T12:42:34+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "Unnamed: 0", "dtype": "int64"}, {"name": "Car Name", "dtype": "string"}, {"name": "Region", "dtype": "string"}, {"name": "Price", "dtype": "string"}, {"name": "Status", "dtype": "string"}, {"name": "Mileage", "dtype": "string"}, {"name": "Car Name.1", "dtype": "string"}, {"name": "Image URL", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8301111.18, "num_examples": 1332}], "download_size": 8084700, "dataset_size": 8301111.18}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T12:43:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cars-for-sale"
More Information needed
|
[
"# Dataset Card for \"cars-for-sale\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cars-for-sale\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cars-for-sale\"\n\nMore Information needed"
] |
120e5e3cfcdd0d3cf24461b7af2a0e313679612c
|
# Dataset of shimabara_elena/島原エレナ (THE iDOLM@STER: Million Live!)
This is the dataset of shimabara_elena/島原エレナ (THE iDOLM@STER: Million Live!), containing 284 images and their tags.
The core tags of this character are `green_hair, long_hair, ahoge, blue_eyes, hairband, bangs, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 284 | 295.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 284 | 197.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 587 | 383.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 284 | 271.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 587 | 508.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shimabara_elena_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, navel, solo, blunt_bangs, blush, collarbone, cowboy_shot, large_breasts, o-ring_bikini, o-ring_bottom, open_mouth, day, outdoors, white_bikini, :d, arm_up, bare_shoulders, earrings, halterneck, heart, medium_breasts, necklace, o-ring_top, signature, skindentation, standing, stomach, thigh_gap, upper_teeth_only, wading, water, wet |
| 1 | 20 |  |  |  |  |  | open_mouth, 1girl, solo, looking_at_viewer, :d, aqua_eyes, jewelry, blush, navel |
| 2 | 9 |  |  |  |  |  | 1girl, blush, 1boy, hetero, nipples, penis, sex, solo_focus, vaginal, open_mouth, pussy, sweat, completely_nude, medium_breasts, mosaic_censoring, spread_legs, navel, cum, female_pubic_hair, girl_on_top, looking_at_viewer, smile, straddling |
| 3 | 20 |  |  |  |  |  | blush, open_mouth, serafuku, white_shirt, 1girl, long_sleeves, solo, hair_bow, pleated_skirt, blue_skirt, :d, neckerchief, cloud, looking_at_viewer, sky, blue_hairband, very_long_hair, day, outdoors, standing, white_sailor_collar |
| 4 | 5 |  |  |  |  |  | earrings, bare_shoulders, blunt_bangs, looking_at_viewer, medium_breasts, sleeveless_dress, yellow_dress, 2girls, blush, corset, frills, open_mouth, print_dress, smile, solo_focus, standing, 1girl, ;d, aqua_eyes, arm_up, black_gloves, blurry_foreground, brown_hair, choker, cowboy_shot, cross-laced_clothes, depth_of_field, hat_flower, mini_hat, one_eye_closed, orange_dress, parted_lips, pearl_(gemstone), pearl_bracelet, pearl_necklace, petals, simple_background, sparkle, stage, wavy_hair, white_background, wrist_cuffs, yellow_headwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | looking_at_viewer | navel | solo | blunt_bangs | blush | collarbone | cowboy_shot | large_breasts | o-ring_bikini | o-ring_bottom | open_mouth | day | outdoors | white_bikini | :d | arm_up | bare_shoulders | earrings | halterneck | heart | medium_breasts | necklace | o-ring_top | signature | skindentation | standing | stomach | thigh_gap | upper_teeth_only | wading | water | wet | aqua_eyes | jewelry | 1boy | hetero | nipples | penis | sex | solo_focus | vaginal | pussy | sweat | completely_nude | mosaic_censoring | spread_legs | cum | female_pubic_hair | girl_on_top | smile | straddling | serafuku | white_shirt | long_sleeves | hair_bow | pleated_skirt | blue_skirt | neckerchief | cloud | sky | blue_hairband | very_long_hair | white_sailor_collar | sleeveless_dress | yellow_dress | 2girls | corset | frills | print_dress | ;d | black_gloves | blurry_foreground | brown_hair | choker | cross-laced_clothes | depth_of_field | hat_flower | mini_hat | one_eye_closed | orange_dress | parted_lips | pearl_(gemstone) | pearl_bracelet | pearl_necklace | petals | simple_background | sparkle | stage | wavy_hair | white_background | wrist_cuffs | yellow_headwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:--------|:-------|:--------------|:--------|:-------------|:--------------|:----------------|:----------------|:----------------|:-------------|:------|:-----------|:---------------|:-----|:---------|:-----------------|:-----------|:-------------|:--------|:-----------------|:-----------|:-------------|:------------|:----------------|:-----------|:----------|:------------|:-------------------|:---------|:--------|:------|:------------|:----------|:-------|:---------|:----------|:--------|:------|:-------------|:----------|:--------|:--------|:------------------|:-------------------|:--------------|:------|:--------------------|:--------------|:--------|:-------------|:-----------|:--------------|:---------------|:-----------|:----------------|:-------------|:--------------|:--------|:------|:----------------|:-----------------|:----------------------|:-------------------|:---------------|:---------|:---------|:---------|:--------------|:-----|:---------------|:--------------------|:-------------|:---------|:----------------------|:-----------------|:-------------|:-----------|:-----------------|:---------------|:--------------|:-------------------|:-----------------|:-----------------|:---------|:--------------------|:----------|:--------|:------------|:-------------------|:--------------|:------------------|
| 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 20 |  |  |  |  |  | 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 | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 20 |  |  |  |  |  | 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 | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/shimabara_elena_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T12:42:56+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:46:12+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of shimabara\_elena/島原エレナ (THE iDOLM@STER: Million Live!)
=================================================================
This is the dataset of shimabara\_elena/島原エレナ (THE iDOLM@STER: Million Live!), containing 284 images and their tags.
The core tags of this character are 'green\_hair, long\_hair, ahoge, blue\_eyes, hairband, bangs, breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
ce1446419bbb2b9787ef0df185ac010c1da65f72
|
# Dataset Card for "guanaco-llama2-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stevessschen/guanaco-llama2-100
|
[
"region:us"
] |
2023-09-16T12:55:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 184326, "num_examples": 100}], "download_size": 111858, "dataset_size": 184326}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T12:55:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-100"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-100\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-100\"\n\nMore Information needed"
] |
06a156292cb2eb8e3b6124556f7037ac22a7bd43
|
# Dataset of kousaka_umi/高坂海美 (THE iDOLM@STER: Million Live!)
This is the dataset of kousaka_umi/高坂海美 (THE iDOLM@STER: Million Live!), containing 268 images and their tags.
The core tags of this character are `long_hair, brown_hair, braid, blue_eyes, twin_braids, bangs, 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 | 268 | 298.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_umi_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 268 | 195.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_umi_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 620 | 398.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_umi_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 268 | 275.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_umi_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 620 | 520.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_umi_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kousaka_umi_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, aqua_eyes, open_mouth, :d, bracelet, necklace, navel |
| 1 | 9 |  |  |  |  |  | 1girl, smile, striped_bikini, blush, cleavage, looking_at_viewer, navel, open_mouth, solo, collarbone, white_background, front-tie_top, simple_background |
| 2 | 10 |  |  |  |  |  | day, smile, 1girl, navel, solo, cleavage, outdoors, looking_at_viewer, open_mouth, beach, blue_sky, bracelet, cloud, cowboy_shot, palm_tree, side-tie_bikini_bottom, standing |
| 3 | 14 |  |  |  |  |  | 1girl, solo, open_mouth, white_shirt, looking_at_viewer, short_sleeves, blush, short_shorts, midriff, navel, tied_shirt, :d, belt, blue_shorts, collarbone, hair_between_eyes, side_braid, denim_shorts, standing, white_background, simple_background, striped_shirt, crop_top, holding, very_long_hair |
| 4 | 7 |  |  |  |  |  | looking_at_viewer, open_mouth, 1girl, beret, red_neckerchief, solo, white_headwear, white_shirt, blue_skirt, red_bow, sleeveless_shirt, standing, white_sailor_collar, cowboy_shot, hair_bow, holding, miniskirt, pleated_skirt, short_shorts, shorts_under_skirt, white_gloves, :d, ;d, blue_sky, cloud, day, hat_bow, one_eye_closed, white_shorts |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | aqua_eyes | open_mouth | :d | bracelet | necklace | navel | smile | striped_bikini | blush | cleavage | collarbone | white_background | front-tie_top | simple_background | day | outdoors | beach | blue_sky | cloud | cowboy_shot | palm_tree | side-tie_bikini_bottom | standing | white_shirt | short_sleeves | short_shorts | midriff | tied_shirt | belt | blue_shorts | hair_between_eyes | side_braid | denim_shorts | striped_shirt | crop_top | holding | very_long_hair | beret | red_neckerchief | white_headwear | blue_skirt | red_bow | sleeveless_shirt | white_sailor_collar | hair_bow | miniskirt | pleated_skirt | shorts_under_skirt | white_gloves | ;d | hat_bow | one_eye_closed | white_shorts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:------------|:-------------|:-----|:-----------|:-----------|:--------|:--------|:-----------------|:--------|:-----------|:-------------|:-------------------|:----------------|:--------------------|:------|:-----------|:--------|:-----------|:--------|:--------------|:------------|:-------------------------|:-----------|:--------------|:----------------|:---------------|:----------|:-------------|:-------|:--------------|:--------------------|:-------------|:---------------|:----------------|:-----------|:----------|:-----------------|:--------|:------------------|:-----------------|:-------------|:----------|:-------------------|:----------------------|:-----------|:------------|:----------------|:---------------------|:---------------|:-----|:----------|:-----------------|:---------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 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 | | | | | | | | | | | | | | | | |
| 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 | X | X | X | X | X | X |
|
CyberHarem/kousaka_umi_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T12:55:44+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:23:03+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kousaka\_umi/高坂海美 (THE iDOLM@STER: Million Live!)
============================================================
This is the dataset of kousaka\_umi/高坂海美 (THE iDOLM@STER: Million Live!), containing 268 images and their tags.
The core tags of this character are 'long\_hair, brown\_hair, braid, blue\_eyes, twin\_braids, bangs, 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"
] |
a18f725219e88c504db79d3c4e598ede2ace9ad4
|
# Dataset Card for "claim_detection_paper_test_distilbert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/claim_detection_paper_test_distilbert
|
[
"region:us"
] |
2023-09-16T12:57:08+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "Is_Claim", "dtype": "string"}, {"name": "Claim_detection_result", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1176058, "num_examples": 11073}], "download_size": 507439, "dataset_size": 1176058}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T12:57:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "claim_detection_paper_test_distilbert"
More Information needed
|
[
"# Dataset Card for \"claim_detection_paper_test_distilbert\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"claim_detection_paper_test_distilbert\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"claim_detection_paper_test_distilbert\"\n\nMore Information needed"
] |
44090c68b3699d1c2f3dd1240905484ff6644ccc
|
# Dataset Card for "claim_detection_paper_test_bert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/claim_detection_paper_test_bert
|
[
"region:us"
] |
2023-09-16T13:01:48+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "Is_Claim", "dtype": "string"}, {"name": "Claim_detection_result", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1175941, "num_examples": 11073}], "download_size": 507279, "dataset_size": 1175941}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T13:01:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "claim_detection_paper_test_bert"
More Information needed
|
[
"# Dataset Card for \"claim_detection_paper_test_bert\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"claim_detection_paper_test_bert\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"claim_detection_paper_test_bert\"\n\nMore Information needed"
] |
67dd2894ec026bdcdc80031a8f4f2505e16b4f05
|
# Dataset Card for Evaluation run of titan087/OpenLlama13B-Guanaco
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/titan087/OpenLlama13B-Guanaco
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [titan087/OpenLlama13B-Guanaco](https://huggingface.co/titan087/OpenLlama13B-Guanaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_titan087__OpenLlama13B-Guanaco",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:18:40.707910](https://huggingface.co/datasets/open-llm-leaderboard/details_titan087__OpenLlama13B-Guanaco/blob/main/results_2023-12-03T17-18-40.707910.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": 0.0758150113722517,
"acc_stderr": 0.007291205723162607
},
"harness|gsm8k|5": {
"acc": 0.0758150113722517,
"acc_stderr": 0.007291205723162607
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_titan087__OpenLlama13B-Guanaco
|
[
"region:us"
] |
2023-09-16T13:03:28+00:00
|
{"pretty_name": "Evaluation run of titan087/OpenLlama13B-Guanaco", "dataset_summary": "Dataset automatically created during the evaluation run of model [titan087/OpenLlama13B-Guanaco](https://huggingface.co/titan087/OpenLlama13B-Guanaco) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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_titan087__OpenLlama13B-Guanaco\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-03T17:18:40.707910](https://huggingface.co/datasets/open-llm-leaderboard/details_titan087__OpenLlama13B-Guanaco/blob/main/results_2023-12-03T17-18-40.707910.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\": 0.0758150113722517,\n \"acc_stderr\": 0.007291205723162607\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0758150113722517,\n \"acc_stderr\": 0.007291205723162607\n }\n}\n```", "repo_url": "https://huggingface.co/titan087/OpenLlama13B-Guanaco", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_16T14_03_24.825840", "path": ["**/details_harness|drop|3_2023-09-16T14-03-24.825840.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-16T14-03-24.825840.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_16T14_03_24.825840", "path": ["**/details_harness|gsm8k|5_2023-09-16T14-03-24.825840.parquet"]}, {"split": "2023_12_03T17_18_40.707910", "path": ["**/details_harness|gsm8k|5_2023-12-03T17-18-40.707910.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-03T17-18-40.707910.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_16T14_03_24.825840", "path": ["**/details_harness|winogrande|5_2023-09-16T14-03-24.825840.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-16T14-03-24.825840.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_16T14_03_24.825840", "path": ["results_2023-09-16T14-03-24.825840.parquet"]}, {"split": "2023_12_03T17_18_40.707910", "path": ["results_2023-12-03T17-18-40.707910.parquet"]}, {"split": "latest", "path": ["results_2023-12-03T17-18-40.707910.parquet"]}]}]}
|
2023-12-03T17:18:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of titan087/OpenLlama13B-Guanaco
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model titan087/OpenLlama13B-Guanaco on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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 2023-12-03T17:18:40.707910(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of titan087/OpenLlama13B-Guanaco",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model titan087/OpenLlama13B-Guanaco on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-12-03T17:18:40.707910(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of titan087/OpenLlama13B-Guanaco",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model titan087/OpenLlama13B-Guanaco on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-12-03T17:18:40.707910(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of titan087/OpenLlama13B-Guanaco## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model titan087/OpenLlama13B-Guanaco on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2023-12-03T17:18:40.707910(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
f6f8b702f46ad0544fab2fd933e8d0c0034f3f9f
|
# Dataset Card for "synapsellm-v0-1-decilm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
WebraftAI/synapsellm-v0-1-decilm
|
[
"region:us"
] |
2023-09-16T13:04:52+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5101008, "num_examples": 9446}], "download_size": 3210322, "dataset_size": 5101008}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T13:12:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "synapsellm-v0-1-decilm"
More Information needed
|
[
"# Dataset Card for \"synapsellm-v0-1-decilm\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"synapsellm-v0-1-decilm\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"synapsellm-v0-1-decilm\"\n\nMore Information needed"
] |
675293c77f0bb3db4a38389d88e92b4ec910e355
|
# Dataset Card for "sql-create-context-modified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rams901/sql-create-context-modified
|
[
"region:us"
] |
2023-09-16T13:11:32+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1415326, "num_examples": 3000}], "download_size": 632495, "dataset_size": 1415326}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T13:11:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sql-create-context-modified"
More Information needed
|
[
"# Dataset Card for \"sql-create-context-modified\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sql-create-context-modified\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sql-create-context-modified\"\n\nMore Information needed"
] |
69f7e622f40df6735725cbf5eeead50c879806e0
|
# Dataset Card for "sql-create-context-3k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rams901/sql-create-context-3k
|
[
"region:us"
] |
2023-09-16T13:11:41+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1415326, "num_examples": 3000}], "download_size": 632495, "dataset_size": 1415326}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T13:11:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sql-create-context-3k"
More Information needed
|
[
"# Dataset Card for \"sql-create-context-3k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sql-create-context-3k\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sql-create-context-3k\"\n\nMore Information needed"
] |
8775e500536f43835769a46e73cfc2a481dcb21a
|
# Dataset of yabuki_kana/矢吹可奈/야부키카나 (THE iDOLM@STER: Million Live!)
This is the dataset of yabuki_kana/矢吹可奈/야부키카나 (THE iDOLM@STER: Million Live!), containing 161 images and their tags.
The core tags of this character are `short_hair, brown_hair, ahoge, hair_ornament, orange_hair, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 161 | 139.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yabuki_kana_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 161 | 106.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yabuki_kana_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 334 | 202.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yabuki_kana_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 161 | 130.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yabuki_kana_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 334 | 244.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yabuki_kana_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/yabuki_kana_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 12 |  |  |  |  |  | 1girl, solo, open_mouth, looking_at_viewer, dress, character_name, :d, jewelry, microphone, yellow_eyes |
| 1 | 10 |  |  |  |  |  | 1girl, open_mouth, solo, blush, looking_at_viewer, hair_bobbles, white_shirt, short_sleeves, smile, bangs, dress, simple_background, white_background, plaid |
| 2 | 12 |  |  |  |  |  | 2girls, open_mouth, blush, closed_eyes, hair_bobbles, :d, dress, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | open_mouth | looking_at_viewer | dress | character_name | :d | jewelry | microphone | yellow_eyes | blush | hair_bobbles | white_shirt | short_sleeves | smile | bangs | simple_background | white_background | plaid | 2girls | closed_eyes | solo_focus |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:--------------------|:--------|:-----------------|:-----|:----------|:-------------|:--------------|:--------|:---------------|:--------------|:----------------|:--------|:--------|:--------------------|:-------------------|:--------|:---------|:--------------|:-------------|
| 0 | 12 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | | | | | | X | X | X | X | X | X | X | X | X | | | |
| 2 | 12 |  |  |  |  |  | | | X | | X | | X | | | | X | X | | | | | | | | X | X | X |
|
CyberHarem/yabuki_kana_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T13:13:51+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T01:25:21+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of yabuki\_kana/矢吹可奈/야부키카나 (THE iDOLM@STER: Million Live!)
==================================================================
This is the dataset of yabuki\_kana/矢吹可奈/야부키카나 (THE iDOLM@STER: Million Live!), containing 161 images and their tags.
The core tags of this character are 'short\_hair, brown\_hair, ahoge, hair\_ornament, orange\_hair, brown\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
55c108fa692be4dbb76b881463591bc799b79791
|
# Dataset Card for "claim_detection_paper_test_squeezebert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/claim_detection_paper_test_squeezebert
|
[
"region:us"
] |
2023-09-16T13:13:55+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "Is_Claim", "dtype": "string"}, {"name": "Claim_detection_result", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1175947, "num_examples": 11073}], "download_size": 507280, "dataset_size": 1175947}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T13:13:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "claim_detection_paper_test_squeezebert"
More Information needed
|
[
"# Dataset Card for \"claim_detection_paper_test_squeezebert\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"claim_detection_paper_test_squeezebert\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"claim_detection_paper_test_squeezebert\"\n\nMore Information needed"
] |
2b3976a732d860091fdfb9000103944bb64f1b98
|
Synthethic Question & Answer dataset trained on a corpus of the book Basic Economics by Thomas Sowell.
Formating could be improved, as model trained on this dataset write \n tokens as words and not as newline, so I guess it gets tokenized in a way different from expectations.
Note that prompt format isn't very consistent in every sample.
Spicyboros 7B gguf was used as a model that generated synthetic responses, so it was all generated locally without leaving the device, as opposed to how commonly GPT 3.5 Turbo or GPT 4 would be used for the purpose.
|
adamo1139/basic_economics_questions_ts_test_1
|
[
"region:us"
] |
2023-09-16T13:23:43+00:00
|
{}
|
2023-09-17T11:06:03+00:00
|
[] |
[] |
TAGS
#region-us
|
Synthethic Question & Answer dataset trained on a corpus of the book Basic Economics by Thomas Sowell.
Formating could be improved, as model trained on this dataset write \n tokens as words and not as newline, so I guess it gets tokenized in a way different from expectations.
Note that prompt format isn't very consistent in every sample.
Spicyboros 7B gguf was used as a model that generated synthetic responses, so it was all generated locally without leaving the device, as opposed to how commonly GPT 3.5 Turbo or GPT 4 would be used for the purpose.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
d5e878d8329975a6cdf0d389d3633c3851c77b3f
|
# Dataset Card for "all-lucidrain-code-python-tokenized-8192"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
kye/all-lucidrain-code-python-tokenized-8192
|
[
"region:us"
] |
2023-09-16T13:30:45+00:00
|
{"dataset_info": {"features": [{"name": "repo_name", "sequence": "string"}, {"name": "file_path", "sequence": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2299336, "num_examples": 21}], "download_size": 349131, "dataset_size": 2299336}}
|
2023-09-21T00:40:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "all-lucidrain-code-python-tokenized-8192"
More Information needed
|
[
"# Dataset Card for \"all-lucidrain-code-python-tokenized-8192\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"all-lucidrain-code-python-tokenized-8192\"\n\nMore Information needed"
] |
[
6,
27
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"all-lucidrain-code-python-tokenized-8192\"\n\nMore Information needed"
] |
88878a92b9f9ecd050cdc55c50196185cde9f1db
|
# Dataset Card for "sql-create-context-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Rams901/sql-create-context-full
|
[
"region:us"
] |
2023-09-16T13:58:09+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36841451, "num_examples": 78577}], "download_size": 13250458, "dataset_size": 36841451}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T13:58:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sql-create-context-full"
More Information needed
|
[
"# Dataset Card for \"sql-create-context-full\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sql-create-context-full\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sql-create-context-full\"\n\nMore Information needed"
] |
a3ce97cb725bf56e50aefdf663644a593b26ba84
|
# Dataset Card for "a2d1bcf0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/a2d1bcf0
|
[
"region:us"
] |
2023-09-16T14:16:30+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 220, "num_examples": 10}], "download_size": 1379, "dataset_size": 220}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T14:16:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "a2d1bcf0"
More Information needed
|
[
"# Dataset Card for \"a2d1bcf0\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"a2d1bcf0\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"a2d1bcf0\"\n\nMore Information needed"
] |
4168db128b43cf7842514e8de56a502d58401e5c
|
# Dataset Card for "8e18a25b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/8e18a25b
|
[
"region:us"
] |
2023-09-16T14:18:37+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 191, "num_examples": 10}], "download_size": 1358, "dataset_size": 191}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T14:18:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "8e18a25b"
More Information needed
|
[
"# Dataset Card for \"8e18a25b\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"8e18a25b\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"8e18a25b\"\n\nMore Information needed"
] |
36fa9a56c9ab5458bc08ab252913845b69e015d2
|
# RAFT submissions for RaftSub
## Submitting to the leaderboard
To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
1. Generate predictions on the unlabeled test set of each task
2. Validate the predictions are compatible with the evaluation framework
3. Push the predictions to the Hub!
See the instructions below for more details.
### Rules
1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week.
2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
### Submission file format
For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
* ID (int)
* Label (string)
See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
```python
from pathlib import Path
import pandas as pd
from collections import Counter
from datasets import load_dataset, get_dataset_config_names
tasks = get_dataset_config_names("ought/raft")
for task in tasks:
# Load dataset
raft_subset = load_dataset("ought/raft", task)
# Compute majority class over training set
counter = Counter(raft_subset["train"]["Label"])
majority_class = counter.most_common(1)[0][0]
# Load predictions file
preds = pd.read_csv(f"data/{task}/predictions.csv")
# Convert label IDs to label names
preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
# Save predictions
preds.to_csv(f"data/{task}/predictions.csv", index=False)
```
As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
```
data
├── ade_corpus_v2
│ ├── predictions.csv
│ └── task.json
├── banking_77
│ ├── predictions.csv
│ └── task.json
├── neurips_impact_statement_risks
│ ├── predictions.csv
│ └── task.json
├── one_stop_english
│ ├── predictions.csv
│ └── task.json
├── overruling
│ ├── predictions.csv
│ └── task.json
├── semiconductor_org_types
│ ├── predictions.csv
│ └── task.json
├── systematic_review_inclusion
│ ├── predictions.csv
│ └── task.json
├── tai_safety_research
│ ├── predictions.csv
│ └── task.json
├── terms_of_service
│ ├── predictions.csv
│ └── task.json
├── tweet_eval_hate
│ ├── predictions.csv
│ └── task.json
└── twitter_complaints
├── predictions.csv
└── task.json
```
### Validate your submission
To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
```
python cli.py validate
```
If everything is correct, you should see the following message:
```
All submission files validated! ✨ 🚀 ✨
Now you can make a submission 🤗
```
### Push your submission to the Hugging Face Hub!
The final step is to commit your files and push them to the Hub:
```
python cli.py submit
```
If there are no errors, you should see the following message:
```
Submission successful! 🎉 🥳 🎉
Your submission will be evaulated on Sunday 05 September 2021 ⏳
```
where the evaluation is run every Sunday and your results will be visible on the leaderboard.
|
HLaci/RaftSub
|
[
"benchmark:raft",
"region:us"
] |
2023-09-16T14:21:47+00:00
|
{"benchmark": "raft", "type": "prediction", "submission_name": "SetFitBase"}
|
2023-09-18T12:03:43+00:00
|
[] |
[] |
TAGS
#benchmark-raft #region-us
|
# RAFT submissions for RaftSub
## Submitting to the leaderboard
To make a submission to the leaderboard, there are three main steps:
1. Generate predictions on the unlabeled test set of each task
2. Validate the predictions are compatible with the evaluation framework
3. Push the predictions to the Hub!
See the instructions below for more details.
### Rules
1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week.
2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
### Submission file format
For each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
* ID (int)
* Label (string)
See the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
As you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:
### Validate your submission
To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
If everything is correct, you should see the following message:
### Push your submission to the Hugging Face Hub!
The final step is to commit your files and push them to the Hub:
If there are no errors, you should see the following message:
where the evaluation is run every Sunday and your results will be visible on the leaderboard.
|
[
"# RAFT submissions for RaftSub",
"## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.",
"### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.",
"### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:",
"### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:",
"### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard."
] |
[
"TAGS\n#benchmark-raft #region-us \n",
"# RAFT submissions for RaftSub",
"## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.",
"### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.",
"### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:",
"### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:",
"### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard."
] |
[
13,
9,
68,
150,
135,
45,
61
] |
[
"passage: TAGS\n#benchmark-raft #region-us \n# RAFT submissions for RaftSub## Submitting to the leaderboard\n\nTo make a submission to the leaderboard, there are three main steps:\n\n1. Generate predictions on the unlabeled test set of each task\n2. Validate the predictions are compatible with the evaluation framework\n3. Push the predictions to the Hub!\n\nSee the instructions below for more details.### Rules\n\n1. To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. \n2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.\n3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.\n4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.### Submission file format\n\nFor each task in RAFT, you should create a CSV file called 'URL' with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:\n\n* ID (int)\n* Label (string)\n\nSee the dummy predictions in the 'data' folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:\n\n\n\nAs you can see in the example, each 'URL' file should be stored in the task's subfolder in 'data' and at the end you should have something like the following:### Validate your submission\n\nTo ensure that your submission files are correctly formatted, run the following command from the root of the repository:\n\n\n\nIf everything is correct, you should see the following message:### Push your submission to the Hugging Face Hub!\n\nThe final step is to commit your files and push them to the Hub:\n\n\n\nIf there are no errors, you should see the following message:\n\n\n\nwhere the evaluation is run every Sunday and your results will be visible on the leaderboard."
] |
7bd4fa8a451ca28839571e8086e984740ff43940
|
# Dataset of nagayoshi_subaru/永吉昴 (THE iDOLM@STER: Million Live!)
This is the dataset of nagayoshi_subaru/永吉昴 (THE iDOLM@STER: Million Live!), containing 229 images and their tags.
The core tags of this character are `short_hair, green_hair, red_eyes, bangs, brown_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 229 | 242.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 229 | 154.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 519 | 319.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 229 | 219.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 519 | 431.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nagayoshi_subaru_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nagayoshi_subaru_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, long_sleeves, white_gloves, ascot, closed_mouth, crown, hat, purple_eyes, white_background, white_jacket, white_pants, blush, epaulettes, grin, hair_between_eyes, hair_ornament, simple_background, upper_body |
| 1 | 12 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, :d, solo, purple_eyes, dress, jewelry, necktie |
| 2 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, purple_eyes, solo, open_mouth, :d, baseball, letterman_jacket, shorts |
| 3 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, open_mouth, skirt, smile, jewelry |
| 4 | 12 |  |  |  |  |  | 1girl, hair_between_eyes, blush, looking_at_viewer, simple_background, solo, smile, white_background, white_shirt, upper_body, open_mouth, collarbone, jacket, short_sleeves |
| 5 | 5 |  |  |  |  |  | 1girl, blush, solo, cleavage, looking_at_viewer, medium_breasts, navel, collarbone, hair_between_eyes, sitting, smile, striped_bikini, beachball, one_eye_closed, open_mouth, partially_submerged, short_shorts, water |
| 6 | 6 |  |  |  |  |  | 1girl, blush, censored, nipples, pussy, small_breasts, hetero, on_back, open_mouth, solo_focus, 1boy, spread_legs, hair_between_eyes, navel, nude, on_bed, penis, pillow, sex, sweat |
| 7 | 5 |  |  |  |  |  | 1girl, detached_collar, playboy_bunny, rabbit_ears, solo, fake_animal_ears, strapless_leotard, wrist_cuffs, blush, indian_style, looking_at_viewer, pantyhose, rabbit_tail, simple_background, small_breasts, black_bowtie, black_leotard, cleavage, covered_navel, purple_eyes, red_bowtie, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | long_sleeves | white_gloves | ascot | closed_mouth | crown | hat | purple_eyes | white_background | white_jacket | white_pants | blush | epaulettes | grin | hair_between_eyes | hair_ornament | simple_background | upper_body | open_mouth | :d | dress | jewelry | necktie | baseball | letterman_jacket | shorts | skirt | smile | white_shirt | collarbone | jacket | short_sleeves | cleavage | medium_breasts | navel | sitting | striped_bikini | beachball | one_eye_closed | partially_submerged | short_shorts | water | censored | nipples | pussy | small_breasts | hetero | on_back | solo_focus | 1boy | spread_legs | nude | on_bed | penis | pillow | sex | sweat | detached_collar | playboy_bunny | rabbit_ears | fake_animal_ears | strapless_leotard | wrist_cuffs | indian_style | pantyhose | rabbit_tail | black_bowtie | black_leotard | covered_navel | red_bowtie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:---------------|:---------------|:--------|:---------------|:--------|:------|:--------------|:-------------------|:---------------|:--------------|:--------|:-------------|:-------|:--------------------|:----------------|:--------------------|:-------------|:-------------|:-----|:--------|:----------|:----------|:-----------|:-------------------|:---------|:--------|:--------|:--------------|:-------------|:---------|:----------------|:-----------|:-----------------|:--------|:----------|:-----------------|:------------|:-----------------|:----------------------|:---------------|:--------|:-----------|:----------|:--------|:----------------|:---------|:----------|:-------------|:-------|:--------------|:-------|:---------|:--------|:---------|:------|:--------|:------------------|:----------------|:--------------|:-------------------|:--------------------|:--------------|:---------------|:------------|:--------------|:---------------|:----------------|:----------------|:-------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | | | | | | | X | | | | | | | | | | | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | X | | | | | | | | | | | X | | | | | | | X | | | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | | | | | | | | | | | | 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 | X | X | X | X | X | X | X |
|
CyberHarem/nagayoshi_subaru_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T14:25:16+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:29:10+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of nagayoshi\_subaru/永吉昴 (THE iDOLM@STER: Million Live!)
================================================================
This is the dataset of nagayoshi\_subaru/永吉昴 (THE iDOLM@STER: Million Live!), containing 229 images and their tags.
The core tags of this character are 'short\_hair, green\_hair, red\_eyes, bangs, brown\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
a58792353067cec83a9f4f9fb2782b5957796e3a
|
# Dataset Card for Evaluation run of vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048](https://huggingface.co/vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_vibhorag101__llama-2-7b-chat-hf-phr_mental_health-2048",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T12:14:06.343612](https://huggingface.co/datasets/open-llm-leaderboard/details_vibhorag101__llama-2-7b-chat-hf-phr_mental_health-2048/blob/main/results_2023-10-23T12-14-06.343612.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.06291946308724833,
"em_stderr": 0.0024866840810898265,
"f1": 0.1230484479865771,
"f1_stderr": 0.002835124699649658,
"acc": 0.3855268126040811,
"acc_stderr": 0.00961257569233847
},
"harness|drop|3": {
"em": 0.06291946308724833,
"em_stderr": 0.0024866840810898265,
"f1": 0.1230484479865771,
"f1_stderr": 0.002835124699649658
},
"harness|gsm8k|5": {
"acc": 0.05913570887035633,
"acc_stderr": 0.006497266660428824
},
"harness|winogrande|5": {
"acc": 0.7119179163378059,
"acc_stderr": 0.012727884724248116
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_vibhorag101__llama-2-7b-chat-hf-phr_mental_health-2048
|
[
"region:us"
] |
2023-09-16T14:28:23+00:00
|
{"pretty_name": "Evaluation run of vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048", "dataset_summary": "Dataset automatically created during the evaluation run of model [vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048](https://huggingface.co/vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vibhorag101__llama-2-7b-chat-hf-phr_mental_health-2048\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T12:14:06.343612](https://huggingface.co/datasets/open-llm-leaderboard/details_vibhorag101__llama-2-7b-chat-hf-phr_mental_health-2048/blob/main/results_2023-10-23T12-14-06.343612.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.06291946308724833,\n \"em_stderr\": 0.0024866840810898265,\n \"f1\": 0.1230484479865771,\n \"f1_stderr\": 0.002835124699649658,\n \"acc\": 0.3855268126040811,\n \"acc_stderr\": 0.00961257569233847\n },\n \"harness|drop|3\": {\n \"em\": 0.06291946308724833,\n \"em_stderr\": 0.0024866840810898265,\n \"f1\": 0.1230484479865771,\n \"f1_stderr\": 0.002835124699649658\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05913570887035633,\n \"acc_stderr\": 0.006497266660428824\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7119179163378059,\n \"acc_stderr\": 0.012727884724248116\n }\n}\n```", "repo_url": "https://huggingface.co/vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_16T15_28_08.035226", "path": ["**/details_harness|arc:challenge|25_2023-09-16T15-28-08.035226.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-16T15-28-08.035226.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T12_14_06.343612", "path": ["**/details_harness|drop|3_2023-10-23T12-14-06.343612.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T12-14-06.343612.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T12_14_06.343612", "path": ["**/details_harness|gsm8k|5_2023-10-23T12-14-06.343612.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T12-14-06.343612.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_16T15_28_08.035226", "path": ["**/details_harness|hellaswag|10_2023-09-16T15-28-08.035226.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-16T15-28-08.035226.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_16T15_28_08.035226", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-16T15-28-08.035226.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-16T15-28-08.035226.parquet", 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|
2023-10-23T11:14:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-23T12:14:06.343612(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T12:14:06.343612(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"# Dataset Card for Evaluation run of vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-23T12:14:06.343612(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model vibhorag101/llama-2-7b-chat-hf-phr_mental_health-2048 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-23T12:14:06.343612(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
a6a01a0ab7fea9c739cf3c1694a54b78917b1882
|
# Dataset of kasuga_mirai/春日未来/카스가미라이 (THE iDOLM@STER: Million Live!)
This is the dataset of kasuga_mirai/春日未来/카스가미라이 (THE iDOLM@STER: Million Live!), containing 330 images and their tags.
The core tags of this character are `brown_hair, hair_ornament, one_side_up, hairclip, brown_eyes, short_hair, bangs, breasts, scrunchie, yellow_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 | 330 | 370.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kasuga_mirai_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 330 | 226.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kasuga_mirai_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 766 | 475.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kasuga_mirai_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 330 | 328.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kasuga_mirai_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 766 | 647.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kasuga_mirai_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kasuga_mirai_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, solo, :d, blush, wrist_cuffs, frills, hair_bow, skirt, star_(symbol), necktie, sleeveless |
| 1 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, solo, bracelet, skirt, midriff, microphone, navel, :d, ;d, belt, crown, one_eye_closed |
| 2 | 7 |  |  |  |  |  | looking_at_viewer, neck_ribbon, open_mouth, 1girl, blush, hair_between_eyes, hair_scrunchie, long_sleeves, red_ribbon, school_uniform, simple_background, solo, white_shirt, collared_shirt, collarbone, upper_body, white_background, :d, cowboy_shot, miniskirt, pink_cardigan, plaid_skirt, pleated_skirt, red_skirt |
| 3 | 16 |  |  |  |  |  | 1girl, navel, blush, collarbone, solo, looking_at_viewer, cleavage, medium_breasts, hair_between_eyes, open_mouth, smile, hair_scrunchie, necklace, bare_shoulders, blue_bikini, bracelet, bow, frilled_bikini, medium_hair, standing, day, floral_print, halterneck, outdoors, stomach, wet |
| 4 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_panties, hair_scrunchie, open_mouth, small_breasts, :d, blush, bow, cleavage, simple_background, underwear_only, white_bra, cowboy_shot, medium_breasts, navel, white_background |
| 5 | 5 |  |  |  |  |  | blush, open_mouth, 2girls, :d, ^_^, hair_scrunchie, solo_focus, 1girl, hug, jacket, pink_scrunchie, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | open_mouth | solo | :d | blush | wrist_cuffs | frills | hair_bow | skirt | star_(symbol) | necktie | sleeveless | bracelet | midriff | microphone | navel | ;d | belt | crown | one_eye_closed | neck_ribbon | hair_between_eyes | hair_scrunchie | long_sleeves | red_ribbon | school_uniform | simple_background | white_shirt | collared_shirt | collarbone | upper_body | white_background | cowboy_shot | miniskirt | pink_cardigan | plaid_skirt | pleated_skirt | red_skirt | cleavage | medium_breasts | smile | necklace | bare_shoulders | blue_bikini | bow | frilled_bikini | medium_hair | standing | day | floral_print | halterneck | outdoors | stomach | wet | white_panties | small_breasts | underwear_only | white_bra | 2girls | ^_^ | solo_focus | hug | jacket | pink_scrunchie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------------|:-------|:-----|:--------|:--------------|:---------|:-----------|:--------|:----------------|:----------|:-------------|:-----------|:----------|:-------------|:--------|:-----|:-------|:--------|:-----------------|:--------------|:--------------------|:-----------------|:---------------|:-------------|:-----------------|:--------------------|:--------------|:-----------------|:-------------|:-------------|:-------------------|:--------------|:------------|:----------------|:--------------|:----------------|:------------|:-----------|:-----------------|:--------|:-----------|:-----------------|:--------------|:------|:-----------------|:--------------|:-----------|:------|:---------------|:-------------|:-----------|:----------|:------|:----------------|:----------------|:-----------------|:------------|:---------|:------|:-------------|:------|:---------|:-----------------|
| 0 | 7 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | X | 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 | 5 |  |  |  |  |  | X | | X | | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
|
CyberHarem/kasuga_mirai_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T14:36:08+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T00:09:10+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kasuga\_mirai/春日未来/카스가미라이 (THE iDOLM@STER: Million Live!)
====================================================================
This is the dataset of kasuga\_mirai/春日未来/카스가미라이 (THE iDOLM@STER: Million Live!), containing 330 images and their tags.
The core tags of this character are 'brown\_hair, hair\_ornament, one\_side\_up, hairclip, brown\_eyes, short\_hair, bangs, breasts, scrunchie, yellow\_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"
] |
ea117f34cd713da3e1baa26ef7cb89ad583461bd
|
# Dataset of julia (THE iDOLM@STER: Million Live!)
This is the dataset of julia (THE iDOLM@STER: Million Live!), containing 172 images and their tags.
The core tags of this character are `short_hair, red_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 | 172 | 196.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/julia_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 172 | 127.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/julia_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 379 | 247.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/julia_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 172 | 175.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/julia_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 379 | 323.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/julia_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/julia_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, navel, solo, cleavage, collarbone, looking_at_viewer, medium_breasts, open_mouth, outdoors, blush, day, necklace, white_bikini, earrings, hair_between_eyes, hair_flower, smile, star_(symbol), cowboy_shot, frilled_bikini, front-tie_bikini_top, hibiscus, sky, straw_hat |
| 1 | 6 |  |  |  |  |  | 1girl, kimono, smile, solo, looking_at_viewer, hair_flower, blush, brown_hair, cherry_blossoms, petals |
| 2 | 14 |  |  |  |  |  | 1girl, electric_guitar, smile, solo, looking_at_viewer, star_(symbol), character_name, choker, plectrum, skirt, bracelet |
| 3 | 10 |  |  |  |  |  | 1girl, open_mouth, brown_hair, :d, skirt, choker, looking_at_viewer, solo, blush, dress, heart |
| 4 | 9 |  |  |  |  |  | 1girl, solo, blush, collarbone, looking_at_viewer, hair_between_eyes, bangs, breasts, upper_body, jewelry, open_mouth, smile, white_background, shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | solo | cleavage | collarbone | looking_at_viewer | medium_breasts | open_mouth | outdoors | blush | day | necklace | white_bikini | earrings | hair_between_eyes | hair_flower | smile | star_(symbol) | cowboy_shot | frilled_bikini | front-tie_bikini_top | hibiscus | sky | straw_hat | kimono | brown_hair | cherry_blossoms | petals | electric_guitar | character_name | choker | plectrum | skirt | bracelet | :d | dress | heart | bangs | breasts | upper_body | jewelry | white_background | shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-----------|:-------------|:--------------------|:-----------------|:-------------|:-----------|:--------|:------|:-----------|:---------------|:-----------|:--------------------|:--------------|:--------|:----------------|:--------------|:-----------------|:-----------------------|:-----------|:------|:------------|:---------|:-------------|:------------------|:---------|:------------------|:-----------------|:---------|:-----------|:--------|:-----------|:-----|:--------|:--------|:--------|:----------|:-------------|:----------|:-------------------|:--------|
| 0 | 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 | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | X | | | X | | | | X | | | | | | X | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | | X | | | X | | | | | | | | | | | X | X | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | 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 |
|
CyberHarem/julia_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T14:57:01+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T03:21:50+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of julia (THE iDOLM@STER: Million Live!)
================================================
This is the dataset of julia (THE iDOLM@STER: Million Live!), containing 172 images and their tags.
The core tags of this character are 'short\_hair, red\_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"
] |
80a67c51c9b52dcbd7506e3075cb6e9aad9d4bdc
|
# Dataset Card for "pq_test2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fmattera/pq_test2
|
[
"region:us"
] |
2023-09-16T15:11:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "file_name", "dtype": "string"}, {"name": "conditioning", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 158, "num_examples": 1}], "download_size": 0, "dataset_size": 158}}
|
2023-09-16T15:11:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pq_test2"
More Information needed
|
[
"# Dataset Card for \"pq_test2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pq_test2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pq_test2\"\n\nMore Information needed"
] |
8b20608f2a04c92bebd6023a3de630eafbc69708
|
# Dataset Card for "claim_detection_paper_test_roberta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/claim_detection_paper_test_roberta
|
[
"region:us"
] |
2023-09-16T16:05:57+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "string"}, {"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "Is_Claim", "dtype": "string"}, {"name": "Claim_detection_result", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1175913, "num_examples": 11073}], "download_size": 0, "dataset_size": 1175913}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T16:18:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "claim_detection_paper_test_roberta"
More Information needed
|
[
"# Dataset Card for \"claim_detection_paper_test_roberta\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"claim_detection_paper_test_roberta\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"claim_detection_paper_test_roberta\"\n\nMore Information needed"
] |
99f0231ba63f3a86b5330bb0eabd1923737c4b01
|
# Dataset of takayama_sayoko/高山紗代子 (THE iDOLM@STER: Million Live!)
This is the dataset of takayama_sayoko/高山紗代子 (THE iDOLM@STER: Million Live!), containing 255 images and their tags.
The core tags of this character are `long_hair, black_hair, red_eyes, bangs, breasts, glasses`, which are 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 | 255 | 345.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takayama_sayoko_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 255 | 198.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takayama_sayoko_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 605 | 426.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takayama_sayoko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 255 | 305.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takayama_sayoko_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 605 | 609.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takayama_sayoko_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/takayama_sayoko_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, smile, solo, blue_sky, day, looking_at_viewer, navel, open_mouth, outdoors, blush, cleavage, side-tie_bikini_bottom, yellow_bikini, armband, beach, cloud, front-tie_top, medium_breasts, necklace, striped_bikini, visor_cap |
| 1 | 16 |  |  |  |  |  | 1girl, solo, looking_at_viewer, smile, blush, open_mouth, dress, hat, black_gloves |
| 2 | 7 |  |  |  |  |  | 1girl, solo, white_headwear, blue_skirt, blush, holding, looking_at_viewer, megaphone, short_shorts, smile, white_gloves, white_shorts, brown_eyes, pleated_skirt, shorts_under_skirt, sleeveless_shirt, white_shirt, bare_shoulders, beret, open_mouth, parted_bangs, red_bow, very_long_hair, white_background, white_sailor_collar, closed_mouth, medium_breasts, simple_background |
| 3 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, school_uniform, twintails, blush, open_mouth, bow, :d, skirt |
| 4 | 10 |  |  |  |  |  | 1girl, pleated_skirt, solo, low_twintails, white_shirt, blush, grey_skirt, plaid_skirt, black-framed_eyewear, looking_at_viewer, open_mouth, puffy_short_sleeves, sailor_collar, serafuku, :d, pink_bow, bowtie, brown_eyes, kneehighs |
| 5 | 6 |  |  |  |  |  | 1girl, black_gloves, black_shorts, fingerless_gloves, looking_at_viewer, midriff, smile, solo, black_jacket, blush, crop_top, navel, ponytail, belt, hair_ornament, holding, open_jacket, shirt, short_shorts, sidelocks, cleavage, clothing_cutout, cowboy_shot, long_sleeves, medium_breasts, open_mouth, stomach, sweat |
| 6 | 7 |  |  |  |  |  | detached_collar, playboy_bunny, rabbit_ears, 1girl, cleavage, fake_animal_ears, looking_at_viewer, rabbit_tail, simple_background, white_background, wrist_cuffs, bare_shoulders, blush, medium_breasts, solo, strapless_leotard, black_leotard, red_bowtie, black_pantyhose, closed_mouth, collarbone, full_body, hair_ornament, high_heels, holding, open_mouth, smile, white_footwear, white_leotard |
| 7 | 6 |  |  |  |  |  | 1girl, blush, hetero, nipples, open_mouth, sex, twintails, vaginal, 1boy, penis, solo_focus, female_pubic_hair, medium_breasts, bra, clothes_lift, cowgirl_position, cum_in_pussy, girl_on_top, mosaic_censoring, navel, nude, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | blue_sky | day | looking_at_viewer | navel | open_mouth | outdoors | blush | cleavage | side-tie_bikini_bottom | yellow_bikini | armband | beach | cloud | front-tie_top | medium_breasts | necklace | striped_bikini | visor_cap | dress | hat | black_gloves | white_headwear | blue_skirt | holding | megaphone | short_shorts | white_gloves | white_shorts | brown_eyes | pleated_skirt | shorts_under_skirt | sleeveless_shirt | white_shirt | bare_shoulders | beret | parted_bangs | red_bow | very_long_hair | white_background | white_sailor_collar | closed_mouth | simple_background | school_uniform | twintails | bow | :d | skirt | low_twintails | grey_skirt | plaid_skirt | black-framed_eyewear | puffy_short_sleeves | sailor_collar | serafuku | pink_bow | bowtie | kneehighs | black_shorts | fingerless_gloves | midriff | black_jacket | crop_top | ponytail | belt | hair_ornament | open_jacket | shirt | sidelocks | clothing_cutout | cowboy_shot | long_sleeves | stomach | sweat | detached_collar | playboy_bunny | rabbit_ears | fake_animal_ears | rabbit_tail | wrist_cuffs | strapless_leotard | black_leotard | red_bowtie | black_pantyhose | collarbone | full_body | high_heels | white_footwear | white_leotard | hetero | nipples | sex | vaginal | 1boy | penis | solo_focus | female_pubic_hair | bra | clothes_lift | cowgirl_position | cum_in_pussy | girl_on_top | mosaic_censoring | nude |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-----------|:------|:--------------------|:--------|:-------------|:-----------|:--------|:-----------|:-------------------------|:----------------|:----------|:--------|:--------|:----------------|:-----------------|:-----------|:-----------------|:------------|:--------|:------|:---------------|:-----------------|:-------------|:----------|:------------|:---------------|:---------------|:---------------|:-------------|:----------------|:---------------------|:-------------------|:--------------|:-----------------|:--------|:---------------|:----------|:-----------------|:-------------------|:----------------------|:---------------|:--------------------|:-----------------|:------------|:------|:-----|:--------|:----------------|:-------------|:--------------|:-----------------------|:----------------------|:----------------|:-----------|:-----------|:---------|:------------|:---------------|:--------------------|:----------|:---------------|:-----------|:-----------|:-------|:----------------|:--------------|:--------|:------------|:------------------|:--------------|:---------------|:----------|:--------|:------------------|:----------------|:--------------|:-------------------|:--------------|:--------------|:--------------------|:----------------|:-------------|:------------------|:-------------|:------------|:-------------|:-----------------|:----------------|:---------|:----------|:------|:----------|:-------|:--------|:-------------|:--------------------|:------|:---------------|:-------------------|:---------------|:--------------|:-------------------|:-------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 16 |  |  |  |  |  | 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 | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 13 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 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 | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | | | | | X | X | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/takayama_sayoko_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T16:17:30+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T01:42:37+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of takayama\_sayoko/高山紗代子 (THE iDOLM@STER: Million Live!)
=================================================================
This is the dataset of takayama\_sayoko/高山紗代子 (THE iDOLM@STER: Million Live!), containing 255 images and their tags.
The core tags of this character are 'long\_hair, black\_hair, red\_eyes, bangs, breasts, glasses', which are 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"
] |
8fb2a3d3957a9d40b960e1b92ad1bdd3571e53f0
|
# Polylingual Indonesia Dataset/Model Card
## Description
Polylingual Indonesia is a diverse dataset composed of a collection of publicly available data and some self-crawled data in the Indonesian language. The data originates from various sources including news articles, books, forums, and stories. This dataset is processed and filtered through specific techniques detailed in the sections below.
## Prerequisites
To run and utilize this dataset, make sure you have the `zstandard` package installed in your environment.
## Format
The original format of the dataset is JSONL with zstandard compressed
## Dataset Details
### Filtering
The dataset undergoes a filtration process using a specific filter from BigScience. Detailed information about the filter used can be found here: [BigScience Filter](https://drive.google.com/file/d/1cCJ8sWE88TRLDAa3eHLmXO4JlkR2QzLY/view?usp=sharing).
### Data Sources
The data originates from various sources, and each folder in the dataset represents the source from where the original data came. Here are the details of each folder/source:
#### 1. HPLT_filtered
- Link: [HPLT Project](https://hplt-project.org/datasets/v1)
- Source : Internet Archive Snapshot WIDE15 and WIDE17 and CC-MAIN-2022-40
#### 2. Mc4-und-id
- Data is filtered from the undefined language segment of MC4 (c4-und) using FastText, BigScience filters.
- Sample: [MC4 Sample](https://huggingface.co/datasets/allenai/c4/blob/mC4310/multilingual/c4-und.00000-00001-00002-00003-00004-00005-00006-00007.json.gz)
#### 3. Indonesia-Crawl
This folder contains a collection of the Common Crawl dataset and self crawled data, specific for the Indonesian language, accumulated from various snapshots. The data is divided into several sections:
- **Mc4 original(dedup)**: Ranging from snapshot CC-2013-20 to CC-2020-34.
- **Kopi-CC(dedup)**: This covers data from snapshot CC-2020-34 to CC-2023-06. More details can be found [here](https://huggingface.co/datasets/acul3/KoPI-CC) (Note: Last snapshot to be uploaded).
- **KoPI-CC_News**: This includes the Common Crawl News Dataset ranging from the year 2016 to 2022. Detailed information can be accessed [here](https://huggingface.co/datasets/acul3/KoPI-CC) (Note: Last snapshot to be uploaded).
- **Self Crawled Data**: consists of data crawled from various platforms including news sites, story sites, forums, and others.
## Usage
install zstandard first
```
from datasets import load_dataset
hplt = load_dataset('duckaiml/Polylingual_Id','hplt') #hplt only
mc4_und = load_dataset('duckaiml/Polylingual_Id','mc4_und') #mc4_und only
indonesia_crawl = load_dataset('duckaiml/Polylingual_Id','indonesia_crawl') #indonesia_crawl only
load_dataset('duckaiml/Polylingual_Id','full') #load all
```
## Limitation/Issue
- Although some source data already dedup separately, some duplicate might be found as dataset need dedup as whole dataset
## Contributing
Feel free to contribute to the dataset by adding more diverse sources or helping in enhancing the filtration process.
## License
The data is collected from public sources, and it's recommended to refer to the original data sources for information on licensing and usage restrictions.
## Contact
For any queries or contributions to the dataset, please feel free to reach out (contact information to be added).
|
duckaiml/Polylingual_Id
|
[
"license:other",
"region:us"
] |
2023-09-16T16:33:13+00:00
|
{"license": "other"}
|
2023-10-12T15:58:37+00:00
|
[] |
[] |
TAGS
#license-other #region-us
|
# Polylingual Indonesia Dataset/Model Card
## Description
Polylingual Indonesia is a diverse dataset composed of a collection of publicly available data and some self-crawled data in the Indonesian language. The data originates from various sources including news articles, books, forums, and stories. This dataset is processed and filtered through specific techniques detailed in the sections below.
## Prerequisites
To run and utilize this dataset, make sure you have the 'zstandard' package installed in your environment.
## Format
The original format of the dataset is JSONL with zstandard compressed
## Dataset Details
### Filtering
The dataset undergoes a filtration process using a specific filter from BigScience. Detailed information about the filter used can be found here: BigScience Filter.
### Data Sources
The data originates from various sources, and each folder in the dataset represents the source from where the original data came. Here are the details of each folder/source:
#### 1. HPLT_filtered
- Link: HPLT Project
- Source : Internet Archive Snapshot WIDE15 and WIDE17 and CC-MAIN-2022-40
#### 2. Mc4-und-id
- Data is filtered from the undefined language segment of MC4 (c4-und) using FastText, BigScience filters.
- Sample: MC4 Sample
#### 3. Indonesia-Crawl
This folder contains a collection of the Common Crawl dataset and self crawled data, specific for the Indonesian language, accumulated from various snapshots. The data is divided into several sections:
- Mc4 original(dedup): Ranging from snapshot CC-2013-20 to CC-2020-34.
- Kopi-CC(dedup): This covers data from snapshot CC-2020-34 to CC-2023-06. More details can be found here (Note: Last snapshot to be uploaded).
- KoPI-CC_News: This includes the Common Crawl News Dataset ranging from the year 2016 to 2022. Detailed information can be accessed here (Note: Last snapshot to be uploaded).
- Self Crawled Data: consists of data crawled from various platforms including news sites, story sites, forums, and others.
## Usage
install zstandard first
## Limitation/Issue
- Although some source data already dedup separately, some duplicate might be found as dataset need dedup as whole dataset
## Contributing
Feel free to contribute to the dataset by adding more diverse sources or helping in enhancing the filtration process.
## License
The data is collected from public sources, and it's recommended to refer to the original data sources for information on licensing and usage restrictions.
## Contact
For any queries or contributions to the dataset, please feel free to reach out (contact information to be added).
|
[
"# Polylingual Indonesia Dataset/Model Card",
"## Description\n\nPolylingual Indonesia is a diverse dataset composed of a collection of publicly available data and some self-crawled data in the Indonesian language. The data originates from various sources including news articles, books, forums, and stories. This dataset is processed and filtered through specific techniques detailed in the sections below.",
"## Prerequisites\n\nTo run and utilize this dataset, make sure you have the 'zstandard' package installed in your environment.",
"## Format\n\nThe original format of the dataset is JSONL with zstandard compressed",
"## Dataset Details",
"### Filtering\n\nThe dataset undergoes a filtration process using a specific filter from BigScience. Detailed information about the filter used can be found here: BigScience Filter.",
"### Data Sources\n\nThe data originates from various sources, and each folder in the dataset represents the source from where the original data came. Here are the details of each folder/source:",
"#### 1. HPLT_filtered\n- Link: HPLT Project\n- Source : Internet Archive Snapshot WIDE15 and WIDE17 and CC-MAIN-2022-40",
"#### 2. Mc4-und-id\n- Data is filtered from the undefined language segment of MC4 (c4-und) using FastText, BigScience filters.\n- Sample: MC4 Sample",
"#### 3. Indonesia-Crawl\nThis folder contains a collection of the Common Crawl dataset and self crawled data, specific for the Indonesian language, accumulated from various snapshots. The data is divided into several sections:\n\n- Mc4 original(dedup): Ranging from snapshot CC-2013-20 to CC-2020-34.\n- Kopi-CC(dedup): This covers data from snapshot CC-2020-34 to CC-2023-06. More details can be found here (Note: Last snapshot to be uploaded).\n- KoPI-CC_News: This includes the Common Crawl News Dataset ranging from the year 2016 to 2022. Detailed information can be accessed here (Note: Last snapshot to be uploaded).\n- Self Crawled Data: consists of data crawled from various platforms including news sites, story sites, forums, and others.",
"## Usage\n\ninstall zstandard first",
"## Limitation/Issue\n- Although some source data already dedup separately, some duplicate might be found as dataset need dedup as whole dataset",
"## Contributing\n\nFeel free to contribute to the dataset by adding more diverse sources or helping in enhancing the filtration process.",
"## License\n\nThe data is collected from public sources, and it's recommended to refer to the original data sources for information on licensing and usage restrictions.",
"## Contact\n\nFor any queries or contributions to the dataset, please feel free to reach out (contact information to be added)."
] |
[
"TAGS\n#license-other #region-us \n",
"# Polylingual Indonesia Dataset/Model Card",
"## Description\n\nPolylingual Indonesia is a diverse dataset composed of a collection of publicly available data and some self-crawled data in the Indonesian language. The data originates from various sources including news articles, books, forums, and stories. This dataset is processed and filtered through specific techniques detailed in the sections below.",
"## Prerequisites\n\nTo run and utilize this dataset, make sure you have the 'zstandard' package installed in your environment.",
"## Format\n\nThe original format of the dataset is JSONL with zstandard compressed",
"## Dataset Details",
"### Filtering\n\nThe dataset undergoes a filtration process using a specific filter from BigScience. Detailed information about the filter used can be found here: BigScience Filter.",
"### Data Sources\n\nThe data originates from various sources, and each folder in the dataset represents the source from where the original data came. Here are the details of each folder/source:",
"#### 1. HPLT_filtered\n- Link: HPLT Project\n- Source : Internet Archive Snapshot WIDE15 and WIDE17 and CC-MAIN-2022-40",
"#### 2. Mc4-und-id\n- Data is filtered from the undefined language segment of MC4 (c4-und) using FastText, BigScience filters.\n- Sample: MC4 Sample",
"#### 3. Indonesia-Crawl\nThis folder contains a collection of the Common Crawl dataset and self crawled data, specific for the Indonesian language, accumulated from various snapshots. The data is divided into several sections:\n\n- Mc4 original(dedup): Ranging from snapshot CC-2013-20 to CC-2020-34.\n- Kopi-CC(dedup): This covers data from snapshot CC-2020-34 to CC-2023-06. More details can be found here (Note: Last snapshot to be uploaded).\n- KoPI-CC_News: This includes the Common Crawl News Dataset ranging from the year 2016 to 2022. Detailed information can be accessed here (Note: Last snapshot to be uploaded).\n- Self Crawled Data: consists of data crawled from various platforms including news sites, story sites, forums, and others.",
"## Usage\n\ninstall zstandard first",
"## Limitation/Issue\n- Although some source data already dedup separately, some duplicate might be found as dataset need dedup as whole dataset",
"## Contributing\n\nFeel free to contribute to the dataset by adding more diverse sources or helping in enhancing the filtration process.",
"## License\n\nThe data is collected from public sources, and it's recommended to refer to the original data sources for information on licensing and usage restrictions.",
"## Contact\n\nFor any queries or contributions to the dataset, please feel free to reach out (contact information to be added)."
] |
[
11,
10,
73,
29,
19,
4,
38,
41,
36,
45,
189,
7,
36,
28,
34,
27
] |
[
"passage: TAGS\n#license-other #region-us \n# Polylingual Indonesia Dataset/Model Card## Description\n\nPolylingual Indonesia is a diverse dataset composed of a collection of publicly available data and some self-crawled data in the Indonesian language. The data originates from various sources including news articles, books, forums, and stories. This dataset is processed and filtered through specific techniques detailed in the sections below.## Prerequisites\n\nTo run and utilize this dataset, make sure you have the 'zstandard' package installed in your environment.## Format\n\nThe original format of the dataset is JSONL with zstandard compressed## Dataset Details### Filtering\n\nThe dataset undergoes a filtration process using a specific filter from BigScience. Detailed information about the filter used can be found here: BigScience Filter.### Data Sources\n\nThe data originates from various sources, and each folder in the dataset represents the source from where the original data came. Here are the details of each folder/source:#### 1. HPLT_filtered\n- Link: HPLT Project\n- Source : Internet Archive Snapshot WIDE15 and WIDE17 and CC-MAIN-2022-40#### 2. Mc4-und-id\n- Data is filtered from the undefined language segment of MC4 (c4-und) using FastText, BigScience filters.\n- Sample: MC4 Sample#### 3. Indonesia-Crawl\nThis folder contains a collection of the Common Crawl dataset and self crawled data, specific for the Indonesian language, accumulated from various snapshots. The data is divided into several sections:\n\n- Mc4 original(dedup): Ranging from snapshot CC-2013-20 to CC-2020-34.\n- Kopi-CC(dedup): This covers data from snapshot CC-2020-34 to CC-2023-06. More details can be found here (Note: Last snapshot to be uploaded).\n- KoPI-CC_News: This includes the Common Crawl News Dataset ranging from the year 2016 to 2022. Detailed information can be accessed here (Note: Last snapshot to be uploaded).\n- Self Crawled Data: consists of data crawled from various platforms including news sites, story sites, forums, and others.## Usage\n\ninstall zstandard first"
] |
4953e137eb9af6cdbdabfc066a2150e0d200747b
|
# Dataset of nakatani_iku/中谷育 (THE iDOLM@STER: Million Live!)
This is the dataset of nakatani_iku/中谷育 (THE iDOLM@STER: Million Live!), containing 203 images and their tags.
The core tags of this character are `black_hair, short_hair, brown_eyes, bangs, one_side_up, 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 | 203 | 181.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakatani_iku_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 203 | 129.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakatani_iku_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 439 | 251.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakatani_iku_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 203 | 167.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakatani_iku_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 439 | 320.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nakatani_iku_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nakatani_iku_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, open_mouth, solo, :d, looking_at_viewer, bow, hair_bobbles, dress, blush |
| 1 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, open_mouth, solo, :d, loli, flat_chest, hair_bobbles, micro_bikini |
| 2 | 5 |  |  |  |  |  | 1girl, blush, long_sleeves, looking_at_viewer, pink_dress, purple_bow, solo, white_headwear, beret, frills, fur-trimmed_dress, fur-trimmed_sleeves, pink_bow, pink_jacket, pom_pom_(clothes), white_gloves, :d, blue_bowtie, blue_thighhighs, boots, fingerless_gloves, hat_bow, open_mouth, simple_background, standing, striped, white_background, white_footwear, white_thighhighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | solo | :d | looking_at_viewer | bow | hair_bobbles | dress | blush | navel | loli | flat_chest | micro_bikini | long_sleeves | pink_dress | purple_bow | white_headwear | beret | frills | fur-trimmed_dress | fur-trimmed_sleeves | pink_bow | pink_jacket | pom_pom_(clothes) | white_gloves | blue_bowtie | blue_thighhighs | boots | fingerless_gloves | hat_bow | simple_background | standing | striped | white_background | white_footwear | white_thighhighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------|:-----|:--------------------|:------|:---------------|:--------|:--------|:--------|:-------|:-------------|:---------------|:---------------|:-------------|:-------------|:-----------------|:--------|:---------|:--------------------|:----------------------|:-----------|:--------------|:--------------------|:---------------|:--------------|:------------------|:--------|:--------------------|:----------|:--------------------|:-----------|:----------|:-------------------|:-----------------|:-------------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | 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 | X | X | X | X |
|
CyberHarem/nakatani_iku_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T16:35:08+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:59:36+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of nakatani\_iku/中谷育 (THE iDOLM@STER: Million Live!)
============================================================
This is the dataset of nakatani\_iku/中谷育 (THE iDOLM@STER: Million Live!), containing 203 images and their tags.
The core tags of this character are 'black\_hair, short\_hair, brown\_eyes, bangs, one\_side\_up, 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"
] |
a0853bfac491d105bc8a74ea87cf6d5107c0b8da
|
# Dataset Card for "dmae-dataset-DA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Augusto777/dmae-dataset-DA
|
[
"region:us"
] |
2023-09-16T17:16:41+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "avanzada", "1": "leve", "2": "moderada", "3": "no amd"}}}}], "splits": [{"name": "validation", "num_bytes": 146781.0, "num_examples": 16}, {"name": "test", "num_bytes": 153500.0, "num_examples": 16}, {"name": "train", "num_bytes": 1578165.0, "num_examples": 200}], "download_size": 1838517, "dataset_size": 1878446.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}]}
|
2023-09-16T17:25:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dmae-dataset-DA"
More Information needed
|
[
"# Dataset Card for \"dmae-dataset-DA\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dmae-dataset-DA\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dmae-dataset-DA\"\n\nMore Information needed"
] |
ae8ba04d9172af2961987e1bfe20f99aef0d5577
|
# Dataset of Yuna
This is the dataset of Yuna, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 615 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 615 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 615 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 615 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/yuna_kumakumakumabear
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T17:17:52+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:43:02+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Yuna
===============
This is the dataset of Yuna, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
c29494456145705912482c94d85236624b48d11c
|
# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [lvkaokao/llama2-7b-hf-chat-lora](https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T18:19:09.096561](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora/blob/main/results_2023-09-16T18-19-09.096561.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094556,
"f1": 0.06527894295302021,
"f1_stderr": 0.0014475102232856358,
"acc": 0.433070962730968,
"acc_stderr": 0.010283233892517613
},
"harness|drop|3": {
"em": 0.002307046979865772,
"em_stderr": 0.0004913221265094556,
"f1": 0.06527894295302021,
"f1_stderr": 0.0014475102232856358
},
"harness|gsm8k|5": {
"acc": 0.10765731614859743,
"acc_stderr": 0.008537484003023352
},
"harness|winogrande|5": {
"acc": 0.7584846093133386,
"acc_stderr": 0.012028983782011874
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora
|
[
"region:us"
] |
2023-09-16T17:19:12+00:00
|
{"pretty_name": "Evaluation run of lvkaokao/llama2-7b-hf-chat-lora", "dataset_summary": "Dataset automatically created during the evaluation run of model [lvkaokao/llama2-7b-hf-chat-lora](https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-16T18:19:09.096561](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora/blob/main/results_2023-09-16T18-19-09.096561.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094556,\n \"f1\": 0.06527894295302021,\n \"f1_stderr\": 0.0014475102232856358,\n \"acc\": 0.433070962730968,\n \"acc_stderr\": 0.010283233892517613\n },\n \"harness|drop|3\": {\n \"em\": 0.002307046979865772,\n \"em_stderr\": 0.0004913221265094556,\n \"f1\": 0.06527894295302021,\n \"f1_stderr\": 0.0014475102232856358\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10765731614859743,\n \"acc_stderr\": 0.008537484003023352\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7584846093133386,\n \"acc_stderr\": 0.012028983782011874\n }\n}\n```", "repo_url": "https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_16T18_19_09.096561", "path": ["**/details_harness|drop|3_2023-09-16T18-19-09.096561.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-16T18-19-09.096561.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_16T18_19_09.096561", "path": ["**/details_harness|gsm8k|5_2023-09-16T18-19-09.096561.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-16T18-19-09.096561.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_16T18_19_09.096561", "path": ["**/details_harness|winogrande|5_2023-09-16T18-19-09.096561.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-16T18-19-09.096561.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_16T18_19_09.096561", "path": ["results_2023-09-16T18-19-09.096561.parquet"]}, {"split": "latest", "path": ["results_2023-09-16T18-19-09.096561.parquet"]}]}]}
|
2023-09-16T17:19:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-09-16T18:19:09.096561(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T18:19:09.096561(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T18:19:09.096561(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-16T18:19:09.096561(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
f5d905b33d7417d78c00967ba42184df1ce6d9ea
|
# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-124M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MBZUAI/LaMini-GPT-124M
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [MBZUAI/LaMini-GPT-124M](https://huggingface.co/MBZUAI/LaMini-GPT-124M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MBZUAI__LaMini-GPT-124M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T18:36:34.459500](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-124M/blob/main/results_2023-09-16T18-36-34.459500.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.012269295302013422,
"em_stderr": 0.0011273758781873528,
"f1": 0.07700503355704716,
"f1_stderr": 0.001885786848498622,
"acc": 0.2569060773480663,
"acc_stderr": 0.007023561458220208
},
"harness|drop|3": {
"em": 0.012269295302013422,
"em_stderr": 0.0011273758781873528,
"f1": 0.07700503355704716,
"f1_stderr": 0.001885786848498622
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5138121546961326,
"acc_stderr": 0.014047122916440415
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_MBZUAI__LaMini-GPT-124M
|
[
"region:us"
] |
2023-09-16T17:36:37+00:00
|
{"pretty_name": "Evaluation run of MBZUAI/LaMini-GPT-124M", "dataset_summary": "Dataset automatically created during the evaluation run of model [MBZUAI/LaMini-GPT-124M](https://huggingface.co/MBZUAI/LaMini-GPT-124M) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MBZUAI__LaMini-GPT-124M\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-16T18:36:34.459500](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__LaMini-GPT-124M/blob/main/results_2023-09-16T18-36-34.459500.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.012269295302013422,\n \"em_stderr\": 0.0011273758781873528,\n \"f1\": 0.07700503355704716,\n \"f1_stderr\": 0.001885786848498622,\n \"acc\": 0.2569060773480663,\n \"acc_stderr\": 0.007023561458220208\n },\n \"harness|drop|3\": {\n \"em\": 0.012269295302013422,\n \"em_stderr\": 0.0011273758781873528,\n \"f1\": 0.07700503355704716,\n \"f1_stderr\": 0.001885786848498622\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5138121546961326,\n \"acc_stderr\": 0.014047122916440415\n }\n}\n```", "repo_url": "https://huggingface.co/MBZUAI/LaMini-GPT-124M", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_16T18_36_34.459500", "path": ["**/details_harness|drop|3_2023-09-16T18-36-34.459500.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-16T18-36-34.459500.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_16T18_36_34.459500", "path": ["**/details_harness|gsm8k|5_2023-09-16T18-36-34.459500.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-16T18-36-34.459500.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_16T18_36_34.459500", "path": ["**/details_harness|winogrande|5_2023-09-16T18-36-34.459500.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-16T18-36-34.459500.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_16T18_36_34.459500", "path": ["results_2023-09-16T18-36-34.459500.parquet"]}, {"split": "latest", "path": ["results_2023-09-16T18-36-34.459500.parquet"]}]}]}
|
2023-09-16T17:36:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-124M
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model MBZUAI/LaMini-GPT-124M on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-09-16T18:36:34.459500(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-124M",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model MBZUAI/LaMini-GPT-124M on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T18:36:34.459500(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-124M",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model MBZUAI/LaMini-GPT-124M on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T18:36:34.459500(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
21,
31,
169,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of MBZUAI/LaMini-GPT-124M## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model MBZUAI/LaMini-GPT-124M on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-16T18:36:34.459500(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
6205df286aabccf44a627cb827e25fa0eb6ba1ad
|
# Dataset of Fina
This is the dataset of Fina, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 609 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 609 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 609 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 609 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/fina_kumakumakumabear
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T17:45:47+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:43:04+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Fina
===============
This is the dataset of Fina, containing 300 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
8371e99020f637ad29fd9a8499bd6058d8faa896
|
# Dataset of oogami_tamaki/大神環 (THE iDOLM@STER: Million Live!)
This is the dataset of oogami_tamaki/大神環 (THE iDOLM@STER: Million Live!), containing 183 images and their tags.
The core tags of this character are `long_hair, one_side_up, orange_hair, brown_hair, brown_eyes, 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 | 183 | 192.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oogami_tamaki_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 183 | 124.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oogami_tamaki_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 405 | 245.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oogami_tamaki_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 183 | 173.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oogami_tamaki_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 405 | 330.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/oogami_tamaki_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/oogami_tamaki_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, :d, looking_at_viewer, open_mouth, solo, blush, collarbone, small_breasts, orange_eyes, simple_background, upper_body, white_background, navel, nipples |
| 1 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, :d, open_mouth, orange_eyes, simple_background, white_background, white_shirt, short_sleeves, upper_body, bow, collared_shirt, necktie, shorts, vest |
| 2 | 15 |  |  |  |  |  | 1girl, :d, open_mouth, denim_shorts, solo, looking_at_viewer, blush, cutoffs, simple_background, hood, white_background |
| 3 | 5 |  |  |  |  |  | 1girl, blush, hetero, open_mouth, penis, solo_focus, bar_censor, breasts, nipples, pussy, smile, 1boy, looking_at_viewer, panties_aside, sex, vaginal, yellow_eyes, cum, heart-shaped_pupils, male_pubic_hair, navel, saliva, shirt_lift, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :d | looking_at_viewer | open_mouth | solo | blush | collarbone | small_breasts | orange_eyes | simple_background | upper_body | white_background | navel | nipples | white_shirt | short_sleeves | bow | collared_shirt | necktie | shorts | vest | denim_shorts | cutoffs | hood | hetero | penis | solo_focus | bar_censor | breasts | pussy | smile | 1boy | panties_aside | sex | vaginal | yellow_eyes | cum | heart-shaped_pupils | male_pubic_hair | saliva | shirt_lift | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----|:--------------------|:-------------|:-------|:--------|:-------------|:----------------|:--------------|:--------------------|:-------------|:-------------------|:--------|:----------|:--------------|:----------------|:------|:-----------------|:----------|:---------|:-------|:---------------|:----------|:-------|:---------|:--------|:-------------|:-------------|:----------|:--------|:--------|:-------|:----------------|:------|:----------|:--------------|:------|:----------------------|:------------------|:---------|:-------------|:--------|
| 0 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | 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 |
|
CyberHarem/oogami_tamaki_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T17:46:58+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T01:19:26+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of oogami\_tamaki/大神環 (THE iDOLM@STER: Million Live!)
=============================================================
This is the dataset of oogami\_tamaki/大神環 (THE iDOLM@STER: Million Live!), containing 183 images and their tags.
The core tags of this character are 'long\_hair, one\_side\_up, orange\_hair, brown\_hair, brown\_eyes, 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"
] |
64b1876eacc4521c57157221a1378e957563f668
|
# Dataset of kinoshita_hinata/木下ひなた (THE iDOLM@STER: Million Live!)
This is the dataset of kinoshita_hinata/木下ひなた (THE iDOLM@STER: Million Live!), containing 103 images and their tags.
The core tags of this character are `brown_hair, short_hair, ahoge, green_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 | 85.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinoshita_hinata_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 103 | 64.82 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinoshita_hinata_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 209 | 119.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinoshita_hinata_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 103 | 81.61 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinoshita_hinata_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 209 | 147.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kinoshita_hinata_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kinoshita_hinata_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, solo, looking_at_viewer, open_mouth, blush, simple_background, white_background, :d, long_sleeves, skirt, white_shirt |
| 1 | 5 |  |  |  |  |  | 1girl, open_mouth, :d, blush, bow, puffy_short_sleeves, blue_eyes, flower, gloves, hairband, looking_at_viewer, solo, frills, mini_hat, red_dress, skirt |
| 2 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, solo, :d, bracelet, character_name, dress, blush, hair_bow, heart, necklace |
| 3 | 13 |  |  |  |  |  | 1girl, solo, blush, open_mouth, :d, looking_at_viewer, apple, ^_^, dress, holding_food |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | open_mouth | blush | simple_background | white_background | :d | long_sleeves | skirt | white_shirt | bow | puffy_short_sleeves | blue_eyes | flower | gloves | hairband | frills | mini_hat | red_dress | bracelet | character_name | dress | hair_bow | heart | necklace | apple | ^_^ | holding_food |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-------------|:--------|:--------------------|:-------------------|:-----|:---------------|:--------|:--------------|:------|:----------------------|:------------|:---------|:---------|:-----------|:---------|:-----------|:------------|:-----------|:-----------------|:--------|:-----------|:--------|:-----------|:--------|:------|:---------------|
| 0 | 10 |  |  |  |  |  | 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 | 5 |  |  |  |  |  | X | X | X | X | X | | | X | | | | | | | | | | | | | X | X | X | X | X | X | | | |
| 3 | 13 |  |  |  |  |  | X | X | X | X | X | | | X | | | | | | | | | | | | | | | X | | | | X | X | X |
|
CyberHarem/kinoshita_hinata_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T18:12:50+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:08:28+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kinoshita\_hinata/木下ひなた (THE iDOLM@STER: Million Live!)
==================================================================
This is the dataset of kinoshita\_hinata/木下ひなた (THE iDOLM@STER: Million Live!), containing 103 images and their tags.
The core tags of this character are 'brown\_hair, short\_hair, ahoge, green\_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"
] |
7508dcd3e49909de8a55137cea327b81177619ac
|
# Dataset of Noire Foschurose
This is the dataset of Noire Foschurose, containing 283 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 283 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 612 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 283 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 283 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 283 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 283 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 283 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 612 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 612 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 612 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/noire_foschurose_kumakumakumabear
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T18:15:35+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:43:10+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Noire Foschurose
===========================
This is the dataset of Noire Foschurose, containing 283 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
bd56afd0f53e7f116ef9ed3cc4472b64cb88a7a9
|
# Dataset of tokoro_megumi/所恵美/토코로메구미 (THE iDOLM@STER: Million Live!)
This is the dataset of tokoro_megumi/所恵美/토코로메구미 (THE iDOLM@STER: Million Live!), containing 430 images and their tags.
The core tags of this character are `long_hair, brown_hair, ahoge, blue_eyes, breasts, bangs, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 430 | 553.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokoro_megumi_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 430 | 314.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokoro_megumi_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1009 | 668.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokoro_megumi_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 430 | 486.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokoro_megumi_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1009 | 977.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tokoro_megumi_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tokoro_megumi_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | blue_sky, collarbone, day, navel, ocean, outdoors, 1girl, blush, cloud, cowboy_shot, looking_at_viewer, medium_breasts, smile, white_bikini, cleavage, hair_between_eyes, bare_arms, bare_shoulders, beach, blonde_hair, blue_bikini, blue_neckerchief, blue_sailor_collar, hand_on_hip, multiple_girls, sailor_bikini, solo_focus, standing, stomach |
| 1 | 12 |  |  |  |  |  | 1girl, solo, cleavage, medium_breasts, looking_at_viewer, navel, black_bikini, smile, blush, water |
| 2 | 9 |  |  |  |  |  | smile, earrings, 1girl, looking_at_viewer, open_mouth, solo, blush, dress, hair_flower |
| 3 | 5 |  |  |  |  |  | 1girl, cleavage, necklace, solo, black_jacket, blush, looking_at_viewer, simple_background, blue_shorts, denim_shorts, fur-trimmed_jacket, long_sleeves, medium_breasts, open_clothes, short_shorts, white_background, ;d, coat, collarbone, one_eye_closed, open_mouth, smile, sweatdrop, tank_top |
| 4 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, off_shoulder, collarbone, smile, bare_shoulders, cleavage, simple_background, white_background, necklace, upper_body, black_shirt, hair_between_eyes, medium_breasts, tank_top |
| 5 | 6 |  |  |  |  |  | 1girl, collarbone, hat, looking_at_viewer, smile, solo, bare_shoulders, choker, cleavage, dress, one_eye_closed, skirt, strapless, bracelet, earrings, medium_breasts, white_background, ;d, black_headwear, blush, cowboy_shot, heart, open_mouth, simple_background |
| 6 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, pleated_skirt, school_uniform, solo, white_shirt, blush, long_sleeves, plaid_skirt, collared_shirt, hair_between_eyes, miniskirt, cardigan, red_necktie, cowboy_shot, green_skirt, simple_background, standing, sweater, white_background, black_thighhighs, diagonal-striped_necktie, diagonal_stripes, dress_shirt, grin, sitting, zettai_ryouiki |
| 7 | 15 |  |  |  |  |  | 1girl, 1boy, blush, hetero, nipples, smile, solo_focus, sweat, penis, looking_at_viewer, navel, sex, vaginal, nude, open_mouth, pussy, female_pubic_hair, medium_breasts, spread_legs, girl_on_top, mosaic_censoring |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blue_sky | collarbone | day | navel | ocean | outdoors | 1girl | blush | cloud | cowboy_shot | looking_at_viewer | medium_breasts | smile | white_bikini | cleavage | hair_between_eyes | bare_arms | bare_shoulders | beach | blonde_hair | blue_bikini | blue_neckerchief | blue_sailor_collar | hand_on_hip | multiple_girls | sailor_bikini | solo_focus | standing | stomach | solo | black_bikini | water | earrings | open_mouth | dress | hair_flower | necklace | black_jacket | simple_background | blue_shorts | denim_shorts | fur-trimmed_jacket | long_sleeves | open_clothes | short_shorts | white_background | ;d | coat | one_eye_closed | sweatdrop | tank_top | off_shoulder | upper_body | black_shirt | hat | choker | skirt | strapless | bracelet | black_headwear | heart | pleated_skirt | school_uniform | white_shirt | plaid_skirt | collared_shirt | miniskirt | cardigan | red_necktie | green_skirt | sweater | black_thighhighs | diagonal-striped_necktie | diagonal_stripes | dress_shirt | grin | sitting | zettai_ryouiki | 1boy | hetero | nipples | sweat | penis | sex | vaginal | nude | pussy | female_pubic_hair | spread_legs | girl_on_top | mosaic_censoring |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------|:-------------|:------|:--------|:--------|:-----------|:--------|:--------|:--------|:--------------|:--------------------|:-----------------|:--------|:---------------|:-----------|:--------------------|:------------|:-----------------|:--------|:--------------|:--------------|:-------------------|:---------------------|:--------------|:-----------------|:----------------|:-------------|:-----------|:----------|:-------|:---------------|:--------|:-----------|:-------------|:--------|:--------------|:-----------|:---------------|:--------------------|:--------------|:---------------|:---------------------|:---------------|:---------------|:---------------|:-------------------|:-----|:-------|:-----------------|:------------|:-----------|:---------------|:-------------|:--------------|:------|:---------|:--------|:------------|:-----------|:-----------------|:--------|:----------------|:-----------------|:--------------|:--------------|:-----------------|:------------|:-----------|:--------------|:--------------|:----------|:-------------------|:---------------------------|:-------------------|:--------------|:-------|:----------|:-----------------|:-------|:---------|:----------|:--------|:--------|:------|:----------|:-------|:--------|:--------------------|:--------------|:--------------|:-------------------|
| 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 | 12 |  |  |  |  |  | | | | X | | | X | X | | | X | X | X | | X | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | | | | | | | 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 | 9 |  |  |  |  |  | | 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 | X | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 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 | | | | | | | | | | | | | |
| 7 | 15 |  |  |  |  |  | | | | X | | | X | X | | | X | X | X | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/tokoro_megumi_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T18:24:17+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-15T22:46:03+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tokoro\_megumi/所恵美/토코로메구미 (THE iDOLM@STER: Million Live!)
====================================================================
This is the dataset of tokoro\_megumi/所恵美/토코로메구미 (THE iDOLM@STER: Million Live!), containing 430 images and their tags.
The core tags of this character are 'long\_hair, brown\_hair, ahoge, blue\_eyes, breasts, bangs, large\_breasts', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
5f324844283f29c44d13ffe878170d7943547700
|
# Dataset of Shuri
This is the dataset of Shuri, containing 134 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 134 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 277 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 134 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 134 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 134 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 134 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 134 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 277 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 277 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 277 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/shuri_kumakumakumabear
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T18:27:49+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:43:15+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Shuri
================
This is the dataset of Shuri, containing 134 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
3baa75c909e795d315bd3281170929d85995945c
|
# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [PocketDoc/Dans-PersonalityEngine-13b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T19:32:36.390690](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-13b/blob/main/results_2023-09-16T19-32-36.390690.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788269345,
"f1": 0.05738255033557058,
"f1_stderr": 0.001309097903957112,
"acc": 0.4341558294682836,
"acc_stderr": 0.009872366201227655
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788269345,
"f1": 0.05738255033557058,
"f1_stderr": 0.001309097903957112
},
"harness|gsm8k|5": {
"acc": 0.0932524639878696,
"acc_stderr": 0.008009688838328578
},
"harness|winogrande|5": {
"acc": 0.7750591949486977,
"acc_stderr": 0.011735043564126732
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-13b
|
[
"region:us"
] |
2023-09-16T18:32:40+00:00
|
{"pretty_name": "Evaluation run of PocketDoc/Dans-PersonalityEngine-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [PocketDoc/Dans-PersonalityEngine-13b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-16T19:32:36.390690](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-13b/blob/main/results_2023-09-16T19-32-36.390690.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788269345,\n \"f1\": 0.05738255033557058,\n \"f1_stderr\": 0.001309097903957112,\n \"acc\": 0.4341558294682836,\n \"acc_stderr\": 0.009872366201227655\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788269345,\n \"f1\": 0.05738255033557058,\n \"f1_stderr\": 0.001309097903957112\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0932524639878696,\n \"acc_stderr\": 0.008009688838328578\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7750591949486977,\n \"acc_stderr\": 0.011735043564126732\n }\n}\n```", "repo_url": "https://huggingface.co/PocketDoc/Dans-PersonalityEngine-13b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_16T19_32_36.390690", "path": ["**/details_harness|drop|3_2023-09-16T19-32-36.390690.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-16T19-32-36.390690.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_16T19_32_36.390690", "path": ["**/details_harness|gsm8k|5_2023-09-16T19-32-36.390690.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-16T19-32-36.390690.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_16T19_32_36.390690", "path": ["**/details_harness|winogrande|5_2023-09-16T19-32-36.390690.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-16T19-32-36.390690.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_16T19_32_36.390690", "path": ["results_2023-09-16T19-32-36.390690.parquet"]}, {"split": "latest", "path": ["results_2023-09-16T19-32-36.390690.parquet"]}]}]}
|
2023-09-16T18:32:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-13b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-13b on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-09-16T19:32:36.390690(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T19:32:36.390690(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-13b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-09-16T19:32:36.390690(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-13b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model PocketDoc/Dans-PersonalityEngine-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-09-16T19:32:36.390690(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
] |
1007960a2c819aa86d41e2b4e2dfcc28200cdf13
|
# Dataset of maihama_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!)
This is the dataset of maihama_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!), containing 125 images and their tags.
The core tags of this character are `pink_hair, multicolored_hair, pink_eyes, ponytail, long_hair, blonde_hair, streaked_hair, breasts, bangs, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 125 | 113.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 125 | 86.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 277 | 167.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 125 | 108.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 277 | 204.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maihama_ayumu_theidolmstermillionlive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/maihama_ayumu_theidolmstermillionlive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, smile, solo, looking_at_viewer, midriff, navel, necklace, bracelet, open_mouth, crop_top, belt, cleavage, earrings, medium_breasts, one_eye_closed, fingerless_gloves, jacket, pants |
| 1 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, collarbone, simple_background, upper_body, smile, bare_shoulders, two-tone_hair, grey_background, open_mouth, sleeveless_shirt, white_shirt, closed_mouth, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | looking_at_viewer | midriff | navel | necklace | bracelet | open_mouth | crop_top | belt | cleavage | earrings | medium_breasts | one_eye_closed | fingerless_gloves | jacket | pants | blush | collarbone | simple_background | upper_body | bare_shoulders | two-tone_hair | grey_background | sleeveless_shirt | white_shirt | closed_mouth | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:----------|:--------|:-----------|:-----------|:-------------|:-----------|:-------|:-----------|:-----------|:-----------------|:-----------------|:--------------------|:---------|:--------|:--------|:-------------|:--------------------|:-------------|:-----------------|:----------------|:------------------|:-------------------|:--------------|:---------------|:-------------------|
| 0 | 10 |  |  |  |  |  | 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 |
|
CyberHarem/maihama_ayumu_theidolmstermillionlive
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-16T18:38:21+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T02:53:53+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of maihama\_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!)
=============================================================
This is the dataset of maihama\_ayumu/舞浜歩 (THE iDOLM@STER: Million Live!), containing 125 images and their tags.
The core tags of this character are 'pink\_hair, multicolored\_hair, pink\_eyes, ponytail, long\_hair, blonde\_hair, streaked\_hair, breasts, bangs, hair\_between\_eyes', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
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