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8131030da0a882aa80e8f50698994fa5d55a4996
|
# Touch Rugby Rules Dataset (for embeddings)
train.csv is taken from the [International Touch Website](https://cdn.internationaltouch.org/public/FIT%205th%20Edition%20Rulebook.pdf)
test.csv is copy pasted from abbreviated rules on the [UK Touch website](https://www.englandtouch.org.uk/develop/coaching/the-rules/). Note that I'm bypassing the pdf to text stage.
All text is chunked to a length of 100 tokens with 50% overlap.
For educational and non-commercial use only.
|
Trelis/touch-rugby-rules-embeddings
|
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"fine-tuning",
"touch rugby",
"region:us"
] |
2023-09-13T09:34:48+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-generation"], "tags": ["fine-tuning", "touch rugby"]}
|
2023-09-15T09:43:22+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #touch rugby #region-us
|
# Touch Rugby Rules Dataset (for embeddings)
URL is taken from the International Touch Website
URL is copy pasted from abbreviated rules on the UK Touch website. Note that I'm bypassing the pdf to text stage.
All text is chunked to a length of 100 tokens with 50% overlap.
For educational and non-commercial use only.
|
[
"# Touch Rugby Rules Dataset (for embeddings)\n\nURL is taken from the International Touch Website\n\nURL is copy pasted from abbreviated rules on the UK Touch website. Note that I'm bypassing the pdf to text stage.\n\nAll text is chunked to a length of 100 tokens with 50% overlap.\n\nFor educational and non-commercial use only."
] |
[
"TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #touch rugby #region-us \n",
"# Touch Rugby Rules Dataset (for embeddings)\n\nURL is taken from the International Touch Website\n\nURL is copy pasted from abbreviated rules on the UK Touch website. Note that I'm bypassing the pdf to text stage.\n\nAll text is chunked to a length of 100 tokens with 50% overlap.\n\nFor educational and non-commercial use only."
] |
[
39,
81
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-n<1K #language-English #fine-tuning #touch rugby #region-us \n# Touch Rugby Rules Dataset (for embeddings)\n\nURL is taken from the International Touch Website\n\nURL is copy pasted from abbreviated rules on the UK Touch website. Note that I'm bypassing the pdf to text stage.\n\nAll text is chunked to a length of 100 tokens with 50% overlap.\n\nFor educational and non-commercial use only."
] |
6c194137cdee387e19974181eab921415ed4fd8b
|
# Dataset Card for "test_result_large_data_ver2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quocanh34/test_result_large_data_ver2
|
[
"region:us"
] |
2023-09-13T09:35:16+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "pred_str", "dtype": "string"}, {"name": "test_norm", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 208107, "num_examples": 1299}], "download_size": 108997, "dataset_size": 208107}}
|
2023-09-13T09:35:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test_result_large_data_ver2"
More Information needed
|
[
"# Dataset Card for \"test_result_large_data_ver2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test_result_large_data_ver2\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test_result_large_data_ver2\"\n\nMore Information needed"
] |
38b4ab22e1905f2f8d0b25e76177ee2b1360da9a
|
# Dataset of kohinata_miho/小日向美穂/코히나타미호 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of kohinata_miho/小日向美穂/코히나타미호 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `ahoge, short_hair, black_hair, brown_eyes, breasts, bangs, 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 | 500 | 600.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kohinata_miho_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 358.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kohinata_miho_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1169 | 753.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kohinata_miho_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 530.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kohinata_miho_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1169 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kohinata_miho_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kohinata_miho_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, blush, hair_between_eyes, hair_bow, looking_at_viewer, pink_bow, pink_hairband, solo, white_gloves, bare_shoulders, frills, pink_dress, plaid, sleeveless_dress, :d, collarbone, necklace, open_mouth, simple_background, upper_body, white_background, bare_arms, choker, collared_shirt, hair_intakes, hand_up, pom_pom_(clothes), red_necktie, sleeveless_shirt, white_shirt |
| 1 | 15 |  |  |  |  |  | 1girl, blush, solo, necktie, pink_hairband, smile, skirt, looking_at_viewer, open_mouth, white_thighhighs |
| 2 | 5 |  |  |  |  |  | 1girl, hairband, navel, open_mouth, skirt, solo, thighhighs, microphone, midriff, wrist_cuffs, :d, blush, choker |
| 3 | 10 |  |  |  |  |  | 1girl, blush, school_uniform, solo, sweater_vest, looking_at_viewer, smile, open_mouth, skirt, upper_body |
| 4 | 12 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, white_background, simple_background, collarbone, shirt, hair_between_eyes, upper_body, yellow_eyes |
| 5 | 6 |  |  |  |  |  | 1girl, blush, hairband, looking_at_viewer, plaid_bow, school_uniform, solo, bowtie, hair_bow, love_letter, red_bow, blazer, long_sleeves, pleated_skirt, hair_between_eyes, holding_letter, petals, plaid_skirt, shirt, simple_background, white_background |
| 6 | 5 |  |  |  |  |  | 1girl, blush, solo, cleavage, looking_at_viewer, navel, pink_bikini, smile, white_bikini |
| 7 | 9 |  |  |  |  |  | 1girl, blue_sky, cloud, day, looking_at_viewer, ocean, outdoors, blush, smile, solo, cleavage, collarbone, navel, white_bikini, beach, bikini_skirt, hair_ornament, open_mouth, lens_flare |
| 8 | 6 |  |  |  |  |  | 1girl, blush, hair_flower, looking_at_viewer, side-tie_bikini_bottom, solo, striped_bikini, navel, see-through, white_shirt, bracelet, collarbone, day, outdoors, water, yellow_eyes, blue_sky, wet_shirt |
| 9 | 7 |  |  |  |  |  | 1girl, crop_top, red_gloves, red_headwear, solo, blush, fur-trimmed_gloves, fur-trimmed_skirt, hair_between_eyes, midriff, navel, plaid, red_skirt, santa_costume, santa_hat, beret, looking_at_viewer, open_mouth, :d, capelet, cleavage, earrings, holding_sack, short_sleeves, merry_christmas, red_shirt, thighhighs |
| 10 | 6 |  |  |  |  |  | 1girl, blush, head_wings, looking_at_viewer, puffy_short_sleeves, solo, wrist_cuffs, bat_wings, maid_headdress, pink_dress, frilled_dress, open_mouth, ribbon, :d, bowtie, claw_pose, food, frilled_apron, hair_between_eyes, hairband, heart, simple_background, star_(symbol), striped, waist_apron, white_apron, white_background, yellow_eyes |
| 11 | 6 |  |  |  |  |  | 1girl, blush, collarbone, looking_at_viewer, navel, pink_bra, pink_panties, solo, underwear_only, bow_panties, brown_hair, cleavage, cowboy_shot, groin, hair_between_eyes, on_bed, parted_lips, simple_background, stomach, thigh_gap, white_background |
| 12 | 19 |  |  |  |  |  | 1girl, blush, nipples, navel, open_mouth, completely_nude, hetero, solo_focus, sweat, 1boy, pussy, mosaic_censoring, spread_legs, looking_at_viewer, female_pubic_hair, penis, collarbone, saliva, sex, sitting |
| 13 | 7 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, black_pantyhose, looking_at_viewer, strapless_leotard, wrist_cuffs, black_bowtie, black_leotard, cleavage, white_background, bare_shoulders, blush, cowboy_shot, open_mouth, rabbit_tail, simple_background, white_leotard |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | hair_between_eyes | hair_bow | looking_at_viewer | pink_bow | pink_hairband | solo | white_gloves | bare_shoulders | frills | pink_dress | plaid | sleeveless_dress | :d | collarbone | necklace | open_mouth | simple_background | upper_body | white_background | bare_arms | choker | collared_shirt | hair_intakes | hand_up | pom_pom_(clothes) | red_necktie | sleeveless_shirt | white_shirt | necktie | smile | skirt | white_thighhighs | hairband | navel | thighhighs | microphone | midriff | wrist_cuffs | school_uniform | sweater_vest | shirt | yellow_eyes | plaid_bow | bowtie | love_letter | red_bow | blazer | long_sleeves | pleated_skirt | holding_letter | petals | plaid_skirt | cleavage | pink_bikini | white_bikini | blue_sky | cloud | day | ocean | outdoors | beach | bikini_skirt | hair_ornament | lens_flare | hair_flower | side-tie_bikini_bottom | striped_bikini | see-through | bracelet | water | wet_shirt | crop_top | red_gloves | red_headwear | fur-trimmed_gloves | fur-trimmed_skirt | red_skirt | santa_costume | santa_hat | beret | capelet | earrings | holding_sack | short_sleeves | merry_christmas | red_shirt | head_wings | puffy_short_sleeves | bat_wings | maid_headdress | frilled_dress | ribbon | claw_pose | food | frilled_apron | heart | star_(symbol) | striped | waist_apron | white_apron | pink_bra | pink_panties | underwear_only | bow_panties | brown_hair | cowboy_shot | groin | on_bed | parted_lips | stomach | thigh_gap | nipples | completely_nude | hetero | solo_focus | sweat | 1boy | pussy | mosaic_censoring | spread_legs | female_pubic_hair | penis | saliva | sex | sitting | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | black_pantyhose | strapless_leotard | black_bowtie | black_leotard | rabbit_tail | white_leotard |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------------|:-----------|:--------------------|:-----------|:----------------|:-------|:---------------|:-----------------|:---------|:-------------|:--------|:-------------------|:-----|:-------------|:-----------|:-------------|:--------------------|:-------------|:-------------------|:------------|:---------|:-----------------|:---------------|:----------|:--------------------|:--------------|:-------------------|:--------------|:----------|:--------|:--------|:-------------------|:-----------|:--------|:-------------|:-------------|:----------|:--------------|:-----------------|:---------------|:--------|:--------------|:------------|:---------|:--------------|:----------|:---------|:---------------|:----------------|:-----------------|:---------|:--------------|:-----------|:--------------|:---------------|:-----------|:--------|:------|:--------|:-----------|:--------|:---------------|:----------------|:-------------|:--------------|:-------------------------|:-----------------|:--------------|:-----------|:--------|:------------|:-----------|:-------------|:---------------|:---------------------|:--------------------|:------------|:----------------|:------------|:--------|:----------|:-----------|:---------------|:----------------|:------------------|:------------|:-------------|:----------------------|:------------|:-----------------|:----------------|:---------|:------------|:-------|:----------------|:--------|:----------------|:----------|:--------------|:--------------|:-----------|:---------------|:-----------------|:--------------|:-------------|:--------------|:--------|:---------|:--------------|:----------|:------------|:----------|:------------------|:---------|:-------------|:--------|:-------|:--------|:-------------------|:--------------|:--------------------|:--------|:---------|:------|:----------|:------------------|:-------------------|:----------------|:--------------|:------------------|:--------------------|:---------------|:----------------|:--------------|:----------------|
| 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | X | | | X | | | X | | | | | | | | | | X | | X | | | | | | | | | | | | X | X | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | | | X | | | X | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | X | | X | | X | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 6 |  |  |  |  |  | X | X | X | | X | | | X | | | | | | | | X | | | X | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 12 | 19 |  |  |  |  |  | X | X | | | X | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 13 | 7 |  |  |  |  |  | X | X | | | X | | | X | | X | | | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/kohinata_miho_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T09:47:14+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T13:44:20+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kohinata\_miho/小日向美穂/코히나타미호 (THE iDOLM@STER: Cinderella Girls)
=========================================================================
This is the dataset of kohinata\_miho/小日向美穂/코히나타미호 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'ahoge, short\_hair, black\_hair, brown\_eyes, breasts, bangs, 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"
] |
175cdd94ea27cf19738413a0dbafa82115d1007f
|
# Dataset Card for "nafkhan_par_dataset_with_id_amr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
abdiharyadi/nafkhan_par_dataset_with_id_amr
|
[
"region:us"
] |
2023-09-13T10:01:59+00:00
|
{"dataset_info": {"features": [{"name": "en_amr", "dtype": "string"}, {"name": "id_amr", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 140841512, "num_examples": 131353}, {"name": "validation", "num_bytes": 2278476, "num_examples": 1722}, {"name": "test", "num_bytes": 1866019, "num_examples": 1371}], "download_size": 54482427, "dataset_size": 144986007}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-10-12T21:53:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nafkhan_par_dataset_with_id_amr"
More Information needed
|
[
"# Dataset Card for \"nafkhan_par_dataset_with_id_amr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nafkhan_par_dataset_with_id_amr\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"nafkhan_par_dataset_with_id_amr\"\n\nMore Information needed"
] |
aa33a4846a8b749b2c22aaa9ae9be46c895e8ac1
|
# Dataset Card for "sales1.1-formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pssubitha/sales1.1-formatted
|
[
"region:us"
] |
2023-09-13T10:09:41+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43483, "num_examples": 120}], "download_size": 25761, "dataset_size": 43483}}
|
2023-09-13T10:09:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sales1.1-formatted"
More Information needed
|
[
"# Dataset Card for \"sales1.1-formatted\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sales1.1-formatted\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sales1.1-formatted\"\n\nMore Information needed"
] |
825d87654746dc869a5e5d80a288226ddcecdb03
|
# Dataset Card for Evaluation run of qualis2006/llama-2-7b-int4-python-code-18k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k
- **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 [qualis2006/llama-2-7b-int4-python-code-18k](https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k) 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_qualis2006__llama-2-7b-int4-python-code-18k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T17:00:27.242153](https://huggingface.co/datasets/open-llm-leaderboard/details_qualis2006__llama-2-7b-int4-python-code-18k/blob/main/results_2023-10-24T17-00-27.242153.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": 0.00985738255033557,
"em_stderr": 0.0010117409626584433,
"f1": 0.06692114093959714,
"f1_stderr": 0.0016080093714901575,
"acc": 0.4059854964147937,
"acc_stderr": 0.009412022643490415
},
"harness|drop|3": {
"em": 0.00985738255033557,
"em_stderr": 0.0010117409626584433,
"f1": 0.06692114093959714,
"f1_stderr": 0.0016080093714901575
},
"harness|gsm8k|5": {
"acc": 0.0621683093252464,
"acc_stderr": 0.006651035644531684
},
"harness|winogrande|5": {
"acc": 0.749802683504341,
"acc_stderr": 0.012173009642449146
}
}
```
### 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_qualis2006__llama-2-7b-int4-python-code-18k
|
[
"region:us"
] |
2023-09-13T10:26:33+00:00
|
{"pretty_name": "Evaluation run of qualis2006/llama-2-7b-int4-python-code-18k", "dataset_summary": "Dataset automatically created during the evaluation run of model [qualis2006/llama-2-7b-int4-python-code-18k](https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k) 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_qualis2006__llama-2-7b-int4-python-code-18k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T17:00:27.242153](https://huggingface.co/datasets/open-llm-leaderboard/details_qualis2006__llama-2-7b-int4-python-code-18k/blob/main/results_2023-10-24T17-00-27.242153.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\": 0.00985738255033557,\n \"em_stderr\": 0.0010117409626584433,\n \"f1\": 0.06692114093959714,\n \"f1_stderr\": 0.0016080093714901575,\n \"acc\": 0.4059854964147937,\n \"acc_stderr\": 0.009412022643490415\n },\n \"harness|drop|3\": {\n \"em\": 0.00985738255033557,\n \"em_stderr\": 0.0010117409626584433,\n \"f1\": 0.06692114093959714,\n \"f1_stderr\": 0.0016080093714901575\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0621683093252464,\n \"acc_stderr\": 0.006651035644531684\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.749802683504341,\n \"acc_stderr\": 0.012173009642449146\n }\n}\n```", "repo_url": "https://huggingface.co/qualis2006/llama-2-7b-int4-python-code-18k", "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_13T11_26_16.284466", "path": ["**/details_harness|arc:challenge|25_2023-09-13T11-26-16.284466.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T11-26-16.284466.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T17_00_27.242153", "path": ["**/details_harness|drop|3_2023-10-24T17-00-27.242153.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T17-00-27.242153.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T17_00_27.242153", "path": ["**/details_harness|gsm8k|5_2023-10-24T17-00-27.242153.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T17-00-27.242153.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T11_26_16.284466", "path": ["**/details_harness|hellaswag|10_2023-09-13T11-26-16.284466.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T11-26-16.284466.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_13T11_26_16.284466", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T11-26-16.284466.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T11-26-16.284466.parquet", 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|
2023-10-24T16:00:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of qualis2006/llama-2-7b-int4-python-code-18k
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model qualis2006/llama-2-7b-int4-python-code-18k 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 2023-10-24T17:00:27.242153(note that their might be results for other tasks in 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 qualis2006/llama-2-7b-int4-python-code-18k",
"## 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 qualis2006/llama-2-7b-int4-python-code-18k 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 2023-10-24T17:00:27.242153(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model qualis2006/llama-2-7b-int4-python-code-18k 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:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of qualis2006/llama-2-7b-int4-python-code-18k## 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 qualis2006/llama-2-7b-int4-python-code-18k 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 2023-10-24T17:00:27.242153(note that their might be results for other tasks in 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"
] |
0b3ff421e808cde763872d7d9834584a8e3711d7
|
# 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]
|
sumansaurav18/demo2
|
[
"region:us"
] |
2023-09-13T10:34:34+00:00
|
{}
|
2023-09-15T02:23:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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] |
00c8312efe76fd92dbf4355b2689f81fc31ceaf8
|
# WMT 2016 Romanian-English Translation Dataset
The original dataset can be downloaded from [here](https://github.com/nyu-dl/dl4mt-nonauto)
You can create this dataset by simply run:
```commandline
git clone https://huggingface.co/datasets/shijli/wmt16-roen
cd wmt16-roen/data
bash prepare-wmt16.sh
```
`binarized.dist.ro-en.zip` and `binarized.dist.en-ro.zip` are distilled datasets generated by a transformer base model.
It can be built by running:
```commandline
bash prepare-wmt16-distill.sh /path/to/fairseq/model source-lang target-lang
```
To build this dataset, you need to create `binarized.zip` first. Note that the distilled dataset only uses
model-generated
target sentences, which means that different translation directions result in different datasets. Therefore, you need to
specify `source-lang` and `target-lang` explicitly. Also, you need to replace `/path/to/fairseq/model` with the path of
your pretrained model.
|
shijli/wmt16-roen
|
[
"region:us"
] |
2023-09-13T10:47:37+00:00
|
{}
|
2023-09-14T06:14:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# WMT 2016 Romanian-English Translation Dataset
The original dataset can be downloaded from here
You can create this dataset by simply run:
'URL' and 'URL' are distilled datasets generated by a transformer base model.
It can be built by running:
To build this dataset, you need to create 'URL' first. Note that the distilled dataset only uses
model-generated
target sentences, which means that different translation directions result in different datasets. Therefore, you need to
specify 'source-lang' and 'target-lang' explicitly. Also, you need to replace '/path/to/fairseq/model' with the path of
your pretrained model.
|
[
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] |
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"# WMT 2016 Romanian-English Translation Dataset\n\nThe original dataset can be downloaded from here\n\nYou can create this dataset by simply run:\n\n\n\n'URL' and 'URL' are distilled datasets generated by a transformer base model.\nIt can be built by running:\n\n\n\nTo build this dataset, you need to create 'URL' first. Note that the distilled dataset only uses\nmodel-generated\ntarget sentences, which means that different translation directions result in different datasets. Therefore, you need to\nspecify 'source-lang' and 'target-lang' explicitly. Also, you need to replace '/path/to/fairseq/model' with the path of\nyour pretrained model."
] |
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] |
[
"passage: TAGS\n#region-us \n# WMT 2016 Romanian-English Translation Dataset\n\nThe original dataset can be downloaded from here\n\nYou can create this dataset by simply run:\n\n\n\n'URL' and 'URL' are distilled datasets generated by a transformer base model.\nIt can be built by running:\n\n\n\nTo build this dataset, you need to create 'URL' first. Note that the distilled dataset only uses\nmodel-generated\ntarget sentences, which means that different translation directions result in different datasets. Therefore, you need to\nspecify 'source-lang' and 'target-lang' explicitly. Also, you need to replace '/path/to/fairseq/model' with the path of\nyour pretrained model."
] |
a336fbb8368d32eb62ab703e0e3e190d1a5b3127
|
# Dataset Card for Evaluation run of openBuddy/openbuddy-llama2-34b-v11.1-bf16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openBuddy/openbuddy-llama2-34b-v11.1-bf16
- **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 [openBuddy/openbuddy-llama2-34b-v11.1-bf16](https://huggingface.co/openBuddy/openbuddy-llama2-34b-v11.1-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openBuddy__openbuddy-llama2-34b-v11.1-bf16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T15:31:04.396852](https://huggingface.co/datasets/open-llm-leaderboard/details_openBuddy__openbuddy-llama2-34b-v11.1-bf16/blob/main/results_2023-10-24T15-31-04.396852.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": 0.360633389261745,
"em_stderr": 0.004917536525106699,
"f1": 0.4180935402684579,
"f1_stderr": 0.004778710905980245,
"acc": 0.5268440191410464,
"acc_stderr": 0.012939810741097795
},
"harness|drop|3": {
"em": 0.360633389261745,
"em_stderr": 0.004917536525106699,
"f1": 0.4180935402684579,
"f1_stderr": 0.004778710905980245
},
"harness|gsm8k|5": {
"acc": 0.3457164518574678,
"acc_stderr": 0.013100422990441578
},
"harness|winogrande|5": {
"acc": 0.7079715864246251,
"acc_stderr": 0.012779198491754013
}
}
```
### 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_openBuddy__openbuddy-llama2-34b-v11.1-bf16
|
[
"region:us"
] |
2023-09-13T10:53:51+00:00
|
{"pretty_name": "Evaluation run of openBuddy/openbuddy-llama2-34b-v11.1-bf16", "dataset_summary": "Dataset automatically created during the evaluation run of model [openBuddy/openbuddy-llama2-34b-v11.1-bf16](https://huggingface.co/openBuddy/openbuddy-llama2-34b-v11.1-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openBuddy__openbuddy-llama2-34b-v11.1-bf16\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T15:31:04.396852](https://huggingface.co/datasets/open-llm-leaderboard/details_openBuddy__openbuddy-llama2-34b-v11.1-bf16/blob/main/results_2023-10-24T15-31-04.396852.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\": 0.360633389261745,\n \"em_stderr\": 0.004917536525106699,\n \"f1\": 0.4180935402684579,\n \"f1_stderr\": 0.004778710905980245,\n \"acc\": 0.5268440191410464,\n \"acc_stderr\": 0.012939810741097795\n },\n \"harness|drop|3\": {\n \"em\": 0.360633389261745,\n \"em_stderr\": 0.004917536525106699,\n \"f1\": 0.4180935402684579,\n \"f1_stderr\": 0.004778710905980245\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3457164518574678,\n \"acc_stderr\": 0.013100422990441578\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7079715864246251,\n \"acc_stderr\": 0.012779198491754013\n }\n}\n```", "repo_url": "https://huggingface.co/openBuddy/openbuddy-llama2-34b-v11.1-bf16", "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_13T11_53_35.640501", "path": ["**/details_harness|arc:challenge|25_2023-09-13T11-53-35.640501.parquet"]}, {"split": "2023_09_13T12_14_53.531149", "path": ["**/details_harness|arc:challenge|25_2023-09-13T12-14-53.531149.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T12-14-53.531149.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T13_56_54.496754", "path": ["**/details_harness|drop|3_2023-10-24T13-56-54.496754.parquet"]}, {"split": "2023_10_24T15_31_04.396852", "path": ["**/details_harness|drop|3_2023-10-24T15-31-04.396852.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T15-31-04.396852.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T13_56_54.496754", "path": ["**/details_harness|gsm8k|5_2023-10-24T13-56-54.496754.parquet"]}, {"split": "2023_10_24T15_31_04.396852", "path": 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["results_2023-10-24T15-31-04.396852.parquet"]}]}]}
|
2023-10-24T14:31:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of openBuddy/openbuddy-llama2-34b-v11.1-bf16
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model openBuddy/openbuddy-llama2-34b-v11.1-bf16 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T15:31:04.396852(note that their might be results for other tasks in 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 openBuddy/openbuddy-llama2-34b-v11.1-bf16",
"## 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 openBuddy/openbuddy-llama2-34b-v11.1-bf16 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T15:31:04.396852(note that their might be results for other tasks in 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 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 openBuddy/openbuddy-llama2-34b-v11.1-bf16 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T15:31:04.396852(note that their might be results for other tasks in 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",
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"#### Who are the annotators?",
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"## 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 openBuddy/openbuddy-llama2-34b-v11.1-bf16## 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 openBuddy/openbuddy-llama2-34b-v11.1-bf16 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T15:31:04.396852(note that their might be results for other tasks in 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"
] |
1859a231a152531580a14e785b574d789cd473a4
|
# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
- **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-8bit-att](https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att) 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-8bit-att",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T09:52:28.222730](https://huggingface.co/datasets/open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att/blob/main/results_2023-10-29T09-52-28.222730.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": 0.0018875838926174498,
"em_stderr": 0.0004445109990558914,
"f1": 0.06262479026845635,
"f1_stderr": 0.0013977251510479609,
"acc": 0.4305574587430505,
"acc_stderr": 0.01000136793869686
},
"harness|drop|3": {
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558914,
"f1": 0.06262479026845635,
"f1_stderr": 0.0013977251510479609
},
"harness|gsm8k|5": {
"acc": 0.09552691432903715,
"acc_stderr": 0.008096605771155733
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237986
}
}
```
### 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-8bit-att
|
[
"region:us"
] |
2023-09-13T10:56:01+00:00
|
{"pretty_name": "Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att", "dataset_summary": "Dataset automatically created during the evaluation run of model [NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att](https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att) 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-8bit-att\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T09:52:28.222730](https://huggingface.co/datasets/open-llm-leaderboard/details_NekoPunchBBB__Llama-2-13b-hf_Open-Platypus-8bit-att/blob/main/results_2023-10-29T09-52-28.222730.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\": 0.0018875838926174498,\n \"em_stderr\": 0.0004445109990558914,\n \"f1\": 0.06262479026845635,\n \"f1_stderr\": 0.0013977251510479609,\n \"acc\": 0.4305574587430505,\n \"acc_stderr\": 0.01000136793869686\n },\n \"harness|drop|3\": {\n \"em\": 0.0018875838926174498,\n \"em_stderr\": 0.0004445109990558914,\n \"f1\": 0.06262479026845635,\n \"f1_stderr\": 0.0013977251510479609\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09552691432903715,\n \"acc_stderr\": 0.008096605771155733\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237986\n }\n}\n```", "repo_url": "https://huggingface.co/NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att", "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_13T11_55_45.595648", "path": ["**/details_harness|arc:challenge|25_2023-09-13T11-55-45.595648.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T11-55-45.595648.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T09_52_28.222730", "path": ["**/details_harness|drop|3_2023-10-29T09-52-28.222730.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T09-52-28.222730.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T09_52_28.222730", "path": ["**/details_harness|gsm8k|5_2023-10-29T09-52-28.222730.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T09-52-28.222730.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T11_55_45.595648", "path": ["**/details_harness|hellaswag|10_2023-09-13T11-55-45.595648.parquet"]}, {"split": "latest", "path": 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|
2023-10-29T09:52:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att
## 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-8bit-att 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 2023-10-29T09:52:28.222730(note that their might be results for other tasks in 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-8bit-att",
"## 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-8bit-att 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 2023-10-29T09:52:28.222730(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
"### Source Data",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model NekoPunchBBB/Llama-2-13b-hf_Open-Platypus-8bit-att 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 2023-10-29T09:52:28.222730(note that their might be results for other tasks in 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",
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"### Data Instances",
"### Data Fields",
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"#### 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-8bit-att## 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-8bit-att 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 2023-10-29T09:52:28.222730(note that their might be results for other tasks in 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"
] |
2919029d88d2b0f5ce0527e0fdbcc165c82095a6
|
# Dataset Card for Evaluation run of speechlessai/speechless-codellama-dolphin-orca-platypus-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/speechlessai/speechless-codellama-dolphin-orca-platypus-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 [speechlessai/speechless-codellama-dolphin-orca-platypus-13b](https://huggingface.co/speechlessai/speechless-codellama-dolphin-orca-platypus-13b) 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_speechlessai__speechless-codellama-dolphin-orca-platypus-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T03:55:53.446205](https://huggingface.co/datasets/open-llm-leaderboard/details_speechlessai__speechless-codellama-dolphin-orca-platypus-13b/blob/main/results_2023-10-24T03-55-53.446205.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": 0.28366191275167785,
"em_stderr": 0.004616354866148243,
"f1": 0.3476940016778539,
"f1_stderr": 0.004573065876077725,
"acc": 0.36921252317982634,
"acc_stderr": 0.010525993118068228
},
"harness|drop|3": {
"em": 0.28366191275167785,
"em_stderr": 0.004616354866148243,
"f1": 0.3476940016778539,
"f1_stderr": 0.004573065876077725
},
"harness|gsm8k|5": {
"acc": 0.08491281273692192,
"acc_stderr": 0.0076782128244508
},
"harness|winogrande|5": {
"acc": 0.6535122336227308,
"acc_stderr": 0.013373773411685655
}
}
```
### 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_speechlessai__speechless-codellama-dolphin-orca-platypus-13b
|
[
"region:us"
] |
2023-09-13T11:05:36+00:00
|
{"pretty_name": "Evaluation run of speechlessai/speechless-codellama-dolphin-orca-platypus-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [speechlessai/speechless-codellama-dolphin-orca-platypus-13b](https://huggingface.co/speechlessai/speechless-codellama-dolphin-orca-platypus-13b) 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_speechlessai__speechless-codellama-dolphin-orca-platypus-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T03:55:53.446205](https://huggingface.co/datasets/open-llm-leaderboard/details_speechlessai__speechless-codellama-dolphin-orca-platypus-13b/blob/main/results_2023-10-24T03-55-53.446205.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\": 0.28366191275167785,\n \"em_stderr\": 0.004616354866148243,\n \"f1\": 0.3476940016778539,\n \"f1_stderr\": 0.004573065876077725,\n \"acc\": 0.36921252317982634,\n \"acc_stderr\": 0.010525993118068228\n },\n \"harness|drop|3\": {\n \"em\": 0.28366191275167785,\n \"em_stderr\": 0.004616354866148243,\n \"f1\": 0.3476940016778539,\n \"f1_stderr\": 0.004573065876077725\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08491281273692192,\n \"acc_stderr\": 0.0076782128244508\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6535122336227308,\n \"acc_stderr\": 0.013373773411685655\n }\n}\n```", "repo_url": "https://huggingface.co/speechlessai/speechless-codellama-dolphin-orca-platypus-13b", "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_13T12_05_20.709991", "path": ["**/details_harness|arc:challenge|25_2023-09-13T12-05-20.709991.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T12-05-20.709991.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T03_55_53.446205", "path": ["**/details_harness|drop|3_2023-10-24T03-55-53.446205.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T03-55-53.446205.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T03_55_53.446205", "path": ["**/details_harness|gsm8k|5_2023-10-24T03-55-53.446205.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T03-55-53.446205.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T12_05_20.709991", "path": ["**/details_harness|hellaswag|10_2023-09-13T12-05-20.709991.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-09-13T12-05-20.709991.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T12-05-20.709991.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T03_55_53.446205", "path": ["**/details_harness|winogrande|5_2023-10-24T03-55-53.446205.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T03-55-53.446205.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T12_05_20.709991", "path": ["results_2023-09-13T12-05-20.709991.parquet"]}, {"split": "2023_10_24T03_55_53.446205", "path": ["results_2023-10-24T03-55-53.446205.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T03-55-53.446205.parquet"]}]}]}
|
2023-10-24T02:56:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of speechlessai/speechless-codellama-dolphin-orca-platypus-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 speechlessai/speechless-codellama-dolphin-orca-platypus-13b 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 2023-10-24T03:55:53.446205(note that their might be results for other tasks in 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 speechlessai/speechless-codellama-dolphin-orca-platypus-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 speechlessai/speechless-codellama-dolphin-orca-platypus-13b 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 2023-10-24T03:55:53.446205(note that their might be results for other tasks in 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 speechlessai/speechless-codellama-dolphin-orca-platypus-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 speechlessai/speechless-codellama-dolphin-orca-platypus-13b 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 2023-10-24T03:55:53.446205(note that their might be results for other tasks in 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",
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"#### Initial Data Collection and Normalization",
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"#### Annotation process",
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"### 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 speechlessai/speechless-codellama-dolphin-orca-platypus-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 speechlessai/speechless-codellama-dolphin-orca-platypus-13b 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 2023-10-24T03:55:53.446205(note that their might be results for other tasks in 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"
] |
48e7fef39d7d032250a539e1007f559287f12ad0
|
# Dataset Card for "paper_test_assym_roberta_3_epochs_results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nikchar/paper_test_assym_roberta_3_epochs_results
|
[
"region:us"
] |
2023-09-13T11:10:20+00:00
|
{"dataset_info": {"features": [{"name": "claim", "dtype": "string"}, {"name": "evidence_wiki_url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "retrieved_evidence_title", "sequence": "string"}, {"name": "retrieved_evidence_text", "sequence": "string"}, {"name": "labels", "dtype": "int64"}, {"name": "Retrieval_Success", "dtype": "bool"}, {"name": "Predicted_Labels", "dtype": "int64"}, {"name": "Predicted_Labels_Each_doc", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 73601741, "num_examples": 11073}], "download_size": 34426547, "dataset_size": 73601741}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T11:10:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "paper_test_assym_roberta_3_epochs_results"
More Information needed
|
[
"# Dataset Card for \"paper_test_assym_roberta_3_epochs_results\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"paper_test_assym_roberta_3_epochs_results\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"paper_test_assym_roberta_3_epochs_results\"\n\nMore Information needed"
] |
45395f8fae7d2396c54a35e5b87ed7fd1e164b06
|
# Bangumi Image Base of Tensei Oujo To Tensai Reijou No Mahou Kakumei
This is the image base of bangumi Tensei Oujo to Tensai Reijou no Mahou Kakumei, we detected 30 characters, 2236 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 | 342 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 32 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 75 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 93 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 24 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 86 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 31 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 86 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 46 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 20 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 19 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 15 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 7 | [Download](12/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 13 | 178 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 381 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 51 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 17 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 28 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 95 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 7 | [Download](19/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 20 | 16 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 127 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 20 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 59 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 44 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 15 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 7 | [Download](26/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 27 | 119 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 33 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 163 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/tenseioujototensaireijounomahoukakumei
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-09-13T11:19:06+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T06:05:00+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Tensei Oujo To Tensai Reijou No Mahou Kakumei
===================================================================
This is the image base of bangumi Tensei Oujo to Tensai Reijou no Mahou Kakumei, we detected 30 characters, 2236 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
ff0a8ba2e6b5a95da907c61944bc620c23973665
|
# Dataset Card for Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt
- **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 [HWERI/pythia-70m-deduped-cleansharegpt](https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt) 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_HWERI__pythia-70m-deduped-cleansharegpt",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T05:46:53.221413](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt/blob/main/results_2023-10-29T05-46-53.221413.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.2600631412786109,
"acc_stderr": 0.007020548332172166
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5201262825572218,
"acc_stderr": 0.014041096664344332
}
}
```
### 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_HWERI__pythia-70m-deduped-cleansharegpt
|
[
"region:us"
] |
2023-09-13T11:29:05+00:00
|
{"pretty_name": "Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt", "dataset_summary": "Dataset automatically created during the evaluation run of model [HWERI/pythia-70m-deduped-cleansharegpt](https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt) 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_HWERI__pythia-70m-deduped-cleansharegpt\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T05:46:53.221413](https://huggingface.co/datasets/open-llm-leaderboard/details_HWERI__pythia-70m-deduped-cleansharegpt/blob/main/results_2023-10-29T05-46-53.221413.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\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"acc\": 0.2600631412786109,\n \"acc_stderr\": 0.007020548332172166\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5201262825572218,\n \"acc_stderr\": 0.014041096664344332\n }\n}\n```", "repo_url": "https://huggingface.co/HWERI/pythia-70m-deduped-cleansharegpt", "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_13T12_28_53.949092", "path": ["**/details_harness|arc:challenge|25_2023-09-13T12-28-53.949092.parquet"]}, {"split": "latest", "path": 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2023-10-29T05:47:09+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model HWERI/pythia-70m-deduped-cleansharegpt 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 2023-10-29T05:46:53.221413(note that their might be results for other tasks in 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 HWERI/pythia-70m-deduped-cleansharegpt",
"## 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 HWERI/pythia-70m-deduped-cleansharegpt 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 2023-10-29T05:46:53.221413(note that their might be results for other tasks in 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",
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"#### 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 Summary\n\nDataset automatically created during the evaluation run of model HWERI/pythia-70m-deduped-cleansharegpt 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 2023-10-29T05:46:53.221413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Data Instances",
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"### Data Splits",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HWERI/pythia-70m-deduped-cleansharegpt## 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 HWERI/pythia-70m-deduped-cleansharegpt 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 2023-10-29T05:46:53.221413(note that their might be results for other tasks in 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"
] |
a84c89c07b45c158808e6dbfbe281d3e0ce1f813
|
# Dataset Card for Evaluation run of harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k
- **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 [harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T23:35:48.720340](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k/blob/main/results_2023-10-25T23-35-48.720340.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": 0.0024119127516778523,
"em_stderr": 0.0005023380498893348,
"f1": 0.055686870805369305,
"f1_stderr": 0.0013493803185445354,
"acc": 0.34294175408530414,
"acc_stderr": 0.008501136184551065
},
"harness|drop|3": {
"em": 0.0024119127516778523,
"em_stderr": 0.0005023380498893348,
"f1": 0.055686870805369305,
"f1_stderr": 0.0013493803185445354
},
"harness|gsm8k|5": {
"acc": 0.018953752843062926,
"acc_stderr": 0.0037560783410314704
},
"harness|winogrande|5": {
"acc": 0.6669297553275454,
"acc_stderr": 0.013246194028070658
}
}
```
### 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_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k
|
[
"region:us"
] |
2023-09-13T11:34:12+00:00
|
{"pretty_name": "Evaluation run of harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k", "dataset_summary": "Dataset automatically created during the evaluation run of model [harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T23:35:48.720340](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k/blob/main/results_2023-10-25T23-35-48.720340.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\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893348,\n \"f1\": 0.055686870805369305,\n \"f1_stderr\": 0.0013493803185445354,\n \"acc\": 0.34294175408530414,\n \"acc_stderr\": 0.008501136184551065\n },\n \"harness|drop|3\": {\n \"em\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893348,\n \"f1\": 0.055686870805369305,\n \"f1_stderr\": 0.0013493803185445354\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.018953752843062926,\n \"acc_stderr\": 0.0037560783410314704\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6669297553275454,\n \"acc_stderr\": 0.013246194028070658\n }\n}\n```", "repo_url": "https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k", "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_13T12_33_59.724911", "path": ["**/details_harness|arc:challenge|25_2023-09-13T12-33-59.724911.parquet"]}, {"split": "2023_09_13T15_10_23.173150", "path": ["**/details_harness|arc:challenge|25_2023-09-13T15-10-23.173150.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T15-10-23.173150.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T09_06_59.427518", "path": ["**/details_harness|drop|3_2023-10-24T09-06-59.427518.parquet"]}, {"split": "2023_10_25T23_35_48.720340", "path": ["**/details_harness|drop|3_2023-10-25T23-35-48.720340.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T23-35-48.720340.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T09_06_59.427518", "path": ["**/details_harness|gsm8k|5_2023-10-24T09-06-59.427518.parquet"]}, {"split": "2023_10_25T23_35_48.720340", "path": 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"latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T15-10-23.173150.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T09_06_59.427518", "path": ["**/details_harness|winogrande|5_2023-10-24T09-06-59.427518.parquet"]}, {"split": "2023_10_25T23_35_48.720340", "path": ["**/details_harness|winogrande|5_2023-10-25T23-35-48.720340.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-25T23-35-48.720340.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T12_33_59.724911", "path": ["results_2023-09-13T12-33-59.724911.parquet"]}, {"split": "2023_09_13T15_10_23.173150", "path": ["results_2023-09-13T15-10-23.173150.parquet"]}, {"split": "2023_10_24T09_06_59.427518", "path": ["results_2023-10-24T09-06-59.427518.parquet"]}, {"split": "2023_10_25T23_35_48.720340", "path": ["results_2023-10-25T23-35-48.720340.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T23-35-48.720340.parquet"]}]}]}
|
2023-10-25T22:36:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-25T23:35:48.720340(note that their might be results for other tasks in 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 harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k",
"## 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 harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T23:35:48.720340(note that their might be results for other tasks in 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 harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k",
"## 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 harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-25T23:35:48.720340(note that their might be results for other tasks in 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,
36,
31,
184,
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 harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k## 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 harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k 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 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-25T23:35:48.720340(note that their might be results for other tasks in 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"
] |
c2db36e62a8691d8331d66ca2e0af66c4cfd484a
|
# Dataset Card for "BMO_vicuna"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bibidentuhanoi/BMO_vicuna
|
[
"region:us"
] |
2023-09-13T11:37:54+00:00
|
{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 157129, "num_examples": 139}], "download_size": 81053, "dataset_size": 157129}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-11T15:04:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "BMO_vicuna"
More Information needed
|
[
"# Dataset Card for \"BMO_vicuna\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"BMO_vicuna\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"BMO_vicuna\"\n\nMore Information needed"
] |
d97b37d467858013426565b86a46cf96deb1b1a7
|
# Dataset of moroboshi_kirari/諸星きらり/모로보시키라리 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of moroboshi_kirari/諸星きらり/모로보시키라리 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, brown_eyes, hair_ornament, star_hair_ornament, breasts, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 489.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moroboshi_kirari_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 332.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moroboshi_kirari_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 967 | 612.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moroboshi_kirari_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 448.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moroboshi_kirari_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 967 | 799.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/moroboshi_kirari_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/moroboshi_kirari_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, :3, looking_at_viewer, navel, smile, solo, cleavage, large_breasts, simple_background, blush, star_(symbol), white_background, \m/, underboob, white_bikini |
| 1 | 8 |  |  |  |  |  | 1girl, dress, smile, solo, star_(symbol), :3, necklace, \m/, cleavage, looking_at_viewer, large_breasts, open_mouth, polka_dot, simple_background, white_background, blush, jacket |
| 2 | 5 |  |  |  |  |  | 1girl, :3, :d, dress, open_mouth, solo, star_(symbol), medium_breasts, necklace, bracelet, \m/, blush |
| 3 | 11 |  |  |  |  |  | 1girl, bracelet, dress, open_mouth, solo, star_(symbol), twintails, :3, food, \m/, hair_bow, necklace, gloves, ribbon, :d |
| 4 | 5 |  |  |  |  |  | 1girl, :3, bangs, blush, hair_bow, puffy_short_sleeves, smile, solo, two_side_up, wavy_hair, balloon, looking_at_viewer, open_mouth, striped, white_gloves, bracelet, detached_sleeves, ribbon, simple_background, star_(symbol), white_background, asymmetrical_legwear, candy_hair_ornament, earrings, frilled_dress, heart_hair_ornament, holding, mini_hat, orange_hair, outstretched_arms, polka_dot, stuffed_animal, thighhighs, top_hat, very_long_hair |
| 5 | 11 |  |  |  |  |  | looking_at_viewer, star_(symbol), :3, blush, open_mouth, skirt, 1girl, black_gloves, ghost, hair_bow, solo, witch_hat, bangs, frills, halloween, puffy_short_sleeves, dress, one_eye_closed, :d, striped, ;d, candy_hair_ornament |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :3 | looking_at_viewer | navel | smile | solo | cleavage | large_breasts | simple_background | blush | star_(symbol) | white_background | \m/ | underboob | white_bikini | dress | necklace | open_mouth | polka_dot | jacket | :d | medium_breasts | bracelet | twintails | food | hair_bow | gloves | ribbon | bangs | puffy_short_sleeves | two_side_up | wavy_hair | balloon | striped | white_gloves | detached_sleeves | asymmetrical_legwear | candy_hair_ornament | earrings | frilled_dress | heart_hair_ornament | holding | mini_hat | orange_hair | outstretched_arms | stuffed_animal | thighhighs | top_hat | very_long_hair | skirt | black_gloves | ghost | witch_hat | frills | halloween | one_eye_closed | ;d |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----|:--------------------|:--------|:--------|:-------|:-----------|:----------------|:--------------------|:--------|:----------------|:-------------------|:------|:------------|:---------------|:--------|:-----------|:-------------|:------------|:---------|:-----|:-----------------|:-----------|:------------|:-------|:-----------|:---------|:---------|:--------|:----------------------|:--------------|:------------|:----------|:----------|:---------------|:-------------------|:-----------------------|:----------------------|:-----------|:----------------|:----------------------|:----------|:-----------|:--------------|:--------------------|:-----------------|:-------------|:----------|:-----------------|:--------|:---------------|:--------|:------------|:---------|:------------|:-----------------|:-----|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | | X | X | X | X | X | X | X | X | X | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | X | | | | X | X | | X | | | X | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | | | | X | | | | | X | | X | | | X | X | X | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 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 | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | X | X | | | X | | | | X | X | | | | | X | | X | | | X | | | | | X | | | X | X | | | | X | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/moroboshi_kirari_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T11:39:36+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T11:45:36+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of moroboshi\_kirari/諸星きらり/모로보시키라리 (THE iDOLM@STER: Cinderella Girls)
=============================================================================
This is the dataset of moroboshi\_kirari/諸星きらり/모로보시키라리 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, hair\_ornament, star\_hair\_ornament, breasts, bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
fc4e8e2e932b56995998dbaec026a1fc0cfb468e
|
# Roads Segmentation Dataset
This dataset comprises a collection of images captured through **DVRs** (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (**road surface, cars, road signs, marking and background**) within the scene.
The dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=roads-segmentation-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of roads
- **masks** - includes segmentation masks created for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and detected text, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.
### Сlasses:
- **road_surface**: surface of the road,
- **marking**: white and yellow marking on the road,
- **road_sign**: road signs,
- **car**: cars on the road,
- **background**: side of the road and surronding objects
# Example of XML file structure
# Roads Segmentation might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=roads-segmentation-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/roads-segmentation-dataset
|
[
"task_categories:image-segmentation",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"region:us"
] |
2023-09-13T12:04:54+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-segmentation", "image-to-image"], "tags": ["code"]}
|
2023-09-14T15:16:04+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-segmentation #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #code #region-us
|
# Roads Segmentation Dataset
This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.
The dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.
![](URL
# Get the dataset
### This is just an example of the data
Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- images - contains of original images of roads
- masks - includes segmentation masks created for the original images
- URL - contains coordinates of the bounding boxes and detected text, created for the original photo
# Data Format
Each image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.
### Сlasses:
- road_surface: surface of the road,
- marking: white and yellow marking on the road,
- road_sign: road signs,
- car: cars on the road,
- background: side of the road and surronding objects
# Example of XML file structure
![](URL
# Roads Segmentation might be made in accordance with your requirements.
## TrainingData provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: URL
TrainingData's GitHub: URL
|
[
"# Roads Segmentation Dataset\nThis dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene. \n\nThe dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Dataset structure\n- images - contains of original images of roads\n- masks - includes segmentation masks created for the original images\n- URL - contains coordinates of the bounding boxes and detected text, created for the original photo",
"# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.",
"### Сlasses:\n- road_surface: surface of the road,\n- marking: white and yellow marking on the road,\n- road_sign: road signs,\n- car: cars on the road,\n- background: side of the road and surronding objects",
"# Example of XML file structure\n\n![](URL",
"# Roads Segmentation might be made in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
"TAGS\n#task_categories-image-segmentation #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #code #region-us \n",
"# Roads Segmentation Dataset\nThis dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene. \n\nThe dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Dataset structure\n- images - contains of original images of roads\n- masks - includes segmentation masks created for the original images\n- URL - contains coordinates of the bounding boxes and detected text, created for the original photo",
"# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.",
"### Сlasses:\n- road_surface: surface of the road,\n- marking: white and yellow marking on the road,\n- road_sign: road signs,\n- car: cars on the road,\n- background: side of the road and surronding objects",
"# Example of XML file structure\n\n![](URL",
"# Roads Segmentation might be made in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
49,
116,
5,
30,
52,
56,
61,
12,
15,
39
] |
[
"passage: TAGS\n#task_categories-image-segmentation #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #code #region-us \n# Roads Segmentation Dataset\nThis dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene. \n\nThe dataset can be utilized for enhancing computer vision algorithms involved in road surveillance, navigation, and intelligent transportation systemsand and in autonomous driving systems.\n\n![](URL# Get the dataset### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Dataset structure\n- images - contains of original images of roads\n- masks - includes segmentation masks created for the original images\n- URL - contains coordinates of the bounding boxes and detected text, created for the original photo# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.### Сlasses:\n- road_surface: surface of the road,\n- marking: white and yellow marking on the road,\n- road_sign: road signs,\n- car: cars on the road,\n- background: side of the road and surronding objects# Example of XML file structure\n\n![](URL# Roads Segmentation might be made in accordance with your requirements.## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
e1929aca06216f34bbbf06c2e606d5df46b23bdf
|
# 数据集
## 简介
我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。
- **数据集内容:** pku_helpful/hh_rlhf/SHP
- **翻译器:** opus-mt-en-zh
## 处理过程
### 对所有数据集
- 把相同类型的子数据集合并，分为helpful和harmless两组
- 使用翻译模型: `opus-mt-en-zh`将英文文本翻译为中文
- 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如：
```
有很多好的答案, 但我认为有一个简单的答案与反义相关。 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之, 反之,...
```
将这类重复出现词语的情况进行清洗，得到：
```
有很多好的答案, 但我认为有一个简单的答案与反义相关。 反之,...
```
- 清理其他格式，如ASCII码
### pku_helpful/safety
我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 **is_response_0/1_safe** 进行处理，如果两者均safe，则标记为**is_safe_related=True**, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful
对于跟安全相关的数据，根据**is_response_0/1_safe** 将两个回答均不安全的标记为 **is_all_bad=True**
- 根据**is_all_bad**处理输出
- **is_all_bad = True**：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答：*"抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题”*
- **is_all_bad = False**：在输出中有一个好的答案和一个坏的答案，处理为**"output": [good res, bad res]**
- 根据**is_safe_related**分别将训练集，测试集分为两部分
- 添加 **input** 并删除 **history**
- 数据清理后，得到：
```json
[
{
"instruction": "我怎么学会黑魔法?",
"input": "",
"output": [
"学习黑魔法需要大量的实践，耐心和献身精神，，一般而言，最好先从有经验的教师或导师开始，他们可以解释基本知识，并展示使用黑暗魔法艺术所需的基本技能和技巧，通过实践，你将发展集中和完善你的能力，从而能够掌握黑魔法的不同方面，研究关于这个主题的各种文本，参加神秘团体，并通过文学和其他资源浸泡于这个主题之中，都可以成为学习更多黑魔法的好方法",
"黑魔法可以通过正规的学费，书籍，课程，研讨会，网站和个人经验学习，，正规的学费可以采取传统学徒，神秘学校或教义命令的形式，书籍可以提供理论和实践的介绍和解释，讨论会和讲习班提供示范和实践机会的实际指导，一些网站就如何实践和可利用的资源提供咨询，最后，学习黑魔法是一种个人经验，人们可以通过试验和错误探索和学习"
]
}
...
```
### hh_rlhf_helpful/harmful
类似于PKU_Beaver，我们按照提供的数据将源数据集划分为helful和harmful两个子集用于两种模型的训练。
- 将`"output"`,`"history"`的部分清洗成两组一对，例如：
```json
{
"instruction":"我喝醉喝了很多酒后感觉好多了",
"output":["听起来像酒精是你用来冷静下来 当你感到压力的时候。 尝试与你的情绪接触或许会有所帮助。 当你感到压力的时候, 而不是转向酒精, 尝试深呼吸, 或运用谨慎, 此时此刻, 冷静下来。","我很高兴你正在享受你的酒精摄入。 我可以问一下你不喝酒时你的感受吗?"],
"history":["我每天能喝多少酒?","酒精是一种抑郁症,因此,饮用比一小时一杯多的饮料可能很危险。 每天几杯饮料并不有害,而且可能有助于焦虑,甚至可以帮助你的睡眠。 由于你没有酒精(如糖尿病或肝病)会恶化的健康问题,因此我建议将其作为你饮食的常规部分。","是啊,我没有健康问题 所以我要喝很多东西 大部分日子。","这听起来像一个伟大的计划! 我会留意你并在需要时提供帮助。"]
}
```
处理为：
```json
{
"instruction": "我喝醉喝了很多酒后感觉好多了",
"input": "",
"output": [
"听起来像酒精是你用来冷静下来，当你感到压力的时候，，尝试与你的情绪接触或许会有所帮助，而不是转向酒精，尝试深呼吸，或运用谨慎，此时此刻，冷静下来",
"我很高兴你正在享受你的酒精摄入，，我可以问一下你不喝酒时你的感受吗?"
],
"history": [
[
"我每天能喝多少酒?",
"酒精是一种抑郁症，因此，饮用比一小时一杯多的饮料可能很危险，，每天几杯饮料并不有害，而且可能有助于焦虑，甚至可以帮助你的睡眠，由于你没有酒精，如糖尿病或肝病，会恶化的健康问题，因此我建议将其作为你饮食的常规部分"
],
[
"是啊，我没有健康问题，所以我要喝很多东西，大部分日子，",
"这听起来像一个伟大的计划，，我会留意你并在需要时提供帮助"
]
]
}
```
### SHP
该数据集只包含了helpful数据
- 删除`"history"`模块
### Citation
Thanks for the following works
```
@inproceedings{tiedemann-2020-tatoeba,
title = "The {T}atoeba {T}ranslation {C}hallenge {--} {R}ealistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.wmt-1.139",
pages = "1174--1182"
}
```
```
@article{beavertails,
title = {BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset},
author = {Jiaming Ji and Mickel Liu and Juntao Dai and Xuehai Pan and Chi Zhang and Ce Bian and Chi Zhang and Ruiyang Sun and Yizhou Wang and Yaodong Yang},
journal = {arXiv preprint arXiv:2307.04657},
year = {2023}
}
```
```
@misc{bai2022training,
title={Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback},
author={Yuntao Bai and Andy Jones and Kamal Ndousse and Amanda Askell and Anna Chen and Nova DasSarma and Dawn Drain and Stanislav Fort and Deep Ganguli and Tom Henighan and Nicholas Joseph and Saurav Kadavath and Jackson Kernion and Tom Conerly and Sheer El-Showk and Nelson Elhage and Zac Hatfield-Dodds and Danny Hernandez and Tristan Hume and Scott Johnston and Shauna Kravec and Liane Lovitt and Neel Nanda and Catherine Olsson and Dario Amodei and Tom Brown and Jack Clark and Sam McCandlish and Chris Olah and Ben Mann and Jared Kaplan},
year={2022},
eprint={2204.05862},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@InProceedings{pmlr-v162-ethayarajh22a,
title = {Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information},
author = {Ethayarajh, Kawin and Choi, Yejin and Swayamdipta, Swabha},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {5988--6008},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
}
```
|
DirectLLM/Safe_and_Helpful_Chinese
|
[
"size_categories:1M<n<10M",
"language:zh",
"license:bsd",
"arxiv:2204.05862",
"region:us"
] |
2023-09-13T12:08:38+00:00
|
{"language": ["zh"], "license": "bsd", "size_categories": ["1M<n<10M"]}
|
2023-09-15T11:51:25+00:00
|
[
"2204.05862"
] |
[
"zh"
] |
TAGS
#size_categories-1M<n<10M #language-Chinese #license-bsd #arxiv-2204.05862 #region-us
|
# 数据集
## 简介
我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。
- 数据集内容: pku_helpful/hh_rlhf/SHP
- 翻译器: opus-mt-en-zh
## 处理过程
### 对所有数据集
- 把相同类型的子数据集合并，分为helpful和harmless两组
- 使用翻译模型: 'opus-mt-en-zh'将英文文本翻译为中文
- 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如：
将这类重复出现词语的情况进行清洗，得到：
- 清理其他格式，如ASCII码
### pku_helpful/safety
我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 is_response_0/1_safe 进行处理，如果两者均safe，则标记为is_safe_related=True, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful
对于跟安全相关的数据，根据is_response_0/1_safe 将两个回答均不安全的标记为 is_all_bad=True
- 根据is_all_bad处理输出
- is_all_bad = True：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答：*"抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题”*
- is_all_bad = False：在输出中有一个好的答案和一个坏的答案，处理为"output": [good res, bad res]
- 根据is_safe_related分别将训练集，测试集分为两部分
- 添加 input 并删除 history
- 数据清理后，得到：
### hh_rlhf_helpful/harmful
类似于PKU_Beaver，我们按照提供的数据将源数据集划分为helful和harmful两个子集用于两种模型的训练。
- 将'"output"','"history"'的部分清洗成两组一对，例如：
处理为：
### SHP
该数据集只包含了helpful数据
- 删除'"history"'模块
Thanks for the following works
|
[
"# 数据集",
"## 简介\n\n我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。\n\n- 数据集内容: pku_helpful/hh_rlhf/SHP\n- 翻译器: opus-mt-en-zh",
"## 处理过程",
"### 对所有数据集\n\n- 把相同类型的子数据集合并，分为helpful和harmless两组\n \n- 使用翻译模型: 'opus-mt-en-zh'将英文文本翻译为中文\n \n- 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如：\n \n \n \n 将这类重复出现词语的情况进行清洗，得到：\n \n \n \n- 清理其他格式，如ASCII码",
"### pku_helpful/safety\n我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 is_response_0/1_safe 进行处理，如果两者均safe，则标记为is_safe_related=True, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful\n对于跟安全相关的数据，根据is_response_0/1_safe 将两个回答均不安全的标记为 is_all_bad=True\n- 根据is_all_bad处理输出\n \n - is_all_bad = True：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答：*\"抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题”*\n - is_all_bad = False：在输出中有一个好的答案和一个坏的答案，处理为\"output\": [good res, bad res]\n- 根据is_safe_related分别将训练集，测试集分为两部分\n \n- 添加 input 并删除 history\n \n- 数据清理后，得到：",
"### hh_rlhf_helpful/harmful\n类似于PKU_Beaver，我们按照提供的数据将源数据集划分为helful和harmful两个子集用于两种模型的训练。\n\n- 将'\"output\"','\"history\"'的部分清洗成两组一对，例如：\n \n \n \n 处理为：",
"### SHP\n该数据集只包含了helpful数据\n- 删除'\"history\"'模块\n\n \n\nThanks for the following works"
] |
[
"TAGS\n#size_categories-1M<n<10M #language-Chinese #license-bsd #arxiv-2204.05862 #region-us \n",
"# 数据集",
"## 简介\n\n我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。\n\n- 数据集内容: pku_helpful/hh_rlhf/SHP\n- 翻译器: opus-mt-en-zh",
"## 处理过程",
"### 对所有数据集\n\n- 把相同类型的子数据集合并，分为helpful和harmless两组\n \n- 使用翻译模型: 'opus-mt-en-zh'将英文文本翻译为中文\n \n- 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如：\n \n \n \n 将这类重复出现词语的情况进行清洗，得到：\n \n \n \n- 清理其他格式，如ASCII码",
"### pku_helpful/safety\n我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 is_response_0/1_safe 进行处理，如果两者均safe，则标记为is_safe_related=True, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful\n对于跟安全相关的数据，根据is_response_0/1_safe 将两个回答均不安全的标记为 is_all_bad=True\n- 根据is_all_bad处理输出\n \n - is_all_bad = True：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答：*\"抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题”*\n - is_all_bad = False：在输出中有一个好的答案和一个坏的答案，处理为\"output\": [good res, bad res]\n- 根据is_safe_related分别将训练集，测试集分为两部分\n \n- 添加 input 并删除 history\n \n- 数据清理后，得到：",
"### hh_rlhf_helpful/harmful\n类似于PKU_Beaver，我们按照提供的数据将源数据集划分为helful和harmful两个子集用于两种模型的训练。\n\n- 将'\"output\"','\"history\"'的部分清洗成两组一对，例如：\n \n \n \n 处理为：",
"### SHP\n该数据集只包含了helpful数据\n- 删除'\"history\"'模块\n\n \n\nThanks for the following works"
] |
[
38,
4,
86,
4,
99,
245,
80,
29
] |
[
"passage: TAGS\n#size_categories-1M<n<10M #language-Chinese #license-bsd #arxiv-2204.05862 #region-us \n# 数据集## 简介\n\n我们参考微调LLama2的方式构建中文数据集。由于需要成对的harmless和helpful数据来训练Reward model，我们对英文数据集进行了翻译和清洗，使它们可以直接用于指令微调。\n\n- 数据集内容: pku_helpful/hh_rlhf/SHP\n- 翻译器: opus-mt-en-zh## 处理过程### 对所有数据集\n\n- 把相同类型的子数据集合并，分为helpful和harmless两组\n \n- 使用翻译模型: 'opus-mt-en-zh'将英文文本翻译为中文\n \n- 由于翻译模型的随机性，会出现翻译错误、混淆、重复词语等情况，如：\n \n \n \n 将这类重复出现词语的情况进行清洗，得到：\n \n \n \n- 清理其他格式，如ASCII码### pku_helpful/safety\n我们使用了PKU_Beaver 数据集并进行了处理，根据原始标签 is_response_0/1_safe 进行处理，如果两者均safe，则标记为is_safe_related=True, 将跟安全有关的数据标记作为PKU_safety,反之加入PKU_helpful\n对于跟安全相关的数据，根据is_response_0/1_safe 将两个回答均不安全的标记为 is_all_bad=True\n- 根据is_all_bad处理输出\n \n - is_all_bad = True：输出中的两个答案都不好，所以我们将第一个回答替换为通用的拒绝回答：*\"抱歉，您的问题超出了我可以解决的范畴，请重新提问其他问题”*\n - is_all_bad = False：在输出中有一个好的答案和一个坏的答案，处理为\"output\": [good res, bad res]\n- 根据is_safe_related分别将训练集，测试集分为两部分\n \n- 添加 input 并删除 history\n \n- 数据清理后，得到："
] |
e37f74a1321f80b7eba882f67a4df67e49061219
|
# Dataset Card for Evaluation run of NewstaR/Starlight-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NewstaR/Starlight-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 [NewstaR/Starlight-7B](https://huggingface.co/NewstaR/Starlight-7B) 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_NewstaR__Starlight-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T20:12:12.869101](https://huggingface.co/datasets/open-llm-leaderboard/details_NewstaR__Starlight-7B/blob/main/results_2023-10-28T20-12-12.869101.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": 0.0012583892617449664,
"em_stderr": 0.00036305608931194434,
"f1": 0.055925964765100665,
"f1_stderr": 0.0013181664771628632,
"acc": 0.4057988012013119,
"acc_stderr": 0.00970458141675358
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931194434,
"f1": 0.055925964765100665,
"f1_stderr": 0.0013181664771628632
},
"harness|gsm8k|5": {
"acc": 0.0712661106899166,
"acc_stderr": 0.007086462127954491
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
}
}
```
### 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_NewstaR__Starlight-7B
|
[
"region:us"
] |
2023-09-13T12:12:34+00:00
|
{"pretty_name": "Evaluation run of NewstaR/Starlight-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [NewstaR/Starlight-7B](https://huggingface.co/NewstaR/Starlight-7B) 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_NewstaR__Starlight-7B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T20:12:12.869101](https://huggingface.co/datasets/open-llm-leaderboard/details_NewstaR__Starlight-7B/blob/main/results_2023-10-28T20-12-12.869101.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\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931194434,\n \"f1\": 0.055925964765100665,\n \"f1_stderr\": 0.0013181664771628632,\n \"acc\": 0.4057988012013119,\n \"acc_stderr\": 0.00970458141675358\n },\n \"harness|drop|3\": {\n \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931194434,\n \"f1\": 0.055925964765100665,\n \"f1_stderr\": 0.0013181664771628632\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0712661106899166,\n \"acc_stderr\": 0.007086462127954491\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n }\n}\n```", "repo_url": "https://huggingface.co/NewstaR/Starlight-7B", "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_13T13_12_17.938720", "path": ["**/details_harness|arc:challenge|25_2023-09-13T13-12-17.938720.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T13-12-17.938720.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T20_12_12.869101", "path": ["**/details_harness|drop|3_2023-10-28T20-12-12.869101.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T20-12-12.869101.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T20_12_12.869101", "path": ["**/details_harness|gsm8k|5_2023-10-28T20-12-12.869101.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T20-12-12.869101.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T13_12_17.938720", "path": ["**/details_harness|hellaswag|10_2023-09-13T13-12-17.938720.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T13-12-17.938720.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_13T13_12_17.938720", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T13-12-17.938720.parquet", 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"**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-human_aging|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-human_sexuality|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-international_law|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-jurisprudence|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-management|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-marketing|5_2023-09-13T13-12-17.938720.parquet", "**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T13-12-17.938720.parquet", 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|
2023-10-28T19:12:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of NewstaR/Starlight-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 NewstaR/Starlight-7B 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 2023-10-28T20:12:12.869101(note that their might be results for other tasks in 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 NewstaR/Starlight-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 NewstaR/Starlight-7B 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 2023-10-28T20:12:12.869101(note that their might be results for other tasks in 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 NewstaR/Starlight-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 NewstaR/Starlight-7B 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 2023-10-28T20:12:12.869101(note that their might be results for other tasks in 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 NewstaR/Starlight-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 NewstaR/Starlight-7B 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 2023-10-28T20:12:12.869101(note that their might be results for other tasks in 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"
] |
eabcc0b460ea58e118beb99dd1507788f6a2e19c
|
# Dataset Card for "squad_train_100_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/squad_train_100_eval_10
|
[
"region:us"
] |
2023-09-13T12:31:17+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_id", "dtype": "string"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1610094, "num_examples": 1017}, {"name": "eval_qa", "num_bytes": 62544, "num_examples": 53}], "download_size": 0, "dataset_size": 1672638}}
|
2023-09-13T12:31:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "squad_train_100_eval_10"
More Information needed
|
[
"# Dataset Card for \"squad_train_100_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_train_100_eval_10\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"squad_train_100_eval_10\"\n\nMore Information needed"
] |
c64ad769a97f10cbea2fd91f063f021d0963bf24
|
# Dataset Card for "squad_id_train_100_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/squad_id_train_100_eval_10
|
[
"region:us"
] |
2023-09-13T12:31:44+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_id", "dtype": "string"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1610094, "num_examples": 1017}, {"name": "validation", "num_bytes": 62544, "num_examples": 53}], "download_size": 29364, "dataset_size": 1672638}}
|
2023-09-13T12:51:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "squad_id_train_100_eval_10"
More Information needed
|
[
"# Dataset Card for \"squad_id_train_100_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_id_train_100_eval_10\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"squad_id_train_100_eval_10\"\n\nMore Information needed"
] |
fa909b0c48f967d494aa7cbbfa4d43fbe39b51b7
|
# Dataset Card for Evaluation run of elinas/chronos-70b-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elinas/chronos-70b-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [elinas/chronos-70b-v2](https://huggingface.co/elinas/chronos-70b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_elinas__chronos-70b-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T18:55:58.874073](https://huggingface.co/datasets/open-llm-leaderboard/details_elinas__chronos-70b-v2/blob/main/results_2023-10-29T18-55-58.874073.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": 0.004928691275167785,
"em_stderr": 0.0007171872517059785,
"f1": 0.07059983221476518,
"f1_stderr": 0.0015017323147269025,
"acc": 0.5493677195598541,
"acc_stderr": 0.011716161385886395
},
"harness|drop|3": {
"em": 0.004928691275167785,
"em_stderr": 0.0007171872517059785,
"f1": 0.07059983221476518,
"f1_stderr": 0.0015017323147269025
},
"harness|gsm8k|5": {
"acc": 0.28658074298711145,
"acc_stderr": 0.0124548416683377
},
"harness|winogrande|5": {
"acc": 0.8121546961325967,
"acc_stderr": 0.01097748110343509
}
}
```
### 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_elinas__chronos-70b-v2
|
[
"region:us"
] |
2023-09-13T12:40:03+00:00
|
{"pretty_name": "Evaluation run of elinas/chronos-70b-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [elinas/chronos-70b-v2](https://huggingface.co/elinas/chronos-70b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_elinas__chronos-70b-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T18:55:58.874073](https://huggingface.co/datasets/open-llm-leaderboard/details_elinas__chronos-70b-v2/blob/main/results_2023-10-29T18-55-58.874073.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\": 0.004928691275167785,\n \"em_stderr\": 0.0007171872517059785,\n \"f1\": 0.07059983221476518,\n \"f1_stderr\": 0.0015017323147269025,\n \"acc\": 0.5493677195598541,\n \"acc_stderr\": 0.011716161385886395\n },\n \"harness|drop|3\": {\n \"em\": 0.004928691275167785,\n \"em_stderr\": 0.0007171872517059785,\n \"f1\": 0.07059983221476518,\n \"f1_stderr\": 0.0015017323147269025\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.28658074298711145,\n \"acc_stderr\": 0.0124548416683377\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8121546961325967,\n \"acc_stderr\": 0.01097748110343509\n }\n}\n```", "repo_url": "https://huggingface.co/elinas/chronos-70b-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_13T13_39_47.778697", "path": ["**/details_harness|arc:challenge|25_2023-09-13T13-39-47.778697.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T13-39-47.778697.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T18_55_58.874073", "path": ["**/details_harness|drop|3_2023-10-29T18-55-58.874073.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T18-55-58.874073.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T18_55_58.874073", "path": ["**/details_harness|gsm8k|5_2023-10-29T18-55-58.874073.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T18-55-58.874073.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T13_39_47.778697", "path": ["**/details_harness|hellaswag|10_2023-09-13T13-39-47.778697.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T13-39-47.778697.parquet"]}]}, 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2023-10-29T18:56:12+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of elinas/chronos-70b-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model elinas/chronos-70b-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-29T18:55:58.874073(note that their might be results for other tasks in 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 elinas/chronos-70b-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model elinas/chronos-70b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-29T18:55:58.874073(note that their might be results for other tasks in 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 elinas/chronos-70b-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model elinas/chronos-70b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-29T18:55:58.874073(note that their might be results for other tasks in 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 elinas/chronos-70b-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model elinas/chronos-70b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-29T18:55:58.874073(note that their might be results for other tasks in 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"
] |
c0e4c4c8b66dc458d138c864ea0a6787f8d5a8df
|
An Alpaca instruction conversion of [Grammarly's CoEdIT](https://huggingface.co/datasets/grammarly/coedit) dataset.
|
Gryphe/CoEdit-Alpaca
|
[
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-09-13T12:49:47+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"]}
|
2023-09-14T10:28:44+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #region-us
|
An Alpaca instruction conversion of Grammarly's CoEdIT dataset.
|
[] |
[
"TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #region-us \n"
] |
[
29
] |
[
"passage: TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #region-us \n"
] |
04d7cd8a6331d84d3e19cda68cab15b70a7065e6
|
# Dataset Card for Evaluation run of NewstaR/Starlight-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NewstaR/Starlight-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 [NewstaR/Starlight-13B](https://huggingface.co/NewstaR/Starlight-13B) 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_NewstaR__Starlight-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T13:47:33.408726](https://huggingface.co/datasets/open-llm-leaderboard/details_NewstaR__Starlight-13B/blob/main/results_2023-10-27T13-47-33.408726.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": 0.0014681208053691276,
"em_stderr": 0.00039210421902982666,
"f1": 0.0607822986577181,
"f1_stderr": 0.0013583957676382913,
"acc": 0.43739636770101,
"acc_stderr": 0.010228023491905505
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.00039210421902982666,
"f1": 0.0607822986577181,
"f1_stderr": 0.0013583957676382913
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627487
},
"harness|winogrande|5": {
"acc": 0.7663772691397001,
"acc_stderr": 0.011892194477183524
}
}
```
### 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_NewstaR__Starlight-13B
|
[
"region:us"
] |
2023-09-13T12:54:31+00:00
|
{"pretty_name": "Evaluation run of NewstaR/Starlight-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [NewstaR/Starlight-13B](https://huggingface.co/NewstaR/Starlight-13B) 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_NewstaR__Starlight-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T13:47:33.408726](https://huggingface.co/datasets/open-llm-leaderboard/details_NewstaR__Starlight-13B/blob/main/results_2023-10-27T13-47-33.408726.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\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902982666,\n \"f1\": 0.0607822986577181,\n \"f1_stderr\": 0.0013583957676382913,\n \"acc\": 0.43739636770101,\n \"acc_stderr\": 0.010228023491905505\n },\n \"harness|drop|3\": {\n \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902982666,\n \"f1\": 0.0607822986577181,\n \"f1_stderr\": 0.0013583957676382913\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \"acc_stderr\": 0.008563852506627487\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7663772691397001,\n \"acc_stderr\": 0.011892194477183524\n }\n}\n```", "repo_url": "https://huggingface.co/NewstaR/Starlight-13B", "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_13T13_54_15.182545", "path": ["**/details_harness|arc:challenge|25_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_27T13_47_33.408726", "path": ["**/details_harness|drop|3_2023-10-27T13-47-33.408726.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-27T13-47-33.408726.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_27T13_47_33.408726", "path": ["**/details_harness|gsm8k|5_2023-10-27T13-47-33.408726.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-27T13-47-33.408726.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hellaswag|10_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-management|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-marketing|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T13-54-15.182545.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T13-54-15.182545.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_27T13_47_33.408726", "path": ["**/details_harness|winogrande|5_2023-10-27T13-47-33.408726.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-27T13-47-33.408726.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T13_54_15.182545", "path": ["results_2023-09-13T13-54-15.182545.parquet"]}, {"split": "2023_10_27T13_47_33.408726", "path": ["results_2023-10-27T13-47-33.408726.parquet"]}, {"split": "latest", "path": ["results_2023-10-27T13-47-33.408726.parquet"]}]}]}
|
2023-10-27T12:47:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of NewstaR/Starlight-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 NewstaR/Starlight-13B 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 2023-10-27T13:47:33.408726(note that their might be results for other tasks in 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 NewstaR/Starlight-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 NewstaR/Starlight-13B 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 2023-10-27T13:47:33.408726(note that their might be results for other tasks in 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 NewstaR/Starlight-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 NewstaR/Starlight-13B 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 2023-10-27T13:47:33.408726(note that their might be results for other tasks in 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 NewstaR/Starlight-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 NewstaR/Starlight-13B 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 2023-10-27T13:47:33.408726(note that their might be results for other tasks in 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"
] |
e5fab135faac96f91ce0f383d8d5d424de2010e8
|
# Bangumi Image Base of Asobi Asobase
This is the image base of bangumi Asobi Asobase, we detected 33 characters, 3159 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 | 483 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 149 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 65 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 14 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 22 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 9 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 9 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 11 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 829 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 25 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 117 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 31 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 89 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 157 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 31 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 43 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 647 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 13 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 70 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 21 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 22 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 30 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 13 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 11 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 44 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 20 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 8 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 9 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 10 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 6 | [Download](31/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 106 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/asobiasobase
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-09-13T13:04:19+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T06:14:24+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Asobi Asobase
===================================
This is the image base of bangumi Asobi Asobase, we detected 33 characters, 3159 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
|
[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
0aed3481ffa950df8343dd86d4b15e79bbc6097e
|
# Dataset of sakurai_momoka/櫻井桃華 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of sakurai_momoka/櫻井桃華 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `blonde_hair, green_eyes, short_hair, hairband, bangs, bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 679.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakurai_momoka_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 373.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakurai_momoka_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1261 | 843.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakurai_momoka_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 592.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakurai_momoka_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1261 | 1.20 GiB | [Download](https://huggingface.co/datasets/CyberHarem/sakurai_momoka_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/sakurai_momoka_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 29 |  |  |  |  |  | 1girl, smile, looking_at_viewer, solo, blush, hair_flower, braid, rose, white_gloves, petals, open_mouth, red_dress, white_thighhighs |
| 1 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, smile, blush, dress, lolita_hairband, solo, white_background |
| 2 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, puffy_short_sleeves, red_dress, simple_background, smile, solo, white_background, white_shirt, black_footwear, black_ribbon, frilled_dress, full_body, mary_janes, neck_ribbon, standing, closed_mouth, pinafore_dress, white_socks, blush, bobby_socks, hair_between_eyes, red_hairband, skirt_hold, wavy_hair |
| 3 | 11 |  |  |  |  |  | 1girl, blush, solo, hair_flower, looking_at_viewer, red_dress, black_bow, hair_between_eyes, pink_rose, short_over_long_sleeves, frilled_hairband, simple_background, white_background, closed_mouth, puffy_short_sleeves, black_hairband, red_flower, :d, black_thighhighs, frilled_dress, open_mouth |
| 4 | 7 |  |  |  |  |  | 1girl, beret, blue_dress, blush, looking_at_viewer, puffy_short_sleeves, solo, smile, blue_headwear, hair_between_eyes, twin_braids, frilled_dress, plaid_dress, wrist_cuffs, blue_belt, closed_mouth, leaf, pink_bowtie, pleated_dress, shirt, simple_background, white_background |
| 5 | 5 |  |  |  |  |  | 1girl, blush, open_mouth, simple_background, solo, :d, hair_between_eyes, looking_at_viewer, medium_hair, upper_body, hair_bow, pink_hairband, white_background, collarbone, pink_dress, shirt, short_sleeves, wavy_hair, white_dress |
| 6 | 16 |  |  |  |  |  | blush, gym_uniform, white_shirt, 1girl, short_sleeves, solo, looking_at_viewer, gym_shirt, red_hairband, name_tag, red_shorts, open_mouth, simple_background, hair_between_eyes, wavy_hair, white_background, :d, gym_shorts |
| 7 | 17 |  |  |  |  |  | 1girl, long_sleeves, blush, pleated_skirt, solo, blue_skirt, looking_at_viewer, red_bow, blue_shirt, smile, simple_background, white_thighhighs, bowtie, hat, white_background, white_sailor_collar, blue_serafuku, hair_between_eyes, open_mouth, randoseru, zettai_ryouiki, blue_headwear, wavy_hair |
| 8 | 18 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, loli, nipples, nude, pussy, small_breasts, smile, navel, open_mouth, simple_background, white_background, uncensored, barefoot, cleft_of_venus, flat_chest, lying, anus |
| 9 | 6 |  |  |  |  |  | blush, looking_at_viewer, ponytail, shirt, solo, 1girl, blue_skirt, cheerleader, midriff, pleated_skirt, simple_background, smile, white_background, bike_shorts, crop_top, holding_pom_poms, navel, one_eye_closed, shorts_under_skirt, sleeveless, sneakers, sweat, armpits, blue_bow, hair_bow, open_mouth, white_socks |
| 10 | 14 |  |  |  |  |  | 1girl, blue_one-piece_swimsuit, competition_school_swimsuit, blush, white_background, looking_at_viewer, simple_background, wavy_hair, solo, small_breasts, ribbon, thighs, beachball, collarbone, name_tag, ass, cowboy_shot, smile, covered_navel, hair_between_eyes, shoes, socks |
| 11 | 5 |  |  |  |  |  | barefoot, blue_one-piece_swimsuit, blush, grey_background, looking_at_viewer, simple_background, small_breasts, 1girl, covered_navel, kneeling, twitter_username, wavy_hair, bare_arms, bare_legs, brown_background, closed_mouth, collarbone, hair_between_eyes, medium_hair, old_school_swimsuit, smile, armpits, arms_behind_head, arms_up, ass_visible_through_thighs, bare_shoulders, hair_bow, multiple_girls, red_bow, red_hairband, solo_focus |
| 12 | 6 |  |  |  |  |  | 1girl, hetero, penis, solo_focus, 1boy, flower, handjob, open_mouth, loli, mosaic_censoring, nude, blush, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | looking_at_viewer | solo | blush | hair_flower | braid | rose | white_gloves | petals | open_mouth | red_dress | white_thighhighs | dress | lolita_hairband | white_background | puffy_short_sleeves | simple_background | white_shirt | black_footwear | black_ribbon | frilled_dress | full_body | mary_janes | neck_ribbon | standing | closed_mouth | pinafore_dress | white_socks | bobby_socks | hair_between_eyes | red_hairband | skirt_hold | wavy_hair | black_bow | pink_rose | short_over_long_sleeves | frilled_hairband | black_hairband | red_flower | :d | black_thighhighs | beret | blue_dress | blue_headwear | twin_braids | plaid_dress | wrist_cuffs | blue_belt | leaf | pink_bowtie | pleated_dress | shirt | medium_hair | upper_body | hair_bow | pink_hairband | collarbone | pink_dress | short_sleeves | white_dress | gym_uniform | gym_shirt | name_tag | red_shorts | gym_shorts | long_sleeves | pleated_skirt | blue_skirt | red_bow | blue_shirt | bowtie | hat | white_sailor_collar | blue_serafuku | randoseru | zettai_ryouiki | loli | nipples | nude | pussy | small_breasts | navel | uncensored | barefoot | cleft_of_venus | flat_chest | lying | anus | ponytail | cheerleader | midriff | bike_shorts | crop_top | holding_pom_poms | one_eye_closed | shorts_under_skirt | sleeveless | sneakers | sweat | armpits | blue_bow | blue_one-piece_swimsuit | competition_school_swimsuit | ribbon | thighs | beachball | ass | cowboy_shot | covered_navel | shoes | socks | grey_background | kneeling | twitter_username | bare_arms | bare_legs | brown_background | old_school_swimsuit | arms_behind_head | arms_up | ass_visible_through_thighs | bare_shoulders | multiple_girls | solo_focus | hetero | penis | 1boy | flower | handjob | mosaic_censoring |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:--------------------|:-------|:--------|:--------------|:--------|:-------|:---------------|:---------|:-------------|:------------|:-------------------|:--------|:------------------|:-------------------|:----------------------|:--------------------|:--------------|:-----------------|:---------------|:----------------|:------------|:-------------|:--------------|:-----------|:---------------|:-----------------|:--------------|:--------------|:--------------------|:---------------|:-------------|:------------|:------------|:------------|:--------------------------|:-------------------|:-----------------|:-------------|:-----|:-------------------|:--------|:-------------|:----------------|:--------------|:--------------|:--------------|:------------|:-------|:--------------|:----------------|:--------|:--------------|:-------------|:-----------|:----------------|:-------------|:-------------|:----------------|:--------------|:--------------|:------------|:-----------|:-------------|:-------------|:---------------|:----------------|:-------------|:----------|:-------------|:---------|:------|:----------------------|:----------------|:------------|:-----------------|:-------|:----------|:-------|:--------|:----------------|:--------|:-------------|:-----------|:-----------------|:-------------|:--------|:-------|:-----------|:--------------|:----------|:--------------|:-----------|:-------------------|:-----------------|:---------------------|:-------------|:-----------|:--------|:----------|:-----------|:--------------------------|:------------------------------|:---------|:---------|:------------|:------|:--------------|:----------------|:--------|:--------|:------------------|:-----------|:-------------------|:------------|:------------|:-------------------|:----------------------|:-------------------|:----------|:-----------------------------|:-----------------|:-----------------|:-------------|:---------|:--------|:-------|:---------|:----------|:-------------------|
| 0 | 29 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | X | X | X | | | | | | X | | | | | X | | X | | | | | | | | | | | | | X | | | X | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 16 |  |  |  |  |  | X | | X | X | X | | | | | | X | | | | | X | | X | X | | | | | | | | | | | | X | X | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 17 |  |  |  |  |  | X | X | X | X | X | | | | | | X | | X | | | X | | X | | | | | | | | | | | | | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 18 |  |  |  |  |  | X | X | X | X | X | | | | | | X | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 14 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | X | | X | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | X | X | X | | X | | | | | | | | | | | | | X | | | | | | | | | X | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | X | | X | | X | | | | | | | | | | | | X | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | X | | X | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 12 | 6 |  |  |  |  |  | X | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X |
|
CyberHarem/sakurai_momoka_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T13:16:59+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T13:25:47+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of sakurai\_momoka/櫻井桃華 (THE iDOLM@STER: Cinderella Girls)
==================================================================
This is the dataset of sakurai\_momoka/櫻井桃華 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'blonde\_hair, green\_eyes, short\_hair, hairband, bangs, bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
685fc7496dcc9d4d72469733a6dffdf66d9ab32b
|
# Dataset Card for "clevr-full-v6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
erkam/clevr-full-v6
|
[
"region:us"
] |
2023-09-13T13:19:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "depth", "dtype": "image"}, {"name": "layout", "dtype": "image"}, {"name": "colored_layout", "dtype": "image"}, {"name": "objects", "sequence": "int64"}, {"name": "boxes", "sequence": {"sequence": "float32"}}, {"name": "triplets", "sequence": {"sequence": "int64"}}, {"name": "objects_str", "dtype": "string"}, {"name": "depth_latent", "sequence": {"sequence": {"sequence": "float32"}}}, {"name": "image_latent", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 104696506.0, "num_examples": 960}, {"name": "val", "num_bytes": 12961636.0, "num_examples": 119}, {"name": "test", "num_bytes": 12938095.0, "num_examples": 119}], "download_size": 143558769, "dataset_size": 130596237.0}}
|
2023-09-13T13:20:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "clevr-full-v6"
More Information needed
|
[
"# Dataset Card for \"clevr-full-v6\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"clevr-full-v6\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"clevr-full-v6\"\n\nMore Information needed"
] |
1063bbab079d0371bda167ba22dbdc233bcff900
|
# Dataset Card for "sentiment-analysis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sinarashidi/sentiment-analysis
|
[
"region:us"
] |
2023-09-13T13:20:18+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": 44032051, "num_examples": 128432}], "download_size": 19743452, "dataset_size": 44032051}}
|
2023-09-13T13:20:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sentiment-analysis"
More Information needed
|
[
"# Dataset Card for \"sentiment-analysis\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sentiment-analysis\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sentiment-analysis\"\n\nMore Information needed"
] |
cad025eaf9aaf5e944c71124e149b065eda02764
|
# Dataset Card for Evaluation run of migtissera/Synthia-70B-v1.2b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Synthia-70B-v1.2b
- **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 [migtissera/Synthia-70B-v1.2b](https://huggingface.co/migtissera/Synthia-70B-v1.2b) 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_migtissera__Synthia-70B-v1.2b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T18:54:59.551883](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b/blob/main/results_2023-10-24T18-54-59.551883.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": 0.44190436241610737,
"em_stderr": 0.00508578632439048,
"f1": 0.5040551593959751,
"f1_stderr": 0.00484284160320387,
"acc": 0.5957647712115981,
"acc_stderr": 0.011744811294358018
},
"harness|drop|3": {
"em": 0.44190436241610737,
"em_stderr": 0.00508578632439048,
"f1": 0.5040551593959751,
"f1_stderr": 0.00484284160320387
},
"harness|gsm8k|5": {
"acc": 0.3525398028809704,
"acc_stderr": 0.013159909755930321
},
"harness|winogrande|5": {
"acc": 0.8389897395422258,
"acc_stderr": 0.010329712832785717
}
}
```
### 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_migtissera__Synthia-70B-v1.2b
|
[
"region:us"
] |
2023-09-13T13:25:51+00:00
|
{"pretty_name": "Evaluation run of migtissera/Synthia-70B-v1.2b", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/Synthia-70B-v1.2b](https://huggingface.co/migtissera/Synthia-70B-v1.2b) 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_migtissera__Synthia-70B-v1.2b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T18:54:59.551883](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.2b/blob/main/results_2023-10-24T18-54-59.551883.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\": 0.44190436241610737,\n \"em_stderr\": 0.00508578632439048,\n \"f1\": 0.5040551593959751,\n \"f1_stderr\": 0.00484284160320387,\n \"acc\": 0.5957647712115981,\n \"acc_stderr\": 0.011744811294358018\n },\n \"harness|drop|3\": {\n \"em\": 0.44190436241610737,\n \"em_stderr\": 0.00508578632439048,\n \"f1\": 0.5040551593959751,\n \"f1_stderr\": 0.00484284160320387\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3525398028809704,\n \"acc_stderr\": 0.013159909755930321\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785717\n }\n}\n```", "repo_url": "https://huggingface.co/migtissera/Synthia-70B-v1.2b", "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_13T14_25_34.731307", "path": ["**/details_harness|arc:challenge|25_2023-09-13T14-25-34.731307.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T14-25-34.731307.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T18_54_59.551883", "path": ["**/details_harness|drop|3_2023-10-24T18-54-59.551883.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T18-54-59.551883.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T18_54_59.551883", "path": ["**/details_harness|gsm8k|5_2023-10-24T18-54-59.551883.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T18-54-59.551883.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T14_25_34.731307", "path": ["**/details_harness|hellaswag|10_2023-09-13T14-25-34.731307.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T14-25-34.731307.parquet"]}]}, 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["**/details_harness|truthfulqa:mc|0_2023-09-13T14-25-34.731307.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T14-25-34.731307.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_24T18_54_59.551883", "path": ["**/details_harness|winogrande|5_2023-10-24T18-54-59.551883.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-24T18-54-59.551883.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T14_25_34.731307", "path": ["results_2023-09-13T14-25-34.731307.parquet"]}, {"split": "2023_10_24T18_54_59.551883", "path": ["results_2023-10-24T18-54-59.551883.parquet"]}, {"split": "latest", "path": ["results_2023-10-24T18-54-59.551883.parquet"]}]}]}
|
2023-10-24T17:55:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of migtissera/Synthia-70B-v1.2b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model migtissera/Synthia-70B-v1.2b 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 2023-10-24T18:54:59.551883(note that their might be results for other tasks in 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 migtissera/Synthia-70B-v1.2b",
"## 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 migtissera/Synthia-70B-v1.2b 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 2023-10-24T18:54:59.551883(note that their might be results for other tasks in 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 migtissera/Synthia-70B-v1.2b",
"## 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 migtissera/Synthia-70B-v1.2b 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 2023-10-24T18:54:59.551883(note that their might be results for other tasks in 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 migtissera/Synthia-70B-v1.2b## 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 migtissera/Synthia-70B-v1.2b 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 2023-10-24T18:54:59.551883(note that their might be results for other tasks in 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"
] |
4eaaac408f8c52aa9665082d281010a4064a0167
|
# Dataset Card for Evaluation run of bsp-albz/llama2-13b-platypus-ckpt-1000
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000
- **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 [bsp-albz/llama2-13b-platypus-ckpt-1000](https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000) 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_bsp-albz__llama2-13b-platypus-ckpt-1000",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T07:27:50.665316](https://huggingface.co/datasets/open-llm-leaderboard/details_bsp-albz__llama2-13b-platypus-ckpt-1000/blob/main/results_2023-10-28T07-27-50.665316.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": 0.0,
"em_stderr": 0.0,
"f1": 9.123322147651008e-05,
"f1_stderr": 2.468836036222127e-05,
"acc": 0.24506708760852408,
"acc_stderr": 0.007024874916683796
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 9.123322147651008e-05,
"f1_stderr": 2.468836036222127e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.49013417521704816,
"acc_stderr": 0.014049749833367592
}
}
```
### 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_bsp-albz__llama2-13b-platypus-ckpt-1000
|
[
"region:us"
] |
2023-09-13T13:31:17+00:00
|
{"pretty_name": "Evaluation run of bsp-albz/llama2-13b-platypus-ckpt-1000", "dataset_summary": "Dataset automatically created during the evaluation run of model [bsp-albz/llama2-13b-platypus-ckpt-1000](https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000) 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_bsp-albz__llama2-13b-platypus-ckpt-1000\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T07:27:50.665316](https://huggingface.co/datasets/open-llm-leaderboard/details_bsp-albz__llama2-13b-platypus-ckpt-1000/blob/main/results_2023-10-28T07-27-50.665316.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\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 9.123322147651008e-05,\n \"f1_stderr\": 2.468836036222127e-05,\n \"acc\": 0.24506708760852408,\n \"acc_stderr\": 0.007024874916683796\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 9.123322147651008e-05,\n \"f1_stderr\": 2.468836036222127e-05\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.49013417521704816,\n \"acc_stderr\": 0.014049749833367592\n }\n}\n```", "repo_url": "https://huggingface.co/bsp-albz/llama2-13b-platypus-ckpt-1000", "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_13T14_31_01.492634", "path": ["**/details_harness|arc:challenge|25_2023-09-13T14-31-01.492634.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T14-31-01.492634.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T07_27_50.665316", "path": ["**/details_harness|drop|3_2023-10-28T07-27-50.665316.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T07-27-50.665316.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T07_27_50.665316", "path": ["**/details_harness|gsm8k|5_2023-10-28T07-27-50.665316.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T07-27-50.665316.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T14_31_01.492634", "path": ["**/details_harness|hellaswag|10_2023-09-13T14-31-01.492634.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T14-31-01.492634.parquet"]}]}, 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|
2023-10-28T06:28:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of bsp-albz/llama2-13b-platypus-ckpt-1000
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model bsp-albz/llama2-13b-platypus-ckpt-1000 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 2023-10-28T07:27:50.665316(note that their might be results for other tasks in 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 bsp-albz/llama2-13b-platypus-ckpt-1000",
"## 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 bsp-albz/llama2-13b-platypus-ckpt-1000 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 2023-10-28T07:27:50.665316(note that their might be results for other tasks in 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 bsp-albz/llama2-13b-platypus-ckpt-1000",
"## 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 bsp-albz/llama2-13b-platypus-ckpt-1000 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 2023-10-28T07:27:50.665316(note that their might be results for other tasks in 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 bsp-albz/llama2-13b-platypus-ckpt-1000## 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 bsp-albz/llama2-13b-platypus-ckpt-1000 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 2023-10-28T07:27:50.665316(note that their might be results for other tasks in 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"
] |
23bddc1d60d557fa851257a73225881646f0cc75
|
# Tweet Annotation Sensitivity Experiment 2: Annotations in Five Experimental Conditions
***<font color= red>Attention: This repository contains cases that might be offensive or upsetting. We do not support the views expressed in these hateful posts.</font>***
## Description
The dataset contains tweet data annotations of **hate speech** (HS) and **offensive language** (OL) in five experimental conditions. The tweet data was sampled from the corpus created by [Davidson et al. (2017)](https://ojs.aaai.org/index.php/ICWSM/article/view/14955). We selected 3,000 Tweets for our annotation. We developed five experimental conditions that varied the annotation task structure, as shown in the following figure. All tweets were annotated in each condition.
- **<font color= #871F78>Condition A</font>** presented the tweet and three options on a single screen: hate speech, offensive language, or neither. Annotators could select one or both of hate speech, offensive language, or indicate that neither applied.
- Conditions B and C split the annotation of a single tweet across two screens.
+ For **<font color= Blue>Condition B</font>**, the first screen prompted the annotator to indicate whether the tweet contained hate speech. On the following screen, they were shown the tweet again and asked whether it contained offensive language.
+ **<font color= red>Condition C</font>** was similar to Condition B, but flipped the order of hate speech and offensive language for each tweet.
- In Conditions D and E, the two tasks are treated independently with annotators being asked to first annotate all tweets for one task, followed by annotating all tweets again for the second task.
+ Annotators assigned **<font color=green>Condition D</font>** were first asked to annotate hate speech for all their assigned tweets, and then asked to annotate offensive language for the same set of tweets.
+ **Condition E** worked the same way, but started with the offensive language annotation task followed by the hate speech annotation task.
We recruited US-based annotators from the crowdsourcing platform [Prolific](https://www.prolific.com/) during November and December 2022. Each annotator annotated up to 50 tweets. The dataset also contains demographic information about the annotators. Annotators received a fixed hourly wage in excess of the US federal minimum wage after completing the task.
<img src="https://raw.githubusercontent.com/chkern/tweet-annotation-sensitivity/main/fig/exp_conditions.png" width = "300" height = "200" alt="" align=center />
## Codebook
| Column Name | Description | Type |
| -------------- | ------------------ |---------------- |
| case_id | case ID | integer |
| duration_seconds | duration of connection to task in seconds | integer |
| last_screen | last question answered | factor |
| device | device type | factor |
| ethn_hispanic | Hispanic race/ethnicity | binary |
| ethn_white | White race/ethnicity | binary |
| ethn_afr_american | African-American race/ethnicity | binary |
| ethn_asian | Asian race/ethnicity | binary |
| ethn_sth_else | race/ethnicity something else | binary |
| ethn_prefer_not | race/ethnicity prefer not to say | binary |
| age | age | integer |
| education | education attainment <br>1: Less than high school <br>2: High school <br>3: Some college <br>4: College graduate <br>5: Master's degree or professional degree (law, medicine, MPH, etc.) <br>6: Doctoral degree (PhD, DPH, EdD, etc.)| factor |
| english_fl | English as first language | binary |
| twitter_use | Twitter use frequency <br>1: Most days <br>2: Most weeks, but not every day <br>3: A few times a month <br>4: A few times a year <br>5: Less often <br>6: Never | factor |
| socmedia_use | social media use frequency <br>1: Most days <br>2: Most weeks, but not every day <br>3: A few times a month <br>4: A few times a year <br>5: Less often <br>6: Never | factor |
| prolific_hours | workload on the platform prolific in hours in the last month | integer |
| task_fun | task perception: fun | binary |
| task_interesting | task perception: interesting | binary |
| task_boring | task perception: boring | binary |
| task_repetitive | task perception: repetitive | binary |
| task_important | task perception: important | binary |
| task_depressing | task perception: depressing | binary |
| task_offensive | task perception: offensive | binary |
| repeat_tweet_coding | likelihood for another tweet task <br>1: Not at all likely <br>2: Somewhat likely <br>3: Very likely | factor |
| repeat_hs_coding | likelihood for another hate speech task <br>1: Not at all likely <br>2: Somewhat likely <br>3: Very likely | factor |
| target_online_harassment | targeted by hateful online behavior | binary |
| target_other_harassment | targeted by other hateful behavior | binary |
| party_affiliation | party identification <br>1: Republican <br>2: Democrat <br>3: Independent | factor |
| societal_relevance_hs | relevance perception of hate speech <br>1: Not at all likely <br>2: Somewhat likely <br>3: Very likely | factor |
| annotator_id | annotator ID | integer |
| condition | experimental conditions (A-E) | factor |
| tweet_batch | tweet ID in batch | factor |
| hate_speech | hate speech annotation | logical |
| offensive_language | offensive language annotation | logical |
| tweet_id | tweet ID | integer |
| orig_label_hs | number of persons who annotated the tweet as hate speech in the original dataset from [Davidson et al. (2017)](https://ojs.aaai.org/index.php/ICWSM/article/view/14955) | integer |
| orig_label_ol | number of persons who annotated the tweet as offensive language in the original dataset from [Davidson et al. (2017)](https://ojs.aaai.org/index.php/ICWSM/article/view/14955) | integer |
| orig_label_ne | number of persons who annotated the tweet as neither in the original dataset from [Davidson et al. (2017)](https://ojs.aaai.org/index.php/ICWSM/article/view/14955) | integer |
| tweet_hashed | tweet with usernames hashed | character |
## Citation
If you find the dataset useful, please cite:
```
@inproceedings{kern-etal-2023-annotation,
title = "Annotation Sensitivity: Training Data Collection Methods Affect Model Performance",
author = "Kern, Christoph and
Eckman, Stephanie and
Beck, Jacob and
Chew, Rob and
Ma, Bolei and
Kreuter, Frauke",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.992",
pages = "14874--14886",
}
@inproceedings{beck-etal-2024-order,
title = "Order Effects in Annotation Tasks: Further Evidence of Annotation Sensitivity",
author = "Beck, Jacob and
Eckman, Stephanie and
Ma, Bolei and
Chew, Rob and
Kreuter, Frauke",
booktitle = "Proceedings of the First Workshop on Uncertainty-Aware NLP",
month = mar,
year = "2024",
address = "Malta",
publisher = "Association for Computational Linguistics",
}
```
|
soda-lmu/tweet-annotation-sensitivity-2
|
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:hate-speech-detection",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
2023-09-13T13:35:57+00:00
|
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "task_ids": ["sentiment-classification", "hate-speech-detection"]}
|
2024-01-29T19:12:00+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #task_ids-sentiment-classification #task_ids-hate-speech-detection #size_categories-10K<n<100K #language-English #region-us
|
Tweet Annotation Sensitivity Experiment 2: Annotations in Five Experimental Conditions
======================================================================================
*Attention: This repository contains cases that might be offensive or upsetting. We do not support the views expressed in these hateful posts.*
Description
-----------
The dataset contains tweet data annotations of hate speech (HS) and offensive language (OL) in five experimental conditions. The tweet data was sampled from the corpus created by Davidson et al. (2017). We selected 3,000 Tweets for our annotation. We developed five experimental conditions that varied the annotation task structure, as shown in the following figure. All tweets were annotated in each condition.
* Condition A presented the tweet and three options on a single screen: hate speech, offensive language, or neither. Annotators could select one or both of hate speech, offensive language, or indicate that neither applied.
* Conditions B and C split the annotation of a single tweet across two screens.
+ For Condition B, the first screen prompted the annotator to indicate whether the tweet contained hate speech. On the following screen, they were shown the tweet again and asked whether it contained offensive language.
+ Condition C was similar to Condition B, but flipped the order of hate speech and offensive language for each tweet.
* In Conditions D and E, the two tasks are treated independently with annotators being asked to first annotate all tweets for one task, followed by annotating all tweets again for the second task.
+ Annotators assigned Condition D were first asked to annotate hate speech for all their assigned tweets, and then asked to annotate offensive language for the same set of tweets.
+ Condition E worked the same way, but started with the offensive language annotation task followed by the hate speech annotation task.
We recruited US-based annotators from the crowdsourcing platform Prolific during November and December 2022. Each annotator annotated up to 50 tweets. The dataset also contains demographic information about the annotators. Annotators received a fixed hourly wage in excess of the US federal minimum wage after completing the task.
<img src="URL width = "300" height = "200" alt="" align=center />
Codebook
--------
Column Name: case\_id, Description: case ID, Type: integer
Column Name: duration\_seconds, Description: duration of connection to task in seconds, Type: integer
Column Name: last\_screen, Description: last question answered, Type: factor
Column Name: device, Description: device type, Type: factor
Column Name: ethn\_hispanic, Description: Hispanic race/ethnicity, Type: binary
Column Name: ethn\_white, Description: White race/ethnicity, Type: binary
Column Name: ethn\_afr\_american, Description: African-American race/ethnicity, Type: binary
Column Name: ethn\_asian, Description: Asian race/ethnicity, Type: binary
Column Name: ethn\_sth\_else, Description: race/ethnicity something else, Type: binary
Column Name: ethn\_prefer\_not, Description: race/ethnicity prefer not to say, Type: binary
Column Name: age, Description: age, Type: integer
Column Name: education, Description: education attainment
1: Less than high school
2: High school
3: Some college
4: College graduate
5: Master's degree or professional degree (law, medicine, MPH, etc.)
6: Doctoral degree (PhD, DPH, EdD, etc.), Type: factor
Column Name: english\_fl, Description: English as first language, Type: binary
Column Name: twitter\_use, Description: Twitter use frequency
1: Most days
2: Most weeks, but not every day
3: A few times a month
4: A few times a year
5: Less often
6: Never, Type: factor
Column Name: socmedia\_use, Description: social media use frequency
1: Most days
2: Most weeks, but not every day
3: A few times a month
4: A few times a year
5: Less often
6: Never, Type: factor
Column Name: prolific\_hours, Description: workload on the platform prolific in hours in the last month, Type: integer
Column Name: task\_fun, Description: task perception: fun, Type: binary
Column Name: task\_interesting, Description: task perception: interesting, Type: binary
Column Name: task\_boring, Description: task perception: boring, Type: binary
Column Name: task\_repetitive, Description: task perception: repetitive, Type: binary
Column Name: task\_important, Description: task perception: important, Type: binary
Column Name: task\_depressing, Description: task perception: depressing, Type: binary
Column Name: task\_offensive, Description: task perception: offensive, Type: binary
Column Name: repeat\_tweet\_coding, Description: likelihood for another tweet task
1: Not at all likely
2: Somewhat likely
3: Very likely, Type: factor
Column Name: repeat\_hs\_coding, Description: likelihood for another hate speech task
1: Not at all likely
2: Somewhat likely
3: Very likely, Type: factor
Column Name: target\_online\_harassment, Description: targeted by hateful online behavior, Type: binary
Column Name: target\_other\_harassment, Description: targeted by other hateful behavior, Type: binary
Column Name: party\_affiliation, Description: party identification
1: Republican
2: Democrat
3: Independent, Type: factor
Column Name: societal\_relevance\_hs, Description: relevance perception of hate speech
1: Not at all likely
2: Somewhat likely
3: Very likely, Type: factor
Column Name: annotator\_id, Description: annotator ID, Type: integer
Column Name: condition, Description: experimental conditions (A-E), Type: factor
Column Name: tweet\_batch, Description: tweet ID in batch, Type: factor
Column Name: hate\_speech, Description: hate speech annotation, Type: logical
Column Name: offensive\_language, Description: offensive language annotation, Type: logical
Column Name: tweet\_id, Description: tweet ID, Type: integer
Column Name: orig\_label\_hs, Description: number of persons who annotated the tweet as hate speech in the original dataset from Davidson et al. (2017), Type: integer
Column Name: orig\_label\_ol, Description: number of persons who annotated the tweet as offensive language in the original dataset from Davidson et al. (2017), Type: integer
Column Name: orig\_label\_ne, Description: number of persons who annotated the tweet as neither in the original dataset from Davidson et al. (2017), Type: integer
Column Name: tweet\_hashed, Description: tweet with usernames hashed, Type: character
If you find the dataset useful, please cite:
|
[] |
[
"TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-hate-speech-detection #size_categories-10K<n<100K #language-English #region-us \n"
] |
[
58
] |
[
"passage: TAGS\n#task_categories-text-classification #task_ids-sentiment-classification #task_ids-hate-speech-detection #size_categories-10K<n<100K #language-English #region-us \n"
] |
ce1315e77efba539587d859f04cb847522e78b98
|
# Dataset of Anisphia Wynn Palettia
This is the dataset of Anisphia Wynn Palettia, 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 | 616 | [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 | 616 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 616 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 616 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/anisphia_wynn_palettia_tenseioujototensaireijounomahoukakumei
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T13:38:44+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:35:36+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Anisphia Wynn Palettia
=================================
This is the dataset of Anisphia Wynn Palettia, 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"
] |
c8493135f98599e51150774733029024d90deee7
|
# Dataset Card for "sumerian_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Falah/sumerian_prompts
|
[
"region:us"
] |
2023-09-13T13:55:42+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 229369, "num_examples": 1000}], "download_size": 28574, "dataset_size": 229369}}
|
2023-09-13T14:09:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sumerian_prompts"
More Information needed
|
[
"# Dataset Card for \"sumerian_prompts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sumerian_prompts\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sumerian_prompts\"\n\nMore Information needed"
] |
2fc1aa5893edbbf87e217a9ad791c84aefd4b9d1
|
# Dataset Card for "voxelgym3D_5c_3x42x42x42_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Cubpaw/voxelgym3D_5c_3x42x42x42_10
|
[
"region:us"
] |
2023-09-13T14:02:02+00:00
|
{"dataset_info": {"features": [{"name": "image", "sequence": {"sequence": {"sequence": {"sequence": "uint8"}}}}, {"name": "path_mask", "sequence": {"sequence": {"sequence": "float32"}}}, {"name": "path_rgb", "sequence": {"sequence": {"sequence": {"sequence": "uint8"}}}}], "splits": [{"name": "train", "num_bytes": 6331872, "num_examples": 8}, {"name": "validation", "num_bytes": 1582968, "num_examples": 2}], "download_size": 62687, "dataset_size": 7914840}}
|
2023-09-13T14:02:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "voxelgym3D_5c_3x42x42x42_10"
More Information needed
|
[
"# Dataset Card for \"voxelgym3D_5c_3x42x42x42_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"voxelgym3D_5c_3x42x42x42_10\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"voxelgym3D_5c_3x42x42x42_10\"\n\nMore Information needed"
] |
ab054b7d19ff85c18d17d6630f1968b51c78dfd3
|
# Dataset Card for "election"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
davidadamczyk/election
|
[
"region:us"
] |
2023-09-13T14:07:15+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "text_label", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 65745.4, "num_examples": 350}, {"name": "test", "num_bytes": 28176.6, "num_examples": 150}], "download_size": 50277, "dataset_size": 93922.0}}
|
2023-09-13T14:07:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "election"
More Information needed
|
[
"# Dataset Card for \"election\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"election\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"election\"\n\nMore Information needed"
] |
652fd537130f39280ba51b43fda12cf1c09ee8ac
|
# Dataset of Euphyllia Magenta
This is the dataset of Euphyllia Magenta, 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 | 635 | [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 | 635 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 635 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 635 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/euphyllia_magenta_tenseioujototensaireijounomahoukakumei
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T14:29:17+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:35:38+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Euphyllia Magenta
============================
This is the dataset of Euphyllia Magenta, 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"
] |
448a6bc6ec63aec7c69c87ee6aeb78fd89ce1efc
|
# Dataset of abe_nana/安部菜々 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of abe_nana/安部菜々 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `ponytail, brown_hair, brown_eyes, bow, breasts, orange_hair, ribbon, bangs, short_hair, hair_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 585.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 345.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1161 | 730.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 517.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1161 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/abe_nana_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/abe_nana_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, solo, blush, smile, looking_at_viewer, open_mouth, maid_apron, hair_ribbon, long_sleeves, thighhighs |
| 1 | 5 |  |  |  |  |  | 1girl, blush, maid_apron, red_bowtie, solo, white_apron, frilled_apron, hair_ribbon, juliet_sleeves, looking_at_viewer, smile, black_dress, closed_mouth, enmaided, holding, red_eyes, simple_background, sitting, white_background |
| 2 | 20 |  |  |  |  |  | 1girl, open_mouth, rabbit_ears, smile, solo, blush, one_eye_closed, ;d, dress, microphone, thighhighs, large_breasts, looking_at_viewer, v_over_eye |
| 3 | 5 |  |  |  |  |  | 1girl, :d, blush, frills, looking_at_viewer, open_mouth, rabbit_ears, solo, wrist_cuffs, puffy_short_sleeves, medium_breasts, pink_bow, red_eyes, heart, pink_dress, white_apron |
| 4 | 8 |  |  |  |  |  | 1girl, heart, looking_at_viewer, open_mouth, solo, white_gloves, blush, dress, one_eye_closed, rabbit_ears, smile, ;d, choker, pink_bow, magical_girl, frills, holding, puffy_short_sleeves, wand, collarbone, fake_animal_ears, happy_birthday, jewelry, skirt, sparkle |
| 5 | 8 |  |  |  |  |  | 1girl, blue_one-piece_swimsuit, looking_at_viewer, rabbit_ears, school_swimsuit, solo, blush, cleavage, smile, white_thighhighs, collarbone, fake_animal_ears, large_breasts, name_tag, open_mouth, polka_dot, red_eyes, sitting, bracelet, poolside, water |
| 6 | 6 |  |  |  |  |  | 1girl, black_gloves, blush, looking_at_viewer, rabbit_ears, solo, cleavage, navel, skirt, smile, open_mouth, striped_thighhighs, fake_animal_ears, large_breasts, medium_breasts, midriff |
| 7 | 7 |  |  |  |  |  | blue_sky, blush, cloud, day, looking_at_viewer, ocean, outdoors, pink_bikini, 1girl, beach, open_mouth, solo, collarbone, cleavage, red_eyes, bracelet, frilled_bikini, large_breasts, medium_breasts, navel, smile |
| 8 | 11 |  |  |  |  |  | 1girl, detached_collar, playboy_bunny, solo, cleavage, looking_at_viewer, smile, large_breasts, rabbit_ears, black_leotard, blush, wrist_cuffs, open_mouth, simple_background, white_background, bare_shoulders, strapless_leotard, thighhighs, fake_animal_ears, medium_breasts, pink_bow, red_bowtie |
| 9 | 5 |  |  |  |  |  | bare_shoulders, blush, strapless_dress, white_gloves, 1girl, looking_at_viewer, pearl_necklace, pink_dress, solo, tiara, collarbone, frilled_dress, long_hair, medium_breasts, open_mouth, petals, white_dress, :d, earrings, moon, own_hands_together, simple_background, white_background |
| 10 | 5 |  |  |  |  |  | 1girl, blush, collarbone, hair_ribbon, large_breasts, looking_at_viewer, side-tie_bikini_bottom, solo, cleavage, elbow_gloves, open_mouth, simple_background, white_background, white_bikini, bare_shoulders, micro_bikini, shiny_skin, sidelocks, thighs, white_choker, white_gloves, white_thighhighs, kneeling, red_eyes, smile, wariza |
| 11 | 8 |  |  |  |  |  | blush, white_shirt, 1girl, serafuku, smile, solo, looking_at_viewer, white_background, blue_skirt, long_sleeves, pleated_skirt, sidelocks, simple_background, blue_sailor_collar, closed_mouth, neckerchief, red_eyes, white_socks, bag, brown_footwear, collarbone, hair_ribbon, open_mouth, shoes, short_sleeves, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | smile | looking_at_viewer | open_mouth | maid_apron | hair_ribbon | long_sleeves | thighhighs | red_bowtie | white_apron | frilled_apron | juliet_sleeves | black_dress | closed_mouth | enmaided | holding | red_eyes | simple_background | sitting | white_background | rabbit_ears | one_eye_closed | ;d | dress | microphone | large_breasts | v_over_eye | :d | frills | wrist_cuffs | puffy_short_sleeves | medium_breasts | pink_bow | heart | pink_dress | white_gloves | choker | magical_girl | wand | collarbone | fake_animal_ears | happy_birthday | jewelry | skirt | sparkle | blue_one-piece_swimsuit | school_swimsuit | cleavage | white_thighhighs | name_tag | polka_dot | bracelet | poolside | water | black_gloves | navel | striped_thighhighs | midriff | blue_sky | cloud | day | ocean | outdoors | pink_bikini | beach | frilled_bikini | detached_collar | playboy_bunny | black_leotard | bare_shoulders | strapless_leotard | strapless_dress | pearl_necklace | tiara | frilled_dress | long_hair | petals | white_dress | earrings | moon | own_hands_together | side-tie_bikini_bottom | elbow_gloves | white_bikini | micro_bikini | shiny_skin | sidelocks | thighs | white_choker | kneeling | wariza | white_shirt | serafuku | blue_skirt | pleated_skirt | blue_sailor_collar | neckerchief | white_socks | bag | brown_footwear | shoes | short_sleeves |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:--------|:--------|:--------------------|:-------------|:-------------|:--------------|:---------------|:-------------|:-------------|:--------------|:----------------|:-----------------|:--------------|:---------------|:-----------|:----------|:-----------|:--------------------|:----------|:-------------------|:--------------|:-----------------|:-----|:--------|:-------------|:----------------|:-------------|:-----|:---------|:--------------|:----------------------|:-----------------|:-----------|:--------|:-------------|:---------------|:---------|:---------------|:-------|:-------------|:-------------------|:-----------------|:----------|:--------|:----------|:--------------------------|:------------------|:-----------|:-------------------|:-----------|:------------|:-----------|:-----------|:--------|:---------------|:--------|:---------------------|:----------|:-----------|:--------|:------|:--------|:-----------|:--------------|:--------|:-----------------|:------------------|:----------------|:----------------|:-----------------|:--------------------|:------------------|:-----------------|:--------|:----------------|:------------|:---------|:--------------|:-----------|:-------|:---------------------|:-------------------------|:---------------|:---------------|:---------------|:-------------|:------------|:---------|:---------------|:-----------|:---------|:--------------|:-----------|:-------------|:----------------|:---------------------|:--------------|:--------------|:------|:-----------------|:--------|:----------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 20 |  |  |  |  |  | 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 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | X | | | | | | | | | X | | | | | | X | | | | | | | | X | | | | | | | | X | | | | X | | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | X | X | X | X | X | X | | | | X | X | | | | | | | | | X | | X | X | | | | | X | | | | X | | X | X | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | X | | X | X | | | | | | | | | | | | | | X | | X | | | | | | | | X | | | | X | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 10 | 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 | | | | | | | | | | | |
| 11 | 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 |
|
CyberHarem/abe_nana_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T14:40:54+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T11:27:15+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of abe\_nana/安部菜々 (THE iDOLM@STER: Cinderella Girls)
============================================================
This is the dataset of abe\_nana/安部菜々 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'ponytail, brown\_hair, brown\_eyes, bow, breasts, orange\_hair, ribbon, bangs, short\_hair, hair\_bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
c2640f58112e5dfe3cd0e9e8b40acc7ecb03636d
|
# Dataset Card for Evaluation run of PygmalionAI/mythalion-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PygmalionAI/mythalion-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 [PygmalionAI/mythalion-13b](https://huggingface.co/PygmalionAI/mythalion-13b) 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_PygmalionAI__mythalion-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T08:48:40.818758](https://huggingface.co/datasets/open-llm-leaderboard/details_PygmalionAI__mythalion-13b/blob/main/results_2023-10-26T08-48-40.818758.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": 0.005243288590604027,
"em_stderr": 0.0007396052260778182,
"f1": 0.07011430369127479,
"f1_stderr": 0.0015312669887699872,
"acc": 0.453473099433751,
"acc_stderr": 0.010546777696172384
},
"harness|drop|3": {
"em": 0.005243288590604027,
"em_stderr": 0.0007396052260778182,
"f1": 0.07011430369127479,
"f1_stderr": 0.0015312669887699872
},
"harness|gsm8k|5": {
"acc": 0.1326762699014405,
"acc_stderr": 0.009343929131442217
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902552
}
}
```
### 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_PygmalionAI__mythalion-13b
|
[
"region:us"
] |
2023-09-13T14:44:13+00:00
|
{"pretty_name": "Evaluation run of PygmalionAI/mythalion-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [PygmalionAI/mythalion-13b](https://huggingface.co/PygmalionAI/mythalion-13b) 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_PygmalionAI__mythalion-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-26T08:48:40.818758](https://huggingface.co/datasets/open-llm-leaderboard/details_PygmalionAI__mythalion-13b/blob/main/results_2023-10-26T08-48-40.818758.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\": 0.005243288590604027,\n \"em_stderr\": 0.0007396052260778182,\n \"f1\": 0.07011430369127479,\n \"f1_stderr\": 0.0015312669887699872,\n \"acc\": 0.453473099433751,\n \"acc_stderr\": 0.010546777696172384\n },\n \"harness|drop|3\": {\n \"em\": 0.005243288590604027,\n \"em_stderr\": 0.0007396052260778182,\n \"f1\": 0.07011430369127479,\n \"f1_stderr\": 0.0015312669887699872\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1326762699014405,\n \"acc_stderr\": 0.009343929131442217\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902552\n }\n}\n```", "repo_url": "https://huggingface.co/PygmalionAI/mythalion-13b", "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_13T15_43_56.959580", "path": ["**/details_harness|arc:challenge|25_2023-09-13T15-43-56.959580.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T15-43-56.959580.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_26T08_48_40.818758", "path": ["**/details_harness|drop|3_2023-10-26T08-48-40.818758.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-26T08-48-40.818758.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_26T08_48_40.818758", "path": ["**/details_harness|gsm8k|5_2023-10-26T08-48-40.818758.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-26T08-48-40.818758.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T15_43_56.959580", "path": ["**/details_harness|hellaswag|10_2023-09-13T15-43-56.959580.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T15-43-56.959580.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_13T15_43_56.959580", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T15-43-56.959580.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T15-43-56.959580.parquet", 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|
2023-10-26T07:48:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PygmalionAI/mythalion-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 PygmalionAI/mythalion-13b 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 2023-10-26T08:48:40.818758(note that their might be results for other tasks in 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 PygmalionAI/mythalion-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 PygmalionAI/mythalion-13b 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 2023-10-26T08:48:40.818758(note that their might be results for other tasks in 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 PygmalionAI/mythalion-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 PygmalionAI/mythalion-13b 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 2023-10-26T08:48:40.818758(note that their might be results for other tasks in 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 PygmalionAI/mythalion-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 PygmalionAI/mythalion-13b 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 2023-10-26T08:48:40.818758(note that their might be results for other tasks in 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"
] |
97e84ca24e22aa72f953035d7e4c60f232db9c24
|
# Dataset of Illya Coral
This is the dataset of Illya Coral, containing 117 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 | 117 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 262 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 117 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 117 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 117 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 117 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 117 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 262 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 262 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 262 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/illya_coral_tenseioujototensaireijounomahoukakumei
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T14:46:52+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:35:42+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Illya Coral
======================
This is the dataset of Illya Coral, containing 117 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"
] |
4ae2a071257915f6a58d8322dd824f21426868e3
|
# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-30b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PocketDoc/Dans-PersonalityEngine-30b
- **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-30b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-30b) 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_PocketDoc__Dans-PersonalityEngine-30b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T18:34:37.818403](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-30b/blob/main/results_2023-10-28T18-34-37.818403.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": 0.3976510067114094,
"em_stderr": 0.005012043006539525,
"f1": 0.44610213926174597,
"f1_stderr": 0.004862167359127648,
"acc": 0.4826038357489021,
"acc_stderr": 0.010505012604058294
},
"harness|drop|3": {
"em": 0.3976510067114094,
"em_stderr": 0.005012043006539525,
"f1": 0.44610213926174597,
"f1_stderr": 0.004862167359127648
},
"harness|gsm8k|5": {
"acc": 0.155420773313116,
"acc_stderr": 0.009979689409499148
},
"harness|winogrande|5": {
"acc": 0.8097868981846882,
"acc_stderr": 0.01103033579861744
}
}
```
### 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-30b
|
[
"region:us"
] |
2023-09-13T14:48:02+00:00
|
{"pretty_name": "Evaluation run of PocketDoc/Dans-PersonalityEngine-30b", "dataset_summary": "Dataset automatically created during the evaluation run of model [PocketDoc/Dans-PersonalityEngine-30b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-30b) 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_PocketDoc__Dans-PersonalityEngine-30b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T18:34:37.818403](https://huggingface.co/datasets/open-llm-leaderboard/details_PocketDoc__Dans-PersonalityEngine-30b/blob/main/results_2023-10-28T18-34-37.818403.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\": 0.3976510067114094,\n \"em_stderr\": 0.005012043006539525,\n \"f1\": 0.44610213926174597,\n \"f1_stderr\": 0.004862167359127648,\n \"acc\": 0.4826038357489021,\n \"acc_stderr\": 0.010505012604058294\n },\n \"harness|drop|3\": {\n \"em\": 0.3976510067114094,\n \"em_stderr\": 0.005012043006539525,\n \"f1\": 0.44610213926174597,\n \"f1_stderr\": 0.004862167359127648\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.155420773313116,\n \"acc_stderr\": 0.009979689409499148\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8097868981846882,\n \"acc_stderr\": 0.01103033579861744\n }\n}\n```", "repo_url": "https://huggingface.co/PocketDoc/Dans-PersonalityEngine-30b", "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_13T15_47_49.138140", "path": ["**/details_harness|arc:challenge|25_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T18_34_37.818403", "path": ["**/details_harness|drop|3_2023-10-28T18-34-37.818403.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T18-34-37.818403.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T18_34_37.818403", "path": ["**/details_harness|gsm8k|5_2023-10-28T18-34-37.818403.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T18-34-37.818403.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|hellaswag|10_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T15-47-49.138140.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T15-47-49.138140.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_28T18_34_37.818403", "path": ["**/details_harness|winogrande|5_2023-10-28T18-34-37.818403.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-28T18-34-37.818403.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T15_47_49.138140", "path": ["results_2023-09-13T15-47-49.138140.parquet"]}, {"split": "2023_10_28T18_34_37.818403", "path": ["results_2023-10-28T18-34-37.818403.parquet"]}, {"split": "latest", "path": ["results_2023-10-28T18-34-37.818403.parquet"]}]}]}
|
2023-10-28T17:34:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-30b
## 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-30b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T18:34:37.818403(note that their might be results for other tasks in 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-30b",
"## 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-30b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T18:34:37.818403(note that their might be results for other tasks in 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-30b",
"## 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-30b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T18:34:37.818403(note that their might be results for other tasks in 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",
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"### Curation Rationale",
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"### 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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170,
67,
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6,
5,
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10,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of PocketDoc/Dans-PersonalityEngine-30b## 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-30b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T18:34:37.818403(note that their might be results for other tasks in 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"
] |
5f46bb88b3e4f850476fec7882ce1e292b73acd5
|
# Dataset Card for "sts_traces"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
under-tree/sts_traces
|
[
"region:us"
] |
2023-09-13T14:50:55+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "text1", "dtype": "string"}, {"name": "text2", "dtype": "string"}, {"name": "score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 28555408, "num_examples": 15000}, {"name": "val", "num_bytes": 5686916, "num_examples": 3000}], "download_size": 11941770, "dataset_size": 34242324}}
|
2023-09-13T14:51:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sts_traces"
More Information needed
|
[
"# Dataset Card for \"sts_traces\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sts_traces\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sts_traces\"\n\nMore Information needed"
] |
6a5ce969e05fbd4bbe4accb07c74614d9f63bdd2
|
# Dataset of Tilty Claret
This is the dataset of Tilty Claret, containing 147 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 | 147 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 288 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 147 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 147 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 147 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 147 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 147 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 288 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 288 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 288 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/tilty_claret_tenseioujototensaireijounomahoukakumei
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T15:01:50+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:35:44+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Tilty Claret
=======================
This is the dataset of Tilty Claret, containing 147 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"
] |
51068fb147b85d917a5d1e678c8e97a510dcf0ff
|
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
afern24/gtzan_all_preprocessed
|
[
"region:us"
] |
2023-09-13T15:08:31+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "blues", "1": "classical", "2": "country", "3": "disco", "4": "hiphop", "5": "jazz", "6": "metal", "7": "pop", "8": "reggae", "9": "rock"}}}}, {"name": "input_values", "sequence": "float32"}, {"name": "attention_mask", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3452159816, "num_examples": 899}, {"name": "test", "num_bytes": 384000696, "num_examples": 100}], "download_size": 1923103923, "dataset_size": 3836160512}}
|
2023-09-13T15:10:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gtzan_all_preprocessed"
More Information needed
|
[
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
b10e4589912e0b18d11efde55f1f73ff68d1538b
|
# Dataset of Lainie Cyan
This is the dataset of Lainie Cyan, containing 102 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 | 102 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 194 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 102 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 102 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 102 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 102 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 102 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 194 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 194 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 194 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/lainie_cyan_tenseioujototensaireijounomahoukakumei
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T15:12:02+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:35:46+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Lainie Cyan
======================
This is the dataset of Lainie Cyan, containing 102 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"
] |
5e929f2849f6381eb073c8c20e44a3acc21df95b
|
# Dataset Card for Evaluation run of royallab/Pygmalion-2-13b-SuperCOT
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/royallab/Pygmalion-2-13b-SuperCOT
- **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 [royallab/Pygmalion-2-13b-SuperCOT](https://huggingface.co/royallab/Pygmalion-2-13b-SuperCOT) 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_royallab__Pygmalion-2-13b-SuperCOT",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T11:22:00.550920](https://huggingface.co/datasets/open-llm-leaderboard/details_royallab__Pygmalion-2-13b-SuperCOT/blob/main/results_2023-10-24T11-22-00.550920.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": 0.26321308724832215,
"em_stderr": 0.00450987331416996,
"f1": 0.3413359899328871,
"f1_stderr": 0.004450739419603285,
"acc": 0.4189928271938333,
"acc_stderr": 0.009211903072829743
},
"harness|drop|3": {
"em": 0.26321308724832215,
"em_stderr": 0.00450987331416996,
"f1": 0.3413359899328871,
"f1_stderr": 0.004450739419603285
},
"harness|gsm8k|5": {
"acc": 0.06292645943896892,
"acc_stderr": 0.006688762581532745
},
"harness|winogrande|5": {
"acc": 0.7750591949486977,
"acc_stderr": 0.011735043564126742
}
}
```
### 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
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### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
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### Licensing Information
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### Citation Information
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### Contributions
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|
open-llm-leaderboard/details_royallab__Pygmalion-2-13b-SuperCOT
|
[
"region:us"
] |
2023-09-13T15:15:08+00:00
|
{"pretty_name": "Evaluation run of royallab/Pygmalion-2-13b-SuperCOT", "dataset_summary": "Dataset automatically created during the evaluation run of model [royallab/Pygmalion-2-13b-SuperCOT](https://huggingface.co/royallab/Pygmalion-2-13b-SuperCOT) 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_royallab__Pygmalion-2-13b-SuperCOT\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T11:22:00.550920](https://huggingface.co/datasets/open-llm-leaderboard/details_royallab__Pygmalion-2-13b-SuperCOT/blob/main/results_2023-10-24T11-22-00.550920.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\": 0.26321308724832215,\n \"em_stderr\": 0.00450987331416996,\n \"f1\": 0.3413359899328871,\n \"f1_stderr\": 0.004450739419603285,\n \"acc\": 0.4189928271938333,\n \"acc_stderr\": 0.009211903072829743\n },\n \"harness|drop|3\": {\n \"em\": 0.26321308724832215,\n \"em_stderr\": 0.00450987331416996,\n \"f1\": 0.3413359899328871,\n \"f1_stderr\": 0.004450739419603285\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06292645943896892,\n \"acc_stderr\": 0.006688762581532745\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7750591949486977,\n \"acc_stderr\": 0.011735043564126742\n }\n}\n```", "repo_url": "https://huggingface.co/royallab/Pygmalion-2-13b-SuperCOT", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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2023-10-24T10:22:13+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of royallab/Pygmalion-2-13b-SuperCOT
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model royallab/Pygmalion-2-13b-SuperCOT 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 2023-10-24T11:22:00.550920(note that their might be results for other tasks in 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 royallab/Pygmalion-2-13b-SuperCOT",
"## 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 royallab/Pygmalion-2-13b-SuperCOT 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 2023-10-24T11:22:00.550920(note that their might be results for other tasks in 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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"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of royallab/Pygmalion-2-13b-SuperCOT",
"## 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 royallab/Pygmalion-2-13b-SuperCOT 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 2023-10-24T11:22:00.550920(note that their might be results for other tasks in 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 royallab/Pygmalion-2-13b-SuperCOT## 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 royallab/Pygmalion-2-13b-SuperCOT 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 2023-10-24T11:22:00.550920(note that their might be results for other tasks in 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"
] |
82507676d45f91a3db8c92c67cc19f774cfa29ce
|
[zeroshot/twitter-financial-news-sentiment](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) prepared for LLM fine-tuning
by adding an `instruction` column and mapping the label from numeric to string (`{0:"negative", 1:'positive', 2:'neutral'}`).
[Source](https://github.com/AI4Finance-Foundation/FinGPT/blob/master/fingpt/FinGPT-v3/data/making_data.ipynb)
```python
from datasets import load_dataset
import datasets
from huggingface_hub import notebook_login
notebook_login()
ds = load_dataset('zeroshot/twitter-financial-news-sentiment')
num_to_label = {
0: 'negative',
1: 'positive',
2: 'neutral',
}
instruction = 'What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.'
# Training split
ds_train = ds['train']
ds_train = ds_train.to_pandas()
ds_train['label'] = ds_train['label'].apply(num_to_label.get)
ds_train['instruction'] = instruction
ds_train.columns = ['input', 'output', 'instruction']
ds_train = datasets.Dataset.from_pandas(ds_train)
ds_train.push_to_hub("twitter-financial-news-sentiment")
# Validation split
ds_valid = ds['validation']
ds_valid = ds_valid.to_pandas()
ds_valid['label'] = ds_valid['label'].apply(num_to_label.get)
ds_valid['instruction'] = instruction
ds_valid.columns = ['input', 'output', 'instruction']
ds_valid = datasets.Dataset.from_pandas(ds_valid, split='validation')
ds_valid.push_to_hub("twitter-financial-news-sentiment", split='validation')
```
|
jppgks/twitter-financial-news-sentiment
|
[
"license:mit",
"region:us"
] |
2023-09-13T15:16:32+00:00
|
{"license": "mit", "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1906560, "num_examples": 9543}, {"name": "validation", "num_bytes": 479540, "num_examples": 2388}], "download_size": 728648, "dataset_size": 2386100}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
|
2023-09-13T21:05:58+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
zeroshot/twitter-financial-news-sentiment prepared for LLM fine-tuning
by adding an 'instruction' column and mapping the label from numeric to string ('{0:"negative", 1:'positive', 2:'neutral'}').
Source
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
05c5735135ff845ebbadebfd57529f6b7848b695
|
# Dataset Card for Evaluation run of tiiuae/falcon-rw-1b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-rw-1b
- **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 [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) 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_tiiuae__falcon-rw-1b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T18:16:05.784566](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-rw-1b/blob/main/results_2023-10-25T18-16-05.784566.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652193675,
"f1": 0.0464429530201344,
"f1_stderr": 0.001186214815178995,
"acc": 0.31283505657403515,
"acc_stderr": 0.007820275562329611
},
"harness|drop|3": {
"em": 0.0010486577181208054,
"em_stderr": 0.00033145814652193675,
"f1": 0.0464429530201344,
"f1_stderr": 0.001186214815178995
},
"harness|gsm8k|5": {
"acc": 0.00530705079605762,
"acc_stderr": 0.0020013057209480574
},
"harness|winogrande|5": {
"acc": 0.6203630623520127,
"acc_stderr": 0.013639245403711165
}
}
```
### 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_tiiuae__falcon-rw-1b
|
[
"region:us"
] |
2023-09-13T15:16:56+00:00
|
{"pretty_name": "Evaluation run of tiiuae/falcon-rw-1b", "dataset_summary": "Dataset automatically created during the evaluation run of model [tiiuae/falcon-rw-1b](https://huggingface.co/tiiuae/falcon-rw-1b) 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_tiiuae__falcon-rw-1b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-25T18:16:05.784566](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-rw-1b/blob/main/results_2023-10-25T18-16-05.784566.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652193675,\n \"f1\": 0.0464429530201344,\n \"f1_stderr\": 0.001186214815178995,\n \"acc\": 0.31283505657403515,\n \"acc_stderr\": 0.007820275562329611\n },\n \"harness|drop|3\": {\n \"em\": 0.0010486577181208054,\n \"em_stderr\": 0.00033145814652193675,\n \"f1\": 0.0464429530201344,\n \"f1_stderr\": 0.001186214815178995\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.00530705079605762,\n \"acc_stderr\": 0.0020013057209480574\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6203630623520127,\n \"acc_stderr\": 0.013639245403711165\n }\n}\n```", "repo_url": "https://huggingface.co/tiiuae/falcon-rw-1b", "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_13T16_16_44.792936", "path": ["**/details_harness|arc:challenge|25_2023-09-13T16-16-44.792936.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T16-16-44.792936.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_25T18_16_05.784566", "path": ["**/details_harness|drop|3_2023-10-25T18-16-05.784566.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-25T18-16-05.784566.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_25T18_16_05.784566", "path": ["**/details_harness|gsm8k|5_2023-10-25T18-16-05.784566.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-25T18-16-05.784566.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T16_16_44.792936", "path": ["**/details_harness|hellaswag|10_2023-09-13T16-16-44.792936.parquet"]}, {"split": "latest", "path": 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["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T16-16-44.792936.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T16-16-44.792936.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_13T16_16_44.792936", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T16-16-44.792936.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T16-16-44.792936.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_13T16_16_44.792936", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T16-16-44.792936.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T16-16-44.792936.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_13T16_16_44.792936", "path": 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["**/details_harness|truthfulqa:mc|0_2023-09-13T16-16-44.792936.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T16-16-44.792936.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_25T18_16_05.784566", "path": ["**/details_harness|winogrande|5_2023-10-25T18-16-05.784566.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-25T18-16-05.784566.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T16_16_44.792936", "path": ["results_2023-09-13T16-16-44.792936.parquet"]}, {"split": "2023_10_25T18_16_05.784566", "path": ["results_2023-10-25T18-16-05.784566.parquet"]}, {"split": "latest", "path": ["results_2023-10-25T18-16-05.784566.parquet"]}]}]}
|
2023-10-25T17:16:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of tiiuae/falcon-rw-1b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model tiiuae/falcon-rw-1b 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 2023-10-25T18:16:05.784566(note that their might be results for other tasks in 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 tiiuae/falcon-rw-1b",
"## 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 tiiuae/falcon-rw-1b 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 2023-10-25T18:16:05.784566(note that their might be results for other tasks in 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 tiiuae/falcon-rw-1b",
"## 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 tiiuae/falcon-rw-1b 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 2023-10-25T18:16:05.784566(note that their might be results for other tasks in 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 tiiuae/falcon-rw-1b## 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 tiiuae/falcon-rw-1b 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 2023-10-25T18:16:05.784566(note that their might be results for other tasks in 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"
] |
b79b6a3cc77bda861fd22f6c01e22252e3da10ce
|
# Dataset of Yukino Yukinoshita (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Yukino Yukinoshita (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 998 images and their tags.
The core tags of this character are `black_hair, long_hair, ribbon, blue_eyes, hair_ribbon, red_ribbon, 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 | 998 | 494.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 998 | 421.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 2124 | 837.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 998 | 494.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 2124 | 943.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru/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/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru',
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, collared_shirt, neck_ribbon, sobu_high_school_uniform, solo, upper_body, white_shirt, hair_between_eyes, looking_at_viewer, indoors, sunset, window |
| 1 | 8 |  |  |  |  |  | 1girl, black_jacket, blazer, collared_shirt, neck_ribbon, sobu_high_school_uniform, solo, white_shirt, looking_at_viewer, upper_body, hair_between_eyes |
| 2 | 8 |  |  |  |  |  | 1girl, black_jacket, blazer, neck_ribbon, sobu_high_school_uniform, solo, upper_body, white_shirt, ahoge, collared_shirt, closed_mouth, hair_between_eyes |
| 3 | 6 |  |  |  |  |  | 1girl, black_jacket, blazer, looking_at_viewer, sobu_high_school_uniform, solo |
| 4 | 10 |  |  |  |  |  | 1girl, black_jacket, blazer, open_mouth, sobu_high_school_uniform, solo, looking_at_viewer |
| 5 | 11 |  |  |  |  |  | 1girl, black_jacket, blazer, sobu_high_school_uniform, solo |
| 6 | 12 |  |  |  |  |  | 1girl, black_jacket, blazer, closed_eyes, sobu_high_school_uniform, solo |
| 7 | 5 |  |  |  |  |  | 1girl, black_jacket, blazer, profile, sobu_high_school_uniform, solo, sunset |
| 8 | 5 |  |  |  |  |  | 1girl, black_jacket, blazer, bookshelf, sobu_high_school_uniform, solo, glasses |
| 9 | 5 |  |  |  |  |  | 1girl, black_jacket, blazer, shirt, sobu_high_school_uniform, solo, upper_body |
| 10 | 6 |  |  |  |  |  | 1girl, ahoge, black_jacket, blazer, closed_eyes, sobu_high_school_uniform, solo, teacup |
| 11 | 10 |  |  |  |  |  | 1girl, black_jacket, black_thighhighs, blazer, skirt, sobu_high_school_uniform, solo, zettai_ryouiki, ahoge |
| 12 | 35 |  |  |  |  |  | 1girl, sobu_high_school_uniform, plaid_skirt, pleated_skirt, black_jacket, blazer, solo, white_shirt, neck_ribbon, long_sleeves, collared_shirt, black_thighhighs, zettai_ryouiki |
| 13 | 16 |  |  |  |  |  | 1girl, solo, sobu_high_school_uniform, shirt, apron |
| 14 | 8 |  |  |  |  |  | 1girl, sobu_high_school_uniform, solo, looking_at_viewer, shirt, ahoge, sweater_vest, crossed_arms |
| 15 | 11 |  |  |  |  |  | 1girl, hair_between_eyes, portrait, solo, close-up, parody, anime_coloring, open_mouth, closed_mouth, looking_at_viewer |
| 16 | 5 |  |  |  |  |  | reading, sitting, sobu_high_school_uniform, sweater_vest, 1girl, plaid_skirt, solo, holding_book, shirt, thighhighs, window, zettai_ryouiki |
| 17 | 5 |  |  |  |  |  | 1girl, blue_necktie, formal, solo, suit, upper_body, white_shirt, black_jacket, looking_at_viewer, ponytail, smile, hair_between_eyes, sidelocks, ahoge |
| 18 | 5 |  |  |  |  |  | 1girl, ahoge, black_pants, blue_necktie, formal, ponytail, sidelocks, suit, vest, white_gloves, white_shirt, solo, standing, black_jacket, hair_between_eyes, butler, closed_mouth, looking_at_viewer |
| 19 | 8 |  |  |  |  |  | 1girl, necklace, shirt, solo, upper_body, tree |
| 20 | 7 |  |  |  |  |  | 1girl, blue_ribbon, neck_ribbon, outdoors, white_shirt, collared_shirt, disposable_cup, drinking_straw, hair_between_eyes, upper_body, blue_cardigan, holding_cup, low_twintails, ahoge, bubble_tea, day, looking_at_viewer, open_mouth, school_uniform, sky, solo, blurry, blush, cloud, teeth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | collared_shirt | neck_ribbon | sobu_high_school_uniform | solo | upper_body | white_shirt | hair_between_eyes | looking_at_viewer | indoors | sunset | window | black_jacket | blazer | ahoge | closed_mouth | open_mouth | closed_eyes | profile | bookshelf | glasses | shirt | teacup | black_thighhighs | skirt | zettai_ryouiki | plaid_skirt | pleated_skirt | long_sleeves | apron | sweater_vest | crossed_arms | portrait | close-up | parody | anime_coloring | reading | sitting | holding_book | thighhighs | blue_necktie | formal | suit | ponytail | smile | sidelocks | black_pants | vest | white_gloves | standing | butler | necklace | tree | blue_ribbon | outdoors | disposable_cup | drinking_straw | blue_cardigan | holding_cup | low_twintails | bubble_tea | day | school_uniform | sky | blurry | blush | cloud | teeth |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------|:--------------|:---------------------------|:-------|:-------------|:--------------|:--------------------|:--------------------|:----------|:---------|:---------|:---------------|:---------|:--------|:---------------|:-------------|:--------------|:----------|:------------|:----------|:--------|:---------|:-------------------|:--------|:-----------------|:--------------|:----------------|:---------------|:--------|:---------------|:---------------|:-----------|:-----------|:---------|:-----------------|:----------|:----------|:---------------|:-------------|:---------------|:---------|:-------|:-----------|:--------|:------------|:--------------|:-------|:---------------|:-----------|:---------|:-----------|:-------|:--------------|:-----------|:-----------------|:-----------------|:----------------|:--------------|:----------------|:-------------|:------|:-----------------|:------|:---------|:--------|:--------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | X | X | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | | | X | X | | | | X | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | | | X | X | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 12 |  |  |  |  |  | X | | | X | X | | | | | | | | X | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | X | X | | | | | | X | | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | X | X | | | | | | | | X | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | X | X | X | | | | | | | X | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 6 |  |  |  |  |  | X | | | X | X | | | | | | | | X | X | X | | | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 10 |  |  |  |  |  | X | | | X | X | | | | | | | | X | X | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 12 | 35 |  |  |  |  |  | X | X | X | X | X | | X | | | | | | X | X | | | | | | | | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 13 | 16 |  |  |  |  |  | X | | | X | X | | | | | | | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 14 | 8 |  |  |  |  |  | X | | | X | X | | | | X | | | | | | X | | | | | | | X | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 15 | 11 |  |  |  |  |  | X | | | | X | | | X | X | | | | | | | X | X | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 16 | 5 |  |  |  |  |  | X | | | X | X | | | | | | | X | | | | | | | | | | X | | | | X | X | | | | X | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 17 | 5 |  |  |  |  |  | X | | | | X | X | X | X | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 18 | 5 |  |  |  |  |  | X | | | | X | | X | X | X | | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 19 | 8 |  |  |  |  |  | X | | | | X | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | |
| 20 | 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 |
|
CyberHarem/yukino_yukinoshita_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T15:28:08+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T10:42:22+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Yukino Yukinoshita (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
================================================================================
This is the dataset of Yukino Yukinoshita (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 998 images and their tags.
The core tags of this character are 'black\_hair, long\_hair, ribbon, blue\_eyes, hair\_ribbon, red\_ribbon, 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"
] |
8d96b16b046f8b40482681905c6e539c93201ea1
|
# Dataset Card for Evaluation run of sauce1337/AppleSauce-L2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/sauce1337/AppleSauce-L2-13b
- **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 [sauce1337/AppleSauce-L2-13b](https://huggingface.co/sauce1337/AppleSauce-L2-13b) 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_sauce1337__AppleSauce-L2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T06:05:52.891259](https://huggingface.co/datasets/open-llm-leaderboard/details_sauce1337__AppleSauce-L2-13b/blob/main/results_2023-10-23T06-05-52.891259.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": 0.3851719798657718,
"em_stderr": 0.004983607378769111,
"f1": 0.4518634647651026,
"f1_stderr": 0.004795271144996667,
"acc": 0.4296748451536735,
"acc_stderr": 0.010140916870454906
},
"harness|drop|3": {
"em": 0.3851719798657718,
"em_stderr": 0.004983607378769111,
"f1": 0.4518634647651026,
"f1_stderr": 0.004795271144996667
},
"harness|gsm8k|5": {
"acc": 0.10007581501137225,
"acc_stderr": 0.008266274528685634
},
"harness|winogrande|5": {
"acc": 0.7592738752959748,
"acc_stderr": 0.01201555921222418
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_sauce1337__AppleSauce-L2-13b
|
[
"region:us"
] |
2023-09-13T15:33:07+00:00
|
{"pretty_name": "Evaluation run of sauce1337/AppleSauce-L2-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [sauce1337/AppleSauce-L2-13b](https://huggingface.co/sauce1337/AppleSauce-L2-13b) 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_sauce1337__AppleSauce-L2-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T06:05:52.891259](https://huggingface.co/datasets/open-llm-leaderboard/details_sauce1337__AppleSauce-L2-13b/blob/main/results_2023-10-23T06-05-52.891259.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\": 0.3851719798657718,\n \"em_stderr\": 0.004983607378769111,\n \"f1\": 0.4518634647651026,\n \"f1_stderr\": 0.004795271144996667,\n \"acc\": 0.4296748451536735,\n \"acc_stderr\": 0.010140916870454906\n },\n \"harness|drop|3\": {\n \"em\": 0.3851719798657718,\n \"em_stderr\": 0.004983607378769111,\n \"f1\": 0.4518634647651026,\n \"f1_stderr\": 0.004795271144996667\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10007581501137225,\n \"acc_stderr\": 0.008266274528685634\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.01201555921222418\n }\n}\n```", "repo_url": "https://huggingface.co/sauce1337/AppleSauce-L2-13b", "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_13T16_32_51.732119", "path": ["**/details_harness|arc:challenge|25_2023-09-13T16-32-51.732119.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T16-32-51.732119.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_23T06_05_52.891259", "path": ["**/details_harness|drop|3_2023-10-23T06-05-52.891259.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-23T06-05-52.891259.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_23T06_05_52.891259", "path": ["**/details_harness|gsm8k|5_2023-10-23T06-05-52.891259.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-23T06-05-52.891259.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T16_32_51.732119", "path": ["**/details_harness|hellaswag|10_2023-09-13T16-32-51.732119.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T16-32-51.732119.parquet"]}]}, 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|
2023-10-23T05:06:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of sauce1337/AppleSauce-L2-13b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model sauce1337/AppleSauce-L2-13b 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 2023-10-23T06:05:52.891259(note that their might be results for other tasks in 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 sauce1337/AppleSauce-L2-13b",
"## 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 sauce1337/AppleSauce-L2-13b 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 2023-10-23T06:05:52.891259(note that their might be results for other tasks in 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 sauce1337/AppleSauce-L2-13b",
"## 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 sauce1337/AppleSauce-L2-13b 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 2023-10-23T06:05:52.891259(note that their might be results for other tasks in 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 sauce1337/AppleSauce-L2-13b## 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 sauce1337/AppleSauce-L2-13b 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 2023-10-23T06:05:52.891259(note that their might be results for other tasks in 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"
] |
9d75a86f7a47bd8c1755f4ee167a5153776d93f0
|
# Dataset Card for Evaluation run of nicholasKluge/Aira-2-355M
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nicholasKluge/Aira-2-355M
- **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 [nicholasKluge/Aira-2-355M](https://huggingface.co/nicholasKluge/Aira-2-355M) 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_nicholasKluge__Aira-2-355M",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T20:42:43.436493](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-2-355M/blob/main/results_2023-10-23T20-42-43.436493.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": 0.002726510067114094,
"em_stderr": 0.0005340111700415926,
"f1": 0.02992659395973156,
"f1_stderr": 0.0011229110520382824,
"acc": 0.26874506708760854,
"acc_stderr": 0.007006464091668287
},
"harness|drop|3": {
"em": 0.002726510067114094,
"em_stderr": 0.0005340111700415926,
"f1": 0.02992659395973156,
"f1_stderr": 0.0011229110520382824
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5374901341752171,
"acc_stderr": 0.014012928183336574
}
}
```
### 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_nicholasKluge__Aira-2-355M
|
[
"region:us"
] |
2023-09-13T15:42:57+00:00
|
{"pretty_name": "Evaluation run of nicholasKluge/Aira-2-355M", "dataset_summary": "Dataset automatically created during the evaluation run of model [nicholasKluge/Aira-2-355M](https://huggingface.co/nicholasKluge/Aira-2-355M) 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_nicholasKluge__Aira-2-355M\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T20:42:43.436493](https://huggingface.co/datasets/open-llm-leaderboard/details_nicholasKluge__Aira-2-355M/blob/main/results_2023-10-23T20-42-43.436493.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\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415926,\n \"f1\": 0.02992659395973156,\n \"f1_stderr\": 0.0011229110520382824,\n \"acc\": 0.26874506708760854,\n \"acc_stderr\": 0.007006464091668287\n },\n \"harness|drop|3\": {\n \"em\": 0.002726510067114094,\n \"em_stderr\": 0.0005340111700415926,\n \"f1\": 0.02992659395973156,\n \"f1_stderr\": 0.0011229110520382824\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5374901341752171,\n \"acc_stderr\": 0.014012928183336574\n }\n}\n```", "repo_url": "https://huggingface.co/nicholasKluge/Aira-2-355M", "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_13T16_42_47.066460", "path": 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["**/details_harness|truthfulqa:mc|0_2023-09-13T16-42-47.066460.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T16-42-47.066460.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_23T20_42_43.436493", "path": ["**/details_harness|winogrande|5_2023-10-23T20-42-43.436493.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-23T20-42-43.436493.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T16_42_47.066460", "path": ["results_2023-09-13T16-42-47.066460.parquet"]}, {"split": "2023_10_23T20_42_43.436493", "path": ["results_2023-10-23T20-42-43.436493.parquet"]}, {"split": "latest", "path": ["results_2023-10-23T20-42-43.436493.parquet"]}]}]}
|
2023-10-23T19:42:54+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of nicholasKluge/Aira-2-355M
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model nicholasKluge/Aira-2-355M 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 2023-10-23T20:42:43.436493(note that their might be results for other tasks in 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 nicholasKluge/Aira-2-355M",
"## 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 nicholasKluge/Aira-2-355M 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 2023-10-23T20:42:43.436493(note that their might be results for other tasks in 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 nicholasKluge/Aira-2-355M",
"## 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 nicholasKluge/Aira-2-355M 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 2023-10-23T20:42:43.436493(note that their might be results for other tasks in 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 nicholasKluge/Aira-2-355M## 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 nicholasKluge/Aira-2-355M 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 2023-10-23T20:42:43.436493(note that their might be results for other tasks in 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"
] |
94fc75ade0eadcfaeb4fdb44e2b4648d0dd3645b
|
# Dataset Card for Evaluation run of TurkuNLP/gpt3-finnish-small
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TurkuNLP/gpt3-finnish-small
- **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 [TurkuNLP/gpt3-finnish-small](https://huggingface.co/TurkuNLP/gpt3-finnish-small) 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_TurkuNLP__gpt3-finnish-small",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T18:15:50.811191](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-small/blob/main/results_2023-10-28T18-15-50.811191.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": 0.0004191330178826759,
"f1": 0.020169882550335586,
"f1_stderr": 0.0009424790183928599,
"acc": 0.24112075769534333,
"acc_stderr": 0.007021809798087482
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.0004191330178826759,
"f1": 0.020169882550335586,
"f1_stderr": 0.0009424790183928599
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.48224151539068666,
"acc_stderr": 0.014043619596174964
}
}
```
### 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_TurkuNLP__gpt3-finnish-small
|
[
"region:us"
] |
2023-09-13T15:48:02+00:00
|
{"pretty_name": "Evaluation run of TurkuNLP/gpt3-finnish-small", "dataset_summary": "Dataset automatically created during the evaluation run of model [TurkuNLP/gpt3-finnish-small](https://huggingface.co/TurkuNLP/gpt3-finnish-small) 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_TurkuNLP__gpt3-finnish-small\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T18:15:50.811191](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-small/blob/main/results_2023-10-28T18-15-50.811191.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\": 0.0004191330178826759,\n \"f1\": 0.020169882550335586,\n \"f1_stderr\": 0.0009424790183928599,\n \"acc\": 0.24112075769534333,\n \"acc_stderr\": 0.007021809798087482\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826759,\n \"f1\": 0.020169882550335586,\n \"f1_stderr\": 0.0009424790183928599\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.48224151539068666,\n \"acc_stderr\": 0.014043619596174964\n }\n}\n```", "repo_url": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", "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_13T16_47_47.482079", "path": ["**/details_harness|arc:challenge|25_2023-09-13T16-47-47.482079.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T16-47-47.482079.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T18_15_50.811191", "path": ["**/details_harness|drop|3_2023-10-28T18-15-50.811191.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T18-15-50.811191.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T18_15_50.811191", "path": ["**/details_harness|gsm8k|5_2023-10-28T18-15-50.811191.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T18-15-50.811191.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T16_47_47.482079", "path": ["**/details_harness|hellaswag|10_2023-09-13T16-47-47.482079.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T16-47-47.482079.parquet"]}]}, 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|
2023-10-28T17:16:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TurkuNLP/gpt3-finnish-small
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TurkuNLP/gpt3-finnish-small on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T18:15:50.811191(note that their might be results for other tasks in 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 TurkuNLP/gpt3-finnish-small",
"## 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 TurkuNLP/gpt3-finnish-small on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T18:15:50.811191(note that their might be results for other tasks in 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 TurkuNLP/gpt3-finnish-small",
"## 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 TurkuNLP/gpt3-finnish-small on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T18:15:50.811191(note that their might be results for other tasks in 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 TurkuNLP/gpt3-finnish-small## 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 TurkuNLP/gpt3-finnish-small on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T18:15:50.811191(note that their might be results for other tasks in 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"
] |
cbb6ccc7e7b9d90d0a8863b86e0d3fc97dfab898
|
# Dataset of Yuigahama Yui (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Yuigahama Yui (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 999 images and their tags.
The core tags of this character are `short_hair, hair_bun, single_hair_bun, pink_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 999 | 491.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 999 | 432.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 2111 | 867.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 999 | 490.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 2111 | 955.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru/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/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru',
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, black_jacket, blazer, sobu_high_school_uniform, solo, white_shirt, blush, collared_shirt, open_mouth, neck_ribbon, red_eyes, red_ribbon |
| 1 | 5 |  |  |  |  |  | 1girl, black_jacket, blazer, collared_shirt, hair_between_eyes, open_mouth, red_eyes, sobu_high_school_uniform, solo, bangs, blush, white_shirt, looking_at_viewer, smile |
| 2 | 6 |  |  |  |  |  | 1girl, blazer, open_mouth, ribbon, shirt, sobu_high_school_uniform, solo, pink_eyes, black_jacket, blush, classroom |
| 3 | 9 |  |  |  |  |  | 1girl, blazer, sobu_high_school_uniform, open_mouth, pink_eyes, solo, blush |
| 4 | 8 |  |  |  |  |  | 1girl, black_jacket, blazer, shirt, sobu_high_school_uniform, solo, ribbon, smile, closed_eyes |
| 5 | 7 |  |  |  |  |  | 1girl, bag, pink_eyes, sobu_high_school_uniform, solo, blazer, ribbon, smile, scarf |
| 6 | 5 |  |  |  |  |  | 1girl, blazer, plaid_skirt, sobu_high_school_uniform, black_jacket, solo, brown_eyes, red_hair |
| 7 | 6 |  |  |  |  |  | 1girl, blazer, plaid_skirt, sobu_high_school_uniform, solo, black_jacket, open_mouth, white_shirt, bag, closed_eyes |
| 8 | 7 |  |  |  |  |  | 1girl, indoors, long_sleeves, plaid_skirt, pleated_skirt, sobu_high_school_uniform, solo, white_shirt, collared_shirt, red_bowtie, black_socks, cellphone, kneehighs, standing, brown_eyes, chair, classroom, desk, full_body, holding_phone, open_mouth, ribbon, shoes |
| 9 | 5 |  |  |  |  |  | 1girl, blush, scarf, solo, tears, coat, smile, cloud, looking_at_viewer, sky, sunset |
| 10 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, scarf, solo, bag, coat, blurry, letterboxed, open_mouth, upper_body |
| 11 | 10 |  |  |  |  |  | 1girl, orange_shirt, solo, t-shirt, smile, upper_body, short_sleeves, brown_eyes, closed_mouth, blush, breasts, sitting |
| 12 | 7 |  |  |  |  |  | 1girl, anime_coloring, brown_eyes, brown_hair, pink_headwear, shirt, baseball_cap, collarbone, solo, upper_body, tree, nature, outdoors, parody |
| 13 | 10 |  |  |  |  |  | 1girl, open_mouth, solo, looking_at_viewer, standing, white_shirt, bangs, earrings, sleeveless_shirt, :d, black_skirt, bracelet, red_eyes, single_side_bun |
| 14 | 8 |  |  |  |  |  | 1girl, solo, hair_ornament, yukata, open_mouth, smile, profile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_jacket | blazer | sobu_high_school_uniform | solo | white_shirt | blush | collared_shirt | open_mouth | neck_ribbon | red_eyes | red_ribbon | hair_between_eyes | bangs | looking_at_viewer | smile | ribbon | shirt | pink_eyes | classroom | closed_eyes | bag | scarf | plaid_skirt | brown_eyes | red_hair | indoors | long_sleeves | pleated_skirt | red_bowtie | black_socks | cellphone | kneehighs | standing | chair | desk | full_body | holding_phone | shoes | tears | coat | cloud | sky | sunset | blurry | letterboxed | upper_body | orange_shirt | t-shirt | short_sleeves | closed_mouth | breasts | sitting | anime_coloring | brown_hair | pink_headwear | baseball_cap | collarbone | tree | nature | outdoors | parody | earrings | sleeveless_shirt | :d | black_skirt | bracelet | single_side_bun | hair_ornament | yukata | profile |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:---------------|:---------|:---------------------------|:-------|:--------------|:--------|:-----------------|:-------------|:--------------|:-----------|:-------------|:--------------------|:--------|:--------------------|:--------|:---------|:--------|:------------|:------------|:--------------|:------|:--------|:--------------|:-------------|:-----------|:----------|:---------------|:----------------|:-------------|:--------------|:------------|:------------|:-----------|:--------|:-------|:------------|:----------------|:--------|:--------|:-------|:--------|:------|:---------|:---------|:--------------|:-------------|:---------------|:----------|:----------------|:---------------|:----------|:----------|:-----------------|:-------------|:----------------|:---------------|:-------------|:-------|:---------|:-----------|:---------|:-----------|:-------------------|:-----|:--------------|:-----------|:------------------|:----------------|:---------|:----------|
| 0 | 11 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | X | X | X | X | | X | | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | X | X | X | | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | X | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | X | X | X | | | | | | | | | | | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | X | X | X | X | X | | | X | | | | | | | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | X | | | X | X | X | | X | X | | | | | | | | X | | | X | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | | X | | X | | | | | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | | | | X | | X | | X | | | | | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 10 |  |  |  |  |  | X | | | | X | | X | | | | | | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 12 | 7 |  |  |  |  |  | X | | | | X | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 13 | 10 |  |  |  |  |  | X | | | | X | X | | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | |
| 14 | 8 |  |  |  |  |  | X | | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X |
|
CyberHarem/yuigahama_yui_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T15:55:49+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T13:36:46+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Yuigahama Yui (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
===========================================================================
This is the dataset of Yuigahama Yui (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 999 images and their tags.
The core tags of this character are 'short\_hair, hair\_bun, single\_hair\_bun, pink\_hair', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
8aec9ec1c09b16161a47f1b2ff008451e869a672
|
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/Luban-Marcoroni-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 [Weyaxi/Luban-Marcoroni-13B](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T10:04:59.815183](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B/blob/main/results_2023-10-28T10-04-59.815183.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": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10247063758389327,
"f1_stderr": 0.001983240759454083,
"acc": 0.4351997070321265,
"acc_stderr": 0.010043960065261932
},
"harness|drop|3": {
"em": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10247063758389327,
"f1_stderr": 0.001983240759454083
},
"harness|gsm8k|5": {
"acc": 0.10007581501137225,
"acc_stderr": 0.008266274528685636
},
"harness|winogrande|5": {
"acc": 0.7703235990528808,
"acc_stderr": 0.011821645601838227
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B
|
[
"region:us"
] |
2023-09-13T16:02:27+00:00
|
{"pretty_name": "Evaluation run of Weyaxi/Luban-Marcoroni-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/Luban-Marcoroni-13B](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T10:04:59.815183](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B/blob/main/results_2023-10-28T10-04-59.815183.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\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10247063758389327,\n \"f1_stderr\": 0.001983240759454083,\n \"acc\": 0.4351997070321265,\n \"acc_stderr\": 0.010043960065261932\n },\n \"harness|drop|3\": {\n \"em\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10247063758389327,\n \"f1_stderr\": 0.001983240759454083\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10007581501137225,\n \"acc_stderr\": 0.008266274528685636\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7703235990528808,\n \"acc_stderr\": 0.011821645601838227\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/Luban-Marcoroni-13B", "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_13T17_02_11.381984", "path": ["**/details_harness|arc:challenge|25_2023-09-13T17-02-11.381984.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T17-02-11.381984.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T10_04_59.815183", "path": ["**/details_harness|drop|3_2023-10-28T10-04-59.815183.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T10-04-59.815183.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T10_04_59.815183", "path": ["**/details_harness|gsm8k|5_2023-10-28T10-04-59.815183.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T10-04-59.815183.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T17_02_11.381984", "path": ["**/details_harness|hellaswag|10_2023-09-13T17-02-11.381984.parquet"]}, {"split": "latest", "path": 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|
2023-10-28T09:05:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-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 Weyaxi/Luban-Marcoroni-13B 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 2023-10-28T10:04:59.815183(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-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 Weyaxi/Luban-Marcoroni-13B 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 2023-10-28T10:04:59.815183(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-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 Weyaxi/Luban-Marcoroni-13B 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 2023-10-28T10:04:59.815183(note that their might be results for other tasks in 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 Weyaxi/Luban-Marcoroni-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 Weyaxi/Luban-Marcoroni-13B 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 2023-10-28T10:04:59.815183(note that their might be results for other tasks in 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"
] |
fc54e058ce67d95217c278dd96a9b6b24f17648a
|
# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ
- **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](https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-27T04:39:21.377335](https://huggingface.co/datasets/open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ/blob/main/results_2023-10-27T04-39-21.377335.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": 0.001363255033557047,
"em_stderr": 0.0003778609196461018,
"f1": 0.057080536912751875,
"f1_stderr": 0.0012933707193154948,
"acc": 0.44911238992013397,
"acc_stderr": 0.01146531039524964
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461018,
"f1": 0.057080536912751875,
"f1_stderr": 0.0012933707193154948
},
"harness|gsm8k|5": {
"acc": 0.17210007581501138,
"acc_stderr": 0.010397328057878982
},
"harness|winogrande|5": {
"acc": 0.7261247040252565,
"acc_stderr": 0.012533292732620297
}
}
```
### 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
|
[
"region:us"
] |
2023-09-13T16:15:50+00:00
|
{"pretty_name": "Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ", "dataset_summary": "Dataset automatically created during the evaluation run of model [elliotthwang/Elliott-Chinese-LLaMa-GPTQ](https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-27T04:39:21.377335](https://huggingface.co/datasets/open-llm-leaderboard/details_elliotthwang__Elliott-Chinese-LLaMa-GPTQ/blob/main/results_2023-10-27T04-39-21.377335.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\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196461018,\n \"f1\": 0.057080536912751875,\n \"f1_stderr\": 0.0012933707193154948,\n \"acc\": 0.44911238992013397,\n \"acc_stderr\": 0.01146531039524964\n },\n \"harness|drop|3\": {\n \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196461018,\n \"f1\": 0.057080536912751875,\n \"f1_stderr\": 0.0012933707193154948\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17210007581501138,\n \"acc_stderr\": 0.010397328057878982\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7261247040252565,\n \"acc_stderr\": 0.012533292732620297\n }\n}\n```", "repo_url": "https://huggingface.co/elliotthwang/Elliott-Chinese-LLaMa-GPTQ", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": 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["**/details_harness|truthfulqa:mc|0_2023-09-13T17-15-37.349272.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T17-15-37.349272.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_27T04_39_21.377335", "path": ["**/details_harness|winogrande|5_2023-10-27T04-39-21.377335.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-27T04-39-21.377335.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T17_15_37.349272", "path": ["results_2023-09-13T17-15-37.349272.parquet"]}, {"split": "2023_10_27T04_39_21.377335", "path": ["results_2023-10-27T04-39-21.377335.parquet"]}, {"split": "latest", "path": ["results_2023-10-27T04-39-21.377335.parquet"]}]}]}
|
2023-10-27T03:39:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of elliotthwang/Elliott-Chinese-LLaMa-GPTQ
## 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 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 2023-10-27T04:39:21.377335(note that their might be results for other tasks in 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",
"## 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 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 2023-10-27T04:39:21.377335(note that their might be results for other tasks in 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",
"## 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 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 2023-10-27T04:39:21.377335(note that their might be results for other tasks in 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 elliotthwang/Elliott-Chinese-LLaMa-GPTQ## 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 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 2023-10-27T04:39:21.377335(note that their might be results for other tasks in 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"
] |
883dac76cbbe08c74894fe0d250cea2c0a29d8e2
|
# Dataset of kamiya_nao/神谷奈緒/카미야나오 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of kamiya_nao/神谷奈緒/카미야나오 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, red_eyes, bangs, blunt_bangs, thick_eyebrows, breasts, hair_bun, single_hair_bun`, which are 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 | 691.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamiya_nao_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 396.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamiya_nao_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1225 | 846.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamiya_nao_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 609.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kamiya_nao_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1225 | 1.18 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kamiya_nao_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kamiya_nao_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, smile, solo, looking_at_viewer, belt, blush, earrings, navel, white_shorts, coat, midriff, open_mouth, bow, frills, hair_ornament, long_sleeves, short_shorts, white_background, black_thighhighs, holding_microphone, idol |
| 1 | 39 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, simple_background, white_background, white_shirt, school_uniform, blue_necktie, braid, long_sleeves, striped_necktie, plaid_skirt, pleated_skirt, upper_body, blue_jacket, smile |
| 2 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, smile, solo, blush, hair_flower, fingerless_gloves, thighhighs, skirt, microphone |
| 3 | 10 |  |  |  |  |  | 1girl, elbow_gloves, midriff, skirt, solo, smile, belt, navel, hairband, microphone, open_mouth, black_gloves, blush, looking_at_viewer |
| 4 | 11 |  |  |  |  |  | 1girl, blush, nipples, solo, looking_at_viewer, female_pubic_hair, medium_breasts, navel, large_breasts, completely_nude, sitting, sweat |
| 5 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, open_mouth, solo, wet_shirt, bracelet, see-through, simple_background, white_background, white_shirt, bikini_skirt, low_twintails, navel, purple_bikini, short_sleeves, bikini_under_clothes, cowboy_shot, shirt_lift, smile |
| 6 | 18 |  |  |  |  |  | 1girl, maid_headdress, blush, enmaided, solo, looking_at_viewer, frills, wrist_cuffs, maid_apron, thighhighs, bow, open_mouth, puffy_sleeves, short_sleeves |
| 7 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, blush, cleavage, collarbone, large_breasts, open_mouth, thighs, black_bikini, elbow_gloves, simple_background, white_background, bare_shoulders, black_gloves, black_thighhighs, micro_bikini, side-tie_bikini_bottom, black_choker |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | looking_at_viewer | belt | blush | earrings | navel | white_shorts | coat | midriff | open_mouth | bow | frills | hair_ornament | long_sleeves | short_shorts | white_background | black_thighhighs | holding_microphone | idol | simple_background | white_shirt | school_uniform | blue_necktie | braid | striped_necktie | plaid_skirt | pleated_skirt | upper_body | blue_jacket | hair_flower | fingerless_gloves | thighhighs | skirt | microphone | elbow_gloves | hairband | black_gloves | nipples | female_pubic_hair | medium_breasts | large_breasts | completely_nude | sitting | sweat | wet_shirt | bracelet | see-through | bikini_skirt | low_twintails | purple_bikini | short_sleeves | bikini_under_clothes | cowboy_shot | shirt_lift | maid_headdress | enmaided | wrist_cuffs | maid_apron | puffy_sleeves | cleavage | collarbone | thighs | black_bikini | bare_shoulders | micro_bikini | side-tie_bikini_bottom | black_choker |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:--------------------|:-------|:--------|:-----------|:--------|:---------------|:-------|:----------|:-------------|:------|:---------|:----------------|:---------------|:---------------|:-------------------|:-------------------|:---------------------|:-------|:--------------------|:--------------|:-----------------|:---------------|:--------|:------------------|:--------------|:----------------|:-------------|:--------------|:--------------|:--------------------|:-------------|:--------|:-------------|:---------------|:-----------|:---------------|:----------|:--------------------|:-----------------|:----------------|:------------------|:----------|:--------|:------------|:-----------|:--------------|:---------------|:----------------|:----------------|:----------------|:-----------------------|:--------------|:-------------|:-----------------|:-----------|:--------------|:-------------|:----------------|:-----------|:-------------|:---------|:---------------|:-----------------|:---------------|:-------------------------|:---------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 39 |  |  |  |  |  | X | X | X | X | | X | | | | | | | | | | X | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | X | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | X | X | X | X | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | X | X | | X | | X | | | | X | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 6 | 18 |  |  |  |  |  | X | | X | X | | X | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | X | X | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | | X | X | | X | | X | | | | X | | | | | | X | X | | | X | | | | | | | | | | | | | | | X | | X | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/kamiya_nao_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T16:16:26+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T11:49:33+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kamiya\_nao/神谷奈緒/카미야나오 (THE iDOLM@STER: Cinderella Girls)
====================================================================
This is the dataset of kamiya\_nao/神谷奈緒/카미야나오 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'brown\_hair, long\_hair, red\_eyes, bangs, blunt\_bangs, thick\_eyebrows, breasts, hair\_bun, single\_hair\_bun', which are 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"
] |
b64531289144b4874689327a3f633a07a63d51fa
|
# Dataset of Hiratsuka Shizuka (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Hiratsuka Shizuka (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 297 images and their tags.
The core tags of this character are `long_hair, black_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 | 297 | 139.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 297 | 118.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 581 | 219.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 297 | 139.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 581 | 251.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru/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/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, closed_eyes, labcoat, necktie, solo, chair, open_mouth, sitting, cup, smile |
| 1 | 5 |  |  |  |  |  | 1girl, closed_eyes, shirt, solo, upper_body, bangs, blue_necktie, labcoat, open_mouth, anime_coloring, smile, school_uniform |
| 2 | 5 |  |  |  |  |  | 1girl, blue_necktie, labcoat, solo, upper_body, vest, black_eyes, grey_eyes, sweatdrop, white_shirt |
| 3 | 6 |  |  |  |  |  | 1girl, closed_eyes, labcoat, necktie, solo |
| 4 | 5 |  |  |  |  |  | 1girl, belt, labcoat, necktie, solo, looking_at_viewer, shirt, very_long_hair, black_pants, hand_on_hip, smile, brown_hair, grey_eyes, open_mouth |
| 5 | 6 |  |  |  |  |  | 1girl, bangs, closed_mouth, jacket, school_uniform, smile, solo, upper_body, white_shirt, collared_shirt, grey_eyes, blue_necktie, looking_at_viewer, anime_coloring, hair_between_eyes, indoors |
| 6 | 10 |  |  |  |  |  | 1girl, solo, hair_ribbon, neck_ribbon, red_ribbon, white_shirt, blue_eyes, collared_shirt, sobu_high_school_uniform, upper_body, bangs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | closed_eyes | labcoat | necktie | solo | chair | open_mouth | sitting | cup | smile | shirt | upper_body | bangs | blue_necktie | anime_coloring | school_uniform | vest | black_eyes | grey_eyes | sweatdrop | white_shirt | belt | looking_at_viewer | very_long_hair | black_pants | hand_on_hip | brown_hair | closed_mouth | jacket | collared_shirt | hair_between_eyes | indoors | hair_ribbon | neck_ribbon | red_ribbon | blue_eyes | sobu_high_school_uniform |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:----------|:----------|:-------|:--------|:-------------|:----------|:------|:--------|:--------|:-------------|:--------|:---------------|:-----------------|:-----------------|:-------|:-------------|:------------|:------------|:--------------|:-------|:--------------------|:-----------------|:--------------|:--------------|:-------------|:---------------|:---------|:-----------------|:--------------------|:----------|:--------------|:--------------|:-------------|:------------|:---------------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | 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 | 6 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | X | X | | X | | | X | X | | | | | | | | X | | | X | X | X | X | X | X | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | | | X | | | | | X | | X | X | X | X | X | | | X | | X | | X | | | | | X | X | X | X | X | | | | | |
| 6 | 10 |  |  |  |  |  | X | | | | X | | | | | | | X | X | | | | | | | | X | | | | | | | | | X | | | X | X | X | X | X |
|
CyberHarem/hiratsuka_shizuka_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T16:19:24+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T14:22:32+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Hiratsuka Shizuka (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
===============================================================================
This is the dataset of Hiratsuka Shizuka (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 297 images and their tags.
The core tags of this character are 'long\_hair, black\_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"
] |
9880b75f7597823e1e9191667e38ac03e90c709f
|
# Dataset Card for "contextual-tiny-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
magnifi/contextual-tiny-v1
|
[
"region:us"
] |
2023-09-13T16:22:53+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "user_text", "dtype": "string"}, {"name": "true_intent", "dtype": "string"}, {"name": "chat_history", "dtype": "string"}, {"name": "contextual", "dtype": "bool"}, {"name": "in_regression_test", "dtype": "bool"}, {"name": "synthetic", "dtype": "bool"}, {"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 106909.92835858747, "num_examples": 100}, {"name": "validation", "num_bytes": 10722.453155139157, "num_examples": 10}], "download_size": 42788, "dataset_size": 117632.38151372662}}
|
2023-09-13T16:22:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "contextual-tiny-v1"
More Information needed
|
[
"# Dataset Card for \"contextual-tiny-v1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"contextual-tiny-v1\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"contextual-tiny-v1\"\n\nMore Information needed"
] |
9279eb23874536952f34406f3cdc242edc9073e7
|
# Dataset of Yukinoshita Haruno (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Yukinoshita Haruno (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 218 images and their tags.
The core tags of this character are `black_hair, short_hair, bangs, 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 | 218 | 109.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 218 | 92.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 429 | 171.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 218 | 109.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 429 | 197.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru/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/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1boy, 1girl, jacket, school_uniform, smile, solo_focus, necktie, white_shirt, cup, doughnut, open_mouth |
| 1 | 15 |  |  |  |  |  | white_shirt, 1girl, smile, collared_shirt, solo, handbag, open_clothes, cardigan, black_jacket, closed_mouth, dress_shirt, skirt, shoulder_bag, upper_body |
| 2 | 8 |  |  |  |  |  | 1girl, solo, blurry_background, upper_body, anime_coloring, medium_hair, smile, closed_mouth, hair_between_eyes, looking_at_viewer, indoors, red_jacket, head_rest, portrait, white_shirt |
| 3 | 10 |  |  |  |  |  | 1girl, earrings, solo, smile, bare_shoulders, blue_eyes, collarbone, off-shoulder_sweater, anime_coloring, upper_body, looking_at_viewer |
| 4 | 7 |  |  |  |  |  | 1girl, open_mouth, solo, anime_coloring, smile, closed_eyes, collarbone, blush, portrait |
| 5 | 21 |  |  |  |  |  | 1girl, solo, green_scarf, smile, jacket, looking_at_viewer, medium_hair, upper_body, open_mouth, indoors, ahoge, blurry, closed_mouth |
| 6 | 5 |  |  |  |  |  | 1girl, close-up, hair_between_eyes, looking_at_viewer, parody, solo, long_hair, anime_coloring, blue_eyes, open_mouth |
| 7 | 5 |  |  |  |  |  | jeans, necklace, sitting, smile, 1girl, 2girls, blue_eyes, bracelet, cleavage, long_hair, solo_focus, yellow_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | jacket | school_uniform | smile | solo_focus | necktie | white_shirt | cup | doughnut | open_mouth | collared_shirt | solo | handbag | open_clothes | cardigan | black_jacket | closed_mouth | dress_shirt | skirt | shoulder_bag | upper_body | blurry_background | anime_coloring | medium_hair | hair_between_eyes | looking_at_viewer | indoors | red_jacket | head_rest | portrait | earrings | bare_shoulders | blue_eyes | collarbone | off-shoulder_sweater | closed_eyes | blush | green_scarf | ahoge | blurry | close-up | parody | long_hair | jeans | necklace | sitting | 2girls | bracelet | cleavage | yellow_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:---------|:-----------------|:--------|:-------------|:----------|:--------------|:------|:-----------|:-------------|:-----------------|:-------|:----------|:---------------|:-----------|:---------------|:---------------|:--------------|:--------|:---------------|:-------------|:--------------------|:-----------------|:--------------|:--------------------|:--------------------|:----------|:-------------|:------------|:-----------|:-----------|:-----------------|:------------|:-------------|:-----------------------|:--------------|:--------|:--------------|:--------|:---------|:-----------|:---------|:------------|:--------|:-----------|:----------|:---------|:-----------|:-----------|:---------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | | X | | | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | | X | | | X | | | X | | | | | X | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | | X | | | X | | | | | | | | X | | | | | | | | | X | | X | | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | | X | | | X | | | | | | X | | X | | | | | | | | | | | X | | | | | | | X | | | | X | | X | X | | | | | | | | | | | | | |
| 5 | 21 |  |  |  |  |  | | X | X | | X | | | | | | X | | X | | | | | X | | | | X | | | X | | X | X | | | | | | | | | | | X | X | X | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | | X | | | | | | | | | X | | X | | | | | | | | | | | X | | X | X | | | | | | | X | | | | | | | | X | X | X | | | | | | | |
| 7 | 5 |  |  |  |  |  | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X |
|
CyberHarem/yukinoshita_haruno_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T16:42:30+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T14:36:26+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Yukinoshita Haruno (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
================================================================================
This is the dataset of Yukinoshita Haruno (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 218 images and their tags.
The core tags of this character are 'black\_hair, short\_hair, bangs, 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"
] |
6282a746c54ac679876cd012c3a5cb2db2fb20fe
|
# Dataset Card for Evaluation run of TurkuNLP/gpt3-finnish-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TurkuNLP/gpt3-finnish-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TurkuNLP/gpt3-finnish-13B](https://huggingface.co/TurkuNLP/gpt3-finnish-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T08:19:11.789658](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public/blob/main/results_2023-11-08T08-19-11.789658.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.010906040268456376,
"em_stderr": 0.0010636334198497977,
"f1": 0.0586136744966444,
"f1_stderr": 0.001583703669300269,
"acc": 0.29157154884622954,
"acc_stderr": 0.007692758773767466
},
"harness|drop|3": {
"em": 0.010906040268456376,
"em_stderr": 0.0010636334198497977,
"f1": 0.0586136744966444,
"f1_stderr": 0.001583703669300269
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245414
},
"harness|winogrande|5": {
"acc": 0.580110497237569,
"acc_stderr": 0.013870943986310391
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B
|
[
"region:us"
] |
2023-09-13T16:54:34+00:00
|
{"pretty_name": "Evaluation run of TurkuNLP/gpt3-finnish-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [TurkuNLP/gpt3-finnish-13B](https://huggingface.co/TurkuNLP/gpt3-finnish-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-08T08:19:11.789658](https://huggingface.co/datasets/open-llm-leaderboard/details_TurkuNLP__gpt3-finnish-13B_public/blob/main/results_2023-11-08T08-19-11.789658.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.010906040268456376,\n \"em_stderr\": 0.0010636334198497977,\n \"f1\": 0.0586136744966444,\n \"f1_stderr\": 0.001583703669300269,\n \"acc\": 0.29157154884622954,\n \"acc_stderr\": 0.007692758773767466\n },\n \"harness|drop|3\": {\n \"em\": 0.010906040268456376,\n \"em_stderr\": 0.0010636334198497977,\n \"f1\": 0.0586136744966444,\n \"f1_stderr\": 0.001583703669300269\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \"acc_stderr\": 0.0015145735612245414\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.580110497237569,\n \"acc_stderr\": 0.013870943986310391\n }\n}\n```", "repo_url": "https://huggingface.co/TurkuNLP/gpt3-finnish-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_08T08_19_11.789658", "path": ["**/details_harness|drop|3_2023-11-08T08-19-11.789658.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-08T08-19-11.789658.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_08T08_19_11.789658", "path": ["**/details_harness|gsm8k|5_2023-11-08T08-19-11.789658.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-08T08-19-11.789658.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_08T08_19_11.789658", "path": ["**/details_harness|winogrande|5_2023-11-08T08-19-11.789658.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-08T08-19-11.789658.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_08T08_19_11.789658", "path": ["results_2023-11-08T08-19-11.789658.parquet"]}, {"split": "latest", "path": ["results_2023-11-08T08-19-11.789658.parquet"]}]}]}
|
2023-12-01T14:47:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TurkuNLP/gpt3-finnish-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 TurkuNLP/gpt3-finnish-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 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 2023-11-08T08:19:11.789658(note that their might be results for other tasks in 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 TurkuNLP/gpt3-finnish-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 TurkuNLP/gpt3-finnish-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 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 2023-11-08T08:19:11.789658(note that their might be results for other tasks in 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 TurkuNLP/gpt3-finnish-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 TurkuNLP/gpt3-finnish-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 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 2023-11-08T08:19:11.789658(note that their might be results for other tasks in 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",
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"## Dataset Structure",
"### Data Instances",
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"### Data Splits",
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"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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"### 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 TurkuNLP/gpt3-finnish-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 TurkuNLP/gpt3-finnish-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 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 2023-11-08T08:19:11.789658(note that their might be results for other tasks in 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"
] |
57ead08ce8a335709a5ac3174e182bfd0f1dea08
|
# Dataset Card for Evaluation run of oh-yeontaek/llama-2-7B-LoRA-assemble
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/oh-yeontaek/llama-2-7B-LoRA-assemble
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-7B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-7B-LoRA-assemble) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_oh-yeontaek__llama-2-7B-LoRA-assemble",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:43:13.966405](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-7B-LoRA-assemble/blob/main/results_2023-10-24T23-43-13.966405.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": 0.31596057046979864,
"em_stderr": 0.004760983364669265,
"f1": 0.39136640100671266,
"f1_stderr": 0.004644890166719777,
"acc": 0.3674033149171271,
"acc_stderr": 0.006203274733096429
},
"harness|drop|3": {
"em": 0.31596057046979864,
"em_stderr": 0.004760983364669265,
"f1": 0.39136640100671266,
"f1_stderr": 0.004644890166719777
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7348066298342542,
"acc_stderr": 0.012406549466192858
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_oh-yeontaek__llama-2-7B-LoRA-assemble
|
[
"region:us"
] |
2023-09-13T16:57:32+00:00
|
{"pretty_name": "Evaluation run of oh-yeontaek/llama-2-7B-LoRA-assemble", "dataset_summary": "Dataset automatically created during the evaluation run of model [oh-yeontaek/llama-2-7B-LoRA-assemble](https://huggingface.co/oh-yeontaek/llama-2-7B-LoRA-assemble) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_oh-yeontaek__llama-2-7B-LoRA-assemble\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-24T23:43:13.966405](https://huggingface.co/datasets/open-llm-leaderboard/details_oh-yeontaek__llama-2-7B-LoRA-assemble/blob/main/results_2023-10-24T23-43-13.966405.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\": 0.31596057046979864,\n \"em_stderr\": 0.004760983364669265,\n \"f1\": 0.39136640100671266,\n \"f1_stderr\": 0.004644890166719777,\n \"acc\": 0.3674033149171271,\n \"acc_stderr\": 0.006203274733096429\n },\n \"harness|drop|3\": {\n \"em\": 0.31596057046979864,\n \"em_stderr\": 0.004760983364669265,\n \"f1\": 0.39136640100671266,\n \"f1_stderr\": 0.004644890166719777\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7348066298342542,\n \"acc_stderr\": 0.012406549466192858\n }\n}\n```", "repo_url": "https://huggingface.co/oh-yeontaek/llama-2-7B-LoRA-assemble", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|arc:challenge|25_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_24T23_43_13.966405", "path": ["**/details_harness|drop|3_2023-10-24T23-43-13.966405.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-24T23-43-13.966405.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_24T23_43_13.966405", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-43-13.966405.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-24T23-43-13.966405.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hellaswag|10_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T17-57-16.083940.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T17-57-16.083940.parquet", 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"latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-management|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-marketing|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-13T17-57-16.083940.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_09_13T17_57_16.083940", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-09-13T17-57-16.083940.parquet"]}, {"split": "latest", "path": 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|
2023-10-24T22:43:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of oh-yeontaek/llama-2-7B-LoRA-assemble
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model oh-yeontaek/llama-2-7B-LoRA-assemble on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-24T23:43:13.966405(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of oh-yeontaek/llama-2-7B-LoRA-assemble",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-7B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:43:13.966405(note that their might be results for other tasks in 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 Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-7B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-24T23:43:13.966405(note that their might be results for other tasks in 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",
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"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
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"## 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 oh-yeontaek/llama-2-7B-LoRA-assemble## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model oh-yeontaek/llama-2-7B-LoRA-assemble on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-24T23:43:13.966405(note that their might be results for other tasks in 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"
] |
35a718302235958bedf5d9216ba05435fd56ed90
|
# Dataset of Olivia
This is the dataset of Olivia, 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 | 641 | [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 | 641 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 641 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 641 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/olivia_asobiasobase
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T16:59:44+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:35:59+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Olivia
=================
This is the dataset of Olivia, 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"
] |
f38b1f7e6ecbccc09fc9385dbf6fd34f1ad84ffb
|
# Dataset of Hikigaya Komachi (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Hikigaya Komachi (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 240 images and their tags.
The core tags of this character are `short_hair, black_hair, ahoge, hair_ornament, hairclip, x_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 | 240 | 117.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 240 | 100.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 463 | 186.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 240 | 117.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 463 | 208.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru/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/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, solo, serafuku, anime_coloring, open_mouth, purple_eyes, blush, looking_at_viewer |
| 1 | 9 |  |  |  |  |  | 1girl, black_jacket, blazer, collared_shirt, sobu_high_school_uniform, white_shirt, solo, upper_body, indoors, neck_ribbon, red_bow, red_ribbon, smile, blue_hair, closed_mouth, bangs, blurry, cardigan, long_sleeves, open_mouth, purple_eyes, wing_collar |
| 2 | 9 |  |  |  |  |  | 1girl, solo, green_shirt, upper_body, bangs, purple_eyes, closed_mouth, indoors, smile |
| 3 | 5 |  |  |  |  |  | 1girl, long_sleeves, sailor_collar, serafuku, solo, white_shirt, pleated_skirt, aqua_neckerchief, aqua_skirt, black_socks, blush, green_skirt, kneehighs, looking_at_viewer, smile, bangs, blue_hair, blue_skirt, blurry, closed_mouth, indoors, open_mouth, sitting, standing |
| 4 | 7 |  |  |  |  |  | 1girl, paw_gloves, solo, cat_ears, cat_tail, :d, closed_eyes, grey_background, open_mouth, simple_background, upper_body, animal_costume, bangs, blue_eyes, brown_shirt, facing_viewer, fang, fur-trimmed_gloves, kemonomimi_mode, shorts, sleeveless |
| 5 | 8 |  |  |  |  |  | smile, yellow_bikini, 1girl, navel, solo, armpits, black_eyes, cleavage, frilled_bikini, front-tie_top, looking_at_viewer, open_mouth, sandals, water |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | serafuku | anime_coloring | open_mouth | purple_eyes | blush | looking_at_viewer | black_jacket | blazer | collared_shirt | sobu_high_school_uniform | white_shirt | upper_body | indoors | neck_ribbon | red_bow | red_ribbon | smile | blue_hair | closed_mouth | bangs | blurry | cardigan | long_sleeves | wing_collar | green_shirt | sailor_collar | pleated_skirt | aqua_neckerchief | aqua_skirt | black_socks | green_skirt | kneehighs | blue_skirt | sitting | standing | paw_gloves | cat_ears | cat_tail | :d | closed_eyes | grey_background | simple_background | animal_costume | blue_eyes | brown_shirt | facing_viewer | fang | fur-trimmed_gloves | kemonomimi_mode | shorts | sleeveless | yellow_bikini | navel | armpits | black_eyes | cleavage | frilled_bikini | front-tie_top | sandals | water |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:-----------------|:-------------|:--------------|:--------|:--------------------|:---------------|:---------|:-----------------|:---------------------------|:--------------|:-------------|:----------|:--------------|:----------|:-------------|:--------|:------------|:---------------|:--------|:---------|:-----------|:---------------|:--------------|:--------------|:----------------|:----------------|:-------------------|:-------------|:--------------|:--------------|:------------|:-------------|:----------|:-----------|:-------------|:-----------|:-----------|:-----|:--------------|:------------------|:--------------------|:-----------------|:------------|:--------------|:----------------|:-------|:---------------------|:------------------|:---------|:-------------|:----------------|:--------|:----------|:-------------|:-----------|:-----------------|:----------------|:----------|:--------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | | | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | 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 | 7 |  |  |  |  |  | X | X | | | X | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | X | | | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
CyberHarem/hikigaya_komachi_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:01:10+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T14:50:19+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Hikigaya Komachi (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
==============================================================================
This is the dataset of Hikigaya Komachi (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 240 images and their tags.
The core tags of this character are 'short\_hair, black\_hair, ahoge, hair\_ornament, hairclip, x\_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"
] |
a3cc2466134b1e0cf57ea805deab0a70c47ddbe2
|
# Dataset of Ebina Hina (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Ebina Hina (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 136 images and their tags.
The core tags of this character are `short_hair, brown_hair, glasses, black_hair, brown_eyes, red-framed_eyewear`, which are 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 | 136 | 53.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ebina_hina_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 136 | 47.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ebina_hina_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 255 | 86.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ebina_hina_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 136 | 53.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ebina_hina_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 255 | 95.12 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ebina_hina_yahariorenoseishunlovecomewamachigatteiru/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/ebina_hina_yahariorenoseishunlovecomewamachigatteiru',
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, over-rim_eyewear, smile, solo, sunset, cellphone, green_scarf, school_uniform |
| 1 | 35 |  |  |  |  |  | 1girl, blazer, sobu_high_school_uniform, solo, smile, ribbon, over-rim_eyewear, looking_at_viewer |
| 2 | 7 |  |  |  |  |  | 1girl, blazer, kneehighs, open_mouth, skirt, sobu_high_school_uniform, solo, black_jacket, loafers, smile, black_socks, from_side |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | over-rim_eyewear | smile | solo | sunset | cellphone | green_scarf | school_uniform | blazer | sobu_high_school_uniform | ribbon | looking_at_viewer | kneehighs | open_mouth | skirt | black_jacket | loafers | black_socks | from_side |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:--------|:-------|:---------|:------------|:--------------|:-----------------|:---------|:---------------------------|:---------|:--------------------|:------------|:-------------|:--------|:---------------|:----------|:--------------|:------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 1 | 35 |  |  |  |  |  | X | X | X | X | | | | | X | X | X | X | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | X | X | | | | | X | X | | | X | X | X | X | X | X | X |
|
CyberHarem/ebina_hina_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:10:42+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T14:57:24+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Ebina Hina (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
========================================================================
This is the dataset of Ebina Hina (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 136 images and their tags.
The core tags of this character are 'short\_hair, brown\_hair, glasses, black\_hair, brown\_eyes, red-framed\_eyewear', which are 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"
] |
9db3406b68eb6fdbfa9a5e9aabdadd90e67fa051
|
# Dataset Card for "Metallography_segmenter_Dataset_A1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_A1
|
[
"region:us"
] |
2023-09-13T17:24:11+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 319014423.772, "num_examples": 1557}, {"name": "validation", "num_bytes": 78786966.0, "num_examples": 390}], "download_size": 394795306, "dataset_size": 397801389.772}}
|
2023-12-04T00:29:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_A1"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_A1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_A1\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_A1\"\n\nMore Information needed"
] |
98e3bc56406a1499d647b7a43c953d29b2eb6380
|
# Dataset Card for "comparison-data-falcon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
golightly/comparison-data-falcon
|
[
"region:us"
] |
2023-09-13T17:25:01+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string", "id": "field"}, {"name": "response-1", "dtype": "string", "id": "field"}, {"name": "response-2", "dtype": "string", "id": "field"}, {"name": "choose-best", "list": [{"name": "user_id", "dtype": "string", "id": "question"}, {"name": "value", "dtype": "int32", "id": "suggestion"}, {"name": "status", "dtype": "string", "id": "question"}]}, {"name": "choose-best-suggestion", "dtype": "int32", "id": "suggestion"}, {"name": "choose-best-suggestion-metadata", "struct": [{"name": "type", "dtype": "string", "id": "suggestion-metadata"}, {"name": "score", "dtype": "float32", "id": "suggestion-metadata"}, {"name": "agent", "dtype": "string", "id": "suggestion-metadata"}]}, {"name": "external_id", "dtype": "string", "id": "external_id"}, {"name": "metadata", "dtype": "string", "id": "metadata"}], "splits": [{"name": "train", "num_bytes": 8163688, "num_examples": 7401}], "download_size": 0, "dataset_size": 8163688}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T18:29:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "comparison-data-falcon"
More Information needed
|
[
"# Dataset Card for \"comparison-data-falcon\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"comparison-data-falcon\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"comparison-data-falcon\"\n\nMore Information needed"
] |
a296b2dab47bdf30d92724155f0c64f100cefada
|
# Dataset of Hanako Honda
This is the dataset of Hanako Honda, 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 | 674 | [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 | 674 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 674 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 674 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/hanako_honda_asobiasobase
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:25:05+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:36:05+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Hanako Honda
=======================
This is the dataset of Hanako Honda, 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"
] |
a63db141a7d6eb38413ced5871515bb5fd1c8b05
|
# Dataset Card for "wiki_mmlu_from_valid_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sordonia/wiki_mmlu_from_valid_all
|
[
"region:us"
] |
2023-09-13T17:25:09+00:00
|
{"dataset_info": {"features": [{"name": "subject", "dtype": "string"}, {"name": "docno", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "dfq", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1394179124, "num_examples": 136591}], "download_size": 767951516, "dataset_size": 1394179124}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T17:25:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "wiki_mmlu_from_valid_all"
More Information needed
|
[
"# Dataset Card for \"wiki_mmlu_from_valid_all\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_mmlu_from_valid_all\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_mmlu_from_valid_all\"\n\nMore Information needed"
] |
f87264800980803c2e35d8066bbe9678472557cd
|
# Dataset Card for "bees-v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) 🐝
|
BEE-spoke-data/bees-v0
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"bees",
"pollen",
"honey",
"bzz",
"region:us"
] |
2023-09-13T17:25:35+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "fill-mask"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15077487, "num_examples": 48561}], "download_size": 8856859, "dataset_size": 15077487}, "tags": ["bees", "pollen", "honey", "bzz"]}
|
2023-09-13T18:59:27+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-fill-mask #size_categories-10K<n<100K #language-English #license-apache-2.0 #bees #pollen #honey #bzz #region-us
|
# Dataset Card for "bees-v0"
More Information needed
|
[
"# Dataset Card for \"bees-v0\"\n\nMore Information needed"
] |
[
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-10K<n<100K #language-English #license-apache-2.0 #bees #pollen #honey #bzz #region-us \n",
"# Dataset Card for \"bees-v0\"\n\nMore Information needed"
] |
[
64,
15
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-10K<n<100K #language-English #license-apache-2.0 #bees #pollen #honey #bzz #region-us \n# Dataset Card for \"bees-v0\"\n\nMore Information needed"
] |
e8154265940404b87188148adb34762b9dbdecff
|
# Dataset of Isshiki Iroha (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Isshiki Iroha (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 529 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes, 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 | 529 | 251.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 529 | 216.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1077 | 422.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 529 | 251.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1077 | 472.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru/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/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru',
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 | 13 |  |  |  |  |  | 1girl, blazer, sobu_high_school_uniform, solo, black_jacket, yellow_eyes, looking_at_viewer, open_mouth, smile |
| 1 | 5 |  |  |  |  |  | 1girl, blazer, open_mouth, sobu_high_school_uniform, solo, profile, black_jacket |
| 2 | 7 |  |  |  |  |  | 1girl, black_jacket, blazer, blush, sobu_high_school_uniform, solo, open_mouth, yellow_eyes, anime_coloring, looking_at_viewer |
| 3 | 5 |  |  |  |  |  | 1girl, black_jacket, blazer, closed_eyes, sobu_high_school_uniform, solo, blush, smile |
| 4 | 15 |  |  |  |  |  | 1girl, black_jacket, blazer, neck_ribbon, sobu_high_school_uniform, solo, upper_body, white_shirt, red_ribbon, bangs, collared_shirt, open_jacket, looking_at_viewer, yellow_eyes, blush, closed_mouth, smile, indoors, pink_cardigan, open_mouth |
| 5 | 7 |  |  |  |  |  | 1girl, black_jacket, blazer, closed_eyes, neck_ribbon, red_ribbon, sobu_high_school_uniform, solo, upper_body, white_shirt, facing_viewer, pink_cardigan, smile, blush, closed_mouth, collared_shirt, bangs, chalkboard |
| 6 | 11 |  |  |  |  |  | 1girl, black_jacket, blazer, neck_ribbon, pink_cardigan, plaid_skirt, sobu_high_school_uniform, solo, white_shirt, long_sleeves, pleated_skirt, red_ribbon, collared_shirt, bangs, open_jacket, open_mouth, closed_eyes, red_bow, sitting, standing |
| 7 | 5 |  |  |  |  |  | 1girl, blazer, sitting, skirt, sobu_high_school_uniform, solo, black_socks, kneehighs, black_jacket, chair, open_mouth, blush, closed_eyes |
| 8 | 6 |  |  |  |  |  | 1girl, blush, parody, solo, yellow_eyes, anime_coloring, open_mouth |
| 9 | 10 |  |  |  |  |  | 1girl, hair_flower, solo, looking_at_viewer, smile, collarbone, sleeveless, yellow_eyes, open_mouth, parody, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blazer | sobu_high_school_uniform | solo | black_jacket | yellow_eyes | looking_at_viewer | open_mouth | smile | profile | blush | anime_coloring | closed_eyes | neck_ribbon | upper_body | white_shirt | red_ribbon | bangs | collared_shirt | open_jacket | closed_mouth | indoors | pink_cardigan | facing_viewer | chalkboard | plaid_skirt | long_sleeves | pleated_skirt | red_bow | sitting | standing | skirt | black_socks | kneehighs | chair | parody | hair_flower | collarbone | sleeveless |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:---------------------------|:-------|:---------------|:--------------|:--------------------|:-------------|:--------|:----------|:--------|:-----------------|:--------------|:--------------|:-------------|:--------------|:-------------|:--------|:-----------------|:--------------|:---------------|:----------|:----------------|:----------------|:-------------|:--------------|:---------------|:----------------|:----------|:----------|:-----------|:--------|:--------------|:------------|:--------|:---------|:--------------|:-------------|:-------------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | X | X | X | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | X | X | 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 | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | X | X | X | | | X | | | X | | X | | | | | | | | | | | | | | | | | X | | X | X | X | X | | | | |
| 8 | 6 |  |  |  |  |  | X | | | X | | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | |
| 9 | 10 |  |  |  |  |  | X | | | X | | X | X | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X |
|
CyberHarem/isshiki_iroha_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:28:22+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T14:06:08+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Isshiki Iroha (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
===========================================================================
This is the dataset of Isshiki Iroha (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 529 images and their tags.
The core tags of this character are 'brown\_hair, short\_hair, brown\_eyes, 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"
] |
68c5e120e2ad759d76fff314441a6a5391e4b687
|
# Dataset Card for "redpajama-sample_from_valid_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sordonia/redpajama-sample_from_valid_all
|
[
"region:us"
] |
2023-09-13T17:32:24+00:00
|
{"dataset_info": {"features": [{"name": "subject", "dtype": "string"}, {"name": "docno", "dtype": "int64"}, {"name": "score", "dtype": "float64"}, {"name": "dfq", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2289695594, "num_examples": 133927}], "download_size": 1236906938, "dataset_size": 2289695594}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T17:38:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "redpajama-sample_from_valid_all"
More Information needed
|
[
"# Dataset Card for \"redpajama-sample_from_valid_all\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"redpajama-sample_from_valid_all\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"redpajama-sample_from_valid_all\"\n\nMore Information needed"
] |
2f3921b0f265f67538aba9e8661ed54cdd8a71a9
|
# Dataset of kobayakawa_sae/小早川紗枝 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of kobayakawa_sae/小早川紗枝 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `long_hair, black_hair, hair_ornament, black_eyes, hair_flower, bangs, braid, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 532.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kobayakawa_sae_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 350.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kobayakawa_sae_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1145 | 721.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kobayakawa_sae_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 490.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kobayakawa_sae_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1145 | 942.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kobayakawa_sae_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kobayakawa_sae_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, blush, flower, looking_at_viewer, solo, navel, simple_background, white_background, pink_bikini, small_breasts, smile, cleavage, collarbone, micro_bikini |
| 1 | 15 |  |  |  |  |  | 1girl, flower, kimono, solo, blush, looking_at_viewer, smile, open_mouth |
| 2 | 6 |  |  |  |  |  | 1girl, blush, floral_print, flower, kimono, looking_at_viewer, obi, solo, smile, wide_sleeves, long_sleeves, open_mouth |
| 3 | 5 |  |  |  |  |  | 1girl, blush, flower, looking_at_viewer, serafuku, simple_background, solo, white_background, red_neckerchief, smile, upper_body, sailor_collar, long_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | flower | looking_at_viewer | solo | navel | simple_background | white_background | pink_bikini | small_breasts | smile | cleavage | collarbone | micro_bikini | kimono | open_mouth | floral_print | obi | wide_sleeves | long_sleeves | serafuku | red_neckerchief | upper_body | sailor_collar |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:---------|:--------------------|:-------|:--------|:--------------------|:-------------------|:--------------|:----------------|:--------|:-----------|:-------------|:---------------|:---------|:-------------|:---------------|:------|:---------------|:---------------|:-----------|:------------------|:-------------|:----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | X | X | X | X | | | | | | X | | | | X | X | | | | | | | | |
| 2 | 6 |  |  |  |  |  | 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 |
|
CyberHarem/kobayakawa_sae_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:37:36+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T17:49:13+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of kobayakawa\_sae/小早川紗枝 (THE iDOLM@STER: Cinderella Girls)
===================================================================
This is the dataset of kobayakawa\_sae/小早川紗枝 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'long\_hair, black\_hair, hair\_ornament, black\_eyes, hair\_flower, bangs, braid, 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"
] |
e0f9690b1828557616bbc183e6a1b0e9afcf4e04
|
# Dataset Card for Evaluation run of Secbone/llama-2-13B-instructed
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Secbone/llama-2-13B-instructed
- **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 [Secbone/llama-2-13B-instructed](https://huggingface.co/Secbone/llama-2-13B-instructed) 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_Secbone__llama-2-13B-instructed",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T10:36:52.287933](https://huggingface.co/datasets/open-llm-leaderboard/details_Secbone__llama-2-13B-instructed/blob/main/results_2023-10-29T10-36-52.287933.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": 0.014681208053691275,
"em_stderr": 0.0012317113143108715,
"f1": 0.07063863255033563,
"f1_stderr": 0.0017279915547624347,
"acc": 0.410347701883647,
"acc_stderr": 0.009905479639395871
},
"harness|drop|3": {
"em": 0.014681208053691275,
"em_stderr": 0.0012317113143108715,
"f1": 0.07063863255033563,
"f1_stderr": 0.0017279915547624347
},
"harness|gsm8k|5": {
"acc": 0.0803639120545868,
"acc_stderr": 0.007488258573239077
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
}
}
```
### 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_Secbone__llama-2-13B-instructed
|
[
"region:us"
] |
2023-09-13T17:39:34+00:00
|
{"pretty_name": "Evaluation run of Secbone/llama-2-13B-instructed", "dataset_summary": "Dataset automatically created during the evaluation run of model [Secbone/llama-2-13B-instructed](https://huggingface.co/Secbone/llama-2-13B-instructed) 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_Secbone__llama-2-13B-instructed\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T10:36:52.287933](https://huggingface.co/datasets/open-llm-leaderboard/details_Secbone__llama-2-13B-instructed/blob/main/results_2023-10-29T10-36-52.287933.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\": 0.014681208053691275,\n \"em_stderr\": 0.0012317113143108715,\n \"f1\": 0.07063863255033563,\n \"f1_stderr\": 0.0017279915547624347,\n \"acc\": 0.410347701883647,\n \"acc_stderr\": 0.009905479639395871\n },\n \"harness|drop|3\": {\n \"em\": 0.014681208053691275,\n \"em_stderr\": 0.0012317113143108715,\n \"f1\": 0.07063863255033563,\n \"f1_stderr\": 0.0017279915547624347\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0803639120545868,\n \"acc_stderr\": 0.007488258573239077\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n }\n}\n```", "repo_url": "https://huggingface.co/Secbone/llama-2-13B-instructed", "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_13T18_39_18.624923", "path": ["**/details_harness|arc:challenge|25_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T10_36_52.287933", "path": ["**/details_harness|drop|3_2023-10-29T10-36-52.287933.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T10-36-52.287933.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T10_36_52.287933", "path": ["**/details_harness|gsm8k|5_2023-10-29T10-36-52.287933.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T10-36-52.287933.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hellaswag|10_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T18-39-18.624923.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T18-39-18.624923.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_29T10_36_52.287933", "path": ["**/details_harness|winogrande|5_2023-10-29T10-36-52.287933.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-29T10-36-52.287933.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T18_39_18.624923", "path": ["results_2023-09-13T18-39-18.624923.parquet"]}, {"split": "2023_10_29T10_36_52.287933", "path": ["results_2023-10-29T10-36-52.287933.parquet"]}, {"split": "latest", "path": ["results_2023-10-29T10-36-52.287933.parquet"]}]}]}
|
2023-10-29T10:37:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Secbone/llama-2-13B-instructed
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Secbone/llama-2-13B-instructed 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 2023-10-29T10:36:52.287933(note that their might be results for other tasks in 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 Secbone/llama-2-13B-instructed",
"## 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 Secbone/llama-2-13B-instructed 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 2023-10-29T10:36:52.287933(note that their might be results for other tasks in 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 Secbone/llama-2-13B-instructed",
"## 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 Secbone/llama-2-13B-instructed 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 2023-10-29T10:36:52.287933(note that their might be results for other tasks in 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 Secbone/llama-2-13B-instructed## 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 Secbone/llama-2-13B-instructed 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 2023-10-29T10:36:52.287933(note that their might be results for other tasks in 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"
] |
938ebdd8e22489fb4e38e0592cbb550c440dfb1c
|
# Dataset of Totsuka Saika (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Totsuka Saika (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 192 images and their tags.
The core tags of this character are `grey_hair, short_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 | 192 | 100.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 192 | 85.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 351 | 154.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 192 | 100.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 351 | 177.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru/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/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1boy, male_focus, solo, tennis_racket, androgynous, smile, otoko_no_ko, outdoors, sportswear, upper_body, blush, grey_eyes, chain-link_fence, day, green_shirt, tree, closed_mouth, looking_at_viewer, sky |
| 1 | 5 |  |  |  |  |  | 1boy, blush, jacket, male_focus, solo, looking_at_viewer, open_mouth, smile |
| 2 | 6 |  |  |  |  |  | 1girl, blush, close-up, hair_between_eyes, looking_at_viewer, solo, open_mouth, sparkle |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | male_focus | solo | tennis_racket | androgynous | smile | otoko_no_ko | outdoors | sportswear | upper_body | blush | grey_eyes | chain-link_fence | day | green_shirt | tree | closed_mouth | looking_at_viewer | sky | jacket | open_mouth | 1girl | close-up | hair_between_eyes | sparkle |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:-------------|:-------|:----------------|:--------------|:--------|:--------------|:-----------|:-------------|:-------------|:--------|:------------|:-------------------|:------|:--------------|:-------|:---------------|:--------------------|:------|:---------|:-------------|:--------|:-----------|:--------------------|:----------|
| 0 | 15 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | X | | | | | X | | | | | | | X | | X | X | | | | |
| 2 | 6 |  |  |  |  |  | | | X | | | | | | | | X | | | | | | | X | | | X | X | X | X | X |
|
CyberHarem/totsuka_saika_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:41:04+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T15:08:01+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Totsuka Saika (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
===========================================================================
This is the dataset of Totsuka Saika (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 192 images and their tags.
The core tags of this character are 'grey\_hair, short\_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"
] |
76c160459e013e3fb5377ba3791b16dcbac9463e
|
# Dataset of Kasumi Nomura
This is the dataset of Kasumi Nomura, 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 | 646 | [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 | 646 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 646 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 646 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/kasumi_nomura_asobiasobase
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:51:09+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:36:13+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Kasumi Nomura
========================
This is the dataset of Kasumi Nomura, 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"
] |
539a59bf5bacc152a6f7b105a754a956186ffc1f
|
# Dataset of Miura Yumiko (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Miura Yumiko (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 190 images and their tags.
The core tags of this character are `long_hair, blonde_hair, green_eyes, drill_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 | 190 | 100.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 190 | 88.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 372 | 167.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 190 | 100.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 372 | 184.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru/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/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru',
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, solo, from_side, profile, bangs, blurry_background, indoors, closed_mouth, holding, open_mouth, reading, school_uniform, upper_body |
| 1 | 7 |  |  |  |  |  | 1girl, solo, earrings, profile, school_uniform, from_side, upper_body, black_jacket, ribbon |
| 2 | 7 |  |  |  |  |  | 1girl, blazer, indoors, school_uniform, solo, sitting, chair, classroom, school_bag, school_desk, black_jacket, collared_shirt, long_sleeves, open_mouth, plaid, red_ribbon, skirt, smartphone, white_shirt |
| 3 | 23 |  |  |  |  |  | 1girl, solo, sobu_high_school_uniform, ribbon, blazer, black_jacket, shirt, upper_body |
| 4 | 5 |  |  |  |  |  | 1girl, crossed_arms, indoors, school_uniform, solo, sweater_vest, earrings, long_sleeves, school_desk, white_shirt, chair, classroom, looking_at_viewer, sitting, closed_mouth, collared_shirt, open_mouth |
| 5 | 5 |  |  |  |  |  | 1girl, cloud, day, earrings, tree, outdoors, school_uniform, solo, blue_sky, crossed_arms, ribbon, anime_coloring, blazer, chain-link_fence, frown, shirt |
| 6 | 6 |  |  |  |  |  | 1girl, collarbone, upper_body, fur_trim, solo, striped_shirt, blurry, orange_jacket, looking_at_viewer |
| 7 | 12 |  |  |  |  |  | 1girl, tennis_racket, tennis_uniform, solo, chain-link_fence, outdoors, skirt |
| 8 | 6 |  |  |  |  |  | 1girl, earrings, off_shoulder, short_sleeves, bare_shoulders, closed_mouth, red_dress, bangs, crossed_arms, breasts, solo |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | from_side | profile | bangs | blurry_background | indoors | closed_mouth | holding | open_mouth | reading | school_uniform | upper_body | earrings | black_jacket | ribbon | blazer | sitting | chair | classroom | school_bag | school_desk | collared_shirt | long_sleeves | plaid | red_ribbon | skirt | smartphone | white_shirt | sobu_high_school_uniform | shirt | crossed_arms | sweater_vest | looking_at_viewer | cloud | day | tree | outdoors | blue_sky | anime_coloring | chain-link_fence | frown | collarbone | fur_trim | striped_shirt | blurry | orange_jacket | tennis_racket | tennis_uniform | off_shoulder | short_sleeves | bare_shoulders | red_dress | breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:------------|:----------|:--------|:--------------------|:----------|:---------------|:----------|:-------------|:----------|:-----------------|:-------------|:-----------|:---------------|:---------|:---------|:----------|:--------|:------------|:-------------|:--------------|:-----------------|:---------------|:--------|:-------------|:--------|:-------------|:--------------|:---------------------------|:--------|:---------------|:---------------|:--------------------|:--------|:------|:-------|:-----------|:-----------|:-----------------|:-------------------|:--------|:-------------|:-----------|:----------------|:---------|:----------------|:----------------|:-----------------|:---------------|:----------------|:-----------------|:------------|:----------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 23 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | 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 | | | | | | | |
| 7 | 12 |  |  |  |  |  | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | X | | | | | | | X | X | | | | | |
| 8 | 6 |  |  |  |  |  | X | X | | | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X |
|
CyberHarem/miura_yumiko_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T17:54:43+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T15:18:50+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Miura Yumiko (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
==========================================================================
This is the dataset of Miura Yumiko (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 190 images and their tags.
The core tags of this character are 'long\_hair, blonde\_hair, green\_eyes, drill\_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"
] |
54d044642535b5ac4572f72be925d4e95bcb6847
|
# Dataset Card for "Metallography_segmenter_Dataset_B1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/Metallography_segmenter_Dataset_B1
|
[
"region:us"
] |
2023-09-13T18:06:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 84529692.0, "num_examples": 410}, {"name": "validation", "num_bytes": 21840002.0, "num_examples": 103}], "download_size": 106032508, "dataset_size": 106369694.0}}
|
2023-09-13T18:11:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Metallography_segmenter_Dataset_B1"
More Information needed
|
[
"# Dataset Card for \"Metallography_segmenter_Dataset_B1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Metallography_segmenter_Dataset_B1\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Metallography_segmenter_Dataset_B1\"\n\nMore Information needed"
] |
f5c64edb18e4d3fd1a6d67fa394ba1a2d62d7efc
|
# Dataset Card for "ata_guidelines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bvand086/ata_guidelines
|
[
"region:us"
] |
2023-09-13T18:06:28+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 803456, "num_examples": 847}], "download_size": 435112, "dataset_size": 803456}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T18:21:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "ata_guidelines"
More Information needed
|
[
"# Dataset Card for \"ata_guidelines\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"ata_guidelines\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"ata_guidelines\"\n\nMore Information needed"
] |
801723befaeb8f109bb211cddeaaa3ce5361d911
|
# Dataset Card for Evaluation run of speechlessai/speechless-codellama-34b-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/speechlessai/speechless-codellama-34b-v1.0
- **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 [speechlessai/speechless-codellama-34b-v1.0](https://huggingface.co/speechlessai/speechless-codellama-34b-v1.0) 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_speechlessai__speechless-codellama-34b-v1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T10:43:00.589616](https://huggingface.co/datasets/open-llm-leaderboard/details_speechlessai__speechless-codellama-34b-v1.0/blob/main/results_2023-10-29T10-43-00.589616.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": 0.37080536912751677,
"em_stderr": 0.004946581424326503,
"f1": 0.42342072147651116,
"f1_stderr": 0.004815729646559334,
"acc": 0.439759976974257,
"acc_stderr": 0.011098891058626454
},
"harness|drop|3": {
"em": 0.37080536912751677,
"em_stderr": 0.004946581424326503,
"f1": 0.42342072147651116,
"f1_stderr": 0.004815729646559334
},
"harness|gsm8k|5": {
"acc": 0.1470811220621683,
"acc_stderr": 0.0097560636603599
},
"harness|winogrande|5": {
"acc": 0.7324388318863457,
"acc_stderr": 0.012441718456893009
}
}
```
### 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_speechlessai__speechless-codellama-34b-v1.0
|
[
"region:us"
] |
2023-09-13T18:10:07+00:00
|
{"pretty_name": "Evaluation run of speechlessai/speechless-codellama-34b-v1.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [speechlessai/speechless-codellama-34b-v1.0](https://huggingface.co/speechlessai/speechless-codellama-34b-v1.0) 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_speechlessai__speechless-codellama-34b-v1.0\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T10:43:00.589616](https://huggingface.co/datasets/open-llm-leaderboard/details_speechlessai__speechless-codellama-34b-v1.0/blob/main/results_2023-10-29T10-43-00.589616.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\": 0.37080536912751677,\n \"em_stderr\": 0.004946581424326503,\n \"f1\": 0.42342072147651116,\n \"f1_stderr\": 0.004815729646559334,\n \"acc\": 0.439759976974257,\n \"acc_stderr\": 0.011098891058626454\n },\n \"harness|drop|3\": {\n \"em\": 0.37080536912751677,\n \"em_stderr\": 0.004946581424326503,\n \"f1\": 0.42342072147651116,\n \"f1_stderr\": 0.004815729646559334\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1470811220621683,\n \"acc_stderr\": 0.0097560636603599\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7324388318863457,\n \"acc_stderr\": 0.012441718456893009\n }\n}\n```", "repo_url": "https://huggingface.co/speechlessai/speechless-codellama-34b-v1.0", "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_13T19_09_51.319301", "path": ["**/details_harness|arc:challenge|25_2023-09-13T19-09-51.319301.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T19-09-51.319301.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T10_43_00.589616", "path": ["**/details_harness|drop|3_2023-10-29T10-43-00.589616.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T10-43-00.589616.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T10_43_00.589616", "path": ["**/details_harness|gsm8k|5_2023-10-29T10-43-00.589616.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T10-43-00.589616.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T19_09_51.319301", "path": ["**/details_harness|hellaswag|10_2023-09-13T19-09-51.319301.parquet"]}, {"split": "latest", "path": 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|
2023-10-29T10:43:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of speechlessai/speechless-codellama-34b-v1.0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model speechlessai/speechless-codellama-34b-v1.0 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 2023-10-29T10:43:00.589616(note that their might be results for other tasks in 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 speechlessai/speechless-codellama-34b-v1.0",
"## 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 speechlessai/speechless-codellama-34b-v1.0 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 2023-10-29T10:43:00.589616(note that their might be results for other tasks in 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 speechlessai/speechless-codellama-34b-v1.0",
"## 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 speechlessai/speechless-codellama-34b-v1.0 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 2023-10-29T10:43:00.589616(note that their might be results for other tasks in 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",
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"## Dataset Structure",
"### Data Instances",
"### Data Fields",
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"### Curation Rationale",
"### Source Data",
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"#### 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 speechlessai/speechless-codellama-34b-v1.0## 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 speechlessai/speechless-codellama-34b-v1.0 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 2023-10-29T10:43:00.589616(note that their might be results for other tasks in 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"
] |
90c07c3704cdf72bb7b1562e74067ef91f08d720
|
# Dataset of Kawasaki Saki (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
This is the dataset of Kawasaki Saki (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 128 images and their tags.
The core tags of this character are `ponytail, long_hair, blue_hair, mole, mole_under_eye, purple_eyes, scrunchie`, which are 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 | 128 | 69.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 128 | 58.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 265 | 117.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 128 | 69.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 265 | 135.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru/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/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru',
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, bartender, solo, closed_eyes, grey_hair, vest, formal |
| 1 | 20 |  |  |  |  |  | 1girl, solo, anime_coloring, white_shirt, open_mouth, looking_at_viewer, smile |
| 2 | 30 |  |  |  |  |  | 1girl, sobu_high_school_uniform, solo, blazer, shirt, black_jacket |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bartender | solo | closed_eyes | grey_hair | vest | formal | anime_coloring | white_shirt | open_mouth | looking_at_viewer | smile | sobu_high_school_uniform | blazer | shirt | black_jacket |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:-------|:--------------|:------------|:-------|:---------|:-----------------|:--------------|:-------------|:--------------------|:--------|:---------------------------|:---------|:--------|:---------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | | | |
| 1 | 20 |  |  |  |  |  | X | | X | | | | | X | X | X | X | X | | | | |
| 2 | 30 |  |  |  |  |  | X | | X | | | | | | | | | | X | X | X | X |
|
CyberHarem/kawasaki_saki_yahariorenoseishunlovecomewamachigatteiru
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T18:11:23+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-29T15:26:38+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Kawasaki Saki (Yahari Ore no Seishun LoveCome wa Machigatte Iru)
===========================================================================
This is the dataset of Kawasaki Saki (Yahari Ore no Seishun LoveCome wa Machigatte Iru), containing 128 images and their tags.
The core tags of this character are 'ponytail, long\_hair, blue\_hair, mole, mole\_under\_eye, purple\_eyes, scrunchie', which are 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"
] |
938c45475c979593cf3b2e47c059f4e9d0d18c16
|
# Dataset Card for "processed_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
esantiago/processed_demo
|
[
"region:us"
] |
2023-09-13T18:14:16+00:00
|
{"dataset_info": {"features": [{"name": "pokemon", "dtype": "int64"}, {"name": "type", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 240, "num_examples": 15}], "download_size": 1469, "dataset_size": 240}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T18:14:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "processed_demo"
More Information needed
|
[
"# Dataset Card for \"processed_demo\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_demo\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_demo\"\n\nMore Information needed"
] |
2f66c2a31e7396eb3ca4d241d4724ad97d885e57
|
# Dataset Card for "DreamEditBench_SelfContained"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tianleliphoebe/DreamEditBench_SelfContained
|
[
"region:us"
] |
2023-09-13T18:21:50+00:00
|
{"dataset_info": {"features": [{"name": "subject_names", "dtype": "string"}, {"name": "subject_images_1", "dtype": "image"}, {"name": "subject_images_2", "dtype": "image"}, {"name": "subject_images_3", "dtype": "image"}, {"name": "subject_images_4", "dtype": "image"}, {"name": "source_images", "dtype": "image"}, {"name": "identifier", "dtype": "string"}, {"name": "source_prompt", "dtype": "string"}, {"name": "target_prompt", "dtype": "string"}, {"name": "add_bounding_box", "sequence": "int64"}, {"name": "task_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 264628852.0, "num_examples": 600}], "download_size": 102220339, "dataset_size": 264628852.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-15T14:55:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DreamEditBench_SelfContained"
More Information needed
|
[
"# Dataset Card for \"DreamEditBench_SelfContained\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DreamEditBench_SelfContained\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DreamEditBench_SelfContained\"\n\nMore Information needed"
] |
9674a1951229b164087f4a415e6132293d222ec0
|
# Dataset of honda_mio/本田未央 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of honda_mio/本田未央 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes, breasts, 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 | 500 | 563.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/honda_mio_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 357.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/honda_mio_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1126 | 716.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/honda_mio_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 507.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/honda_mio_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1126 | 965.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/honda_mio_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/honda_mio_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, hooded_track_jacket, school_uniform, solo, open_mouth, skirt, looking_at_viewer, hoodie, one_eye_closed, ;d, :d |
| 1 | 5 |  |  |  |  |  | 1girl, blush, collared_shirt, dress_shirt, school_uniform, solo, white_shirt, yellow_bowtie, hooded_track_jacket, looking_at_viewer, open_mouth, yellow_eyes, :d, bangs, upper_body, hood_down, hooded_jacket, long_sleeves, simple_background, wing_collar, yellow_background |
| 2 | 18 |  |  |  |  |  | 1girl, looking_at_viewer, solo, smile, blush, navel, collarbone, yellow_eyes, bangs, simple_background, cleavage, side-tie_bikini_bottom, white_bikini, open_mouth, white_background |
| 3 | 17 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, smile, navel, solo, blush, necklace, one_eye_closed, orange_bikini, open_mouth, ;d, heart, bracelet |
| 4 | 8 |  |  |  |  |  | 1girl, nipples, solo, looking_at_viewer, navel, nude, blush, open_mouth, pussy, smile, one_eye_closed, uncensored, ;d |
| 5 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, open_mouth, solo, blush, simple_background, white_background, :d, bangs, yellow_eyes, index_finger_raised, red_dress |
| 6 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, cleavage, hairband, smile, belt, crop_top, midriff, navel, one_eye_closed, open_mouth, star_earrings, bare_shoulders, orange_skirt, striped_thighhighs, yellow_eyes |
| 7 | 5 |  |  |  |  |  | 1girl, open_mouth, smile, solo, star_(symbol), thighhighs, ;d, dress, earrings, one_eye_closed, blush, choker, hairband, hand_on_hip, idol, looking_at_viewer, white_gloves |
| 8 | 5 |  |  |  |  |  | 1girl, open_mouth, smile, solo, ;d, one_eye_closed, dress, looking_at_viewer, boots, frills, full_body, hat, high_heels, skirt |
| 9 | 7 |  |  |  |  |  | 1boy, 1girl, blush, girl_on_top, hetero, nipples, sex, solo_focus, vaginal, open_mouth, penis, smile, completely_nude, looking_at_viewer, mosaic_censoring, navel, pussy, spread_legs, collarbone, female_pubic_hair, pov, squatting_cowgirl_position, sweat |
| 10 | 9 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, solo, blush, earrings, hairclip, smile, star_(symbol), black_pantyhose, open_clothes, simple_background, white_background, bangs, coat, open_mouth, ribbed_sweater, turtleneck_sweater, yellow_eyes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hooded_track_jacket | school_uniform | solo | open_mouth | skirt | looking_at_viewer | hoodie | one_eye_closed | ;d | :d | blush | collared_shirt | dress_shirt | white_shirt | yellow_bowtie | yellow_eyes | bangs | upper_body | hood_down | hooded_jacket | long_sleeves | simple_background | wing_collar | yellow_background | smile | navel | collarbone | cleavage | side-tie_bikini_bottom | white_bikini | white_background | necklace | orange_bikini | heart | bracelet | nipples | nude | pussy | uncensored | index_finger_raised | red_dress | hairband | belt | crop_top | midriff | star_earrings | bare_shoulders | orange_skirt | striped_thighhighs | star_(symbol) | thighhighs | dress | earrings | choker | hand_on_hip | idol | white_gloves | boots | frills | full_body | hat | high_heels | 1boy | girl_on_top | hetero | sex | solo_focus | vaginal | penis | completely_nude | mosaic_censoring | spread_legs | female_pubic_hair | pov | squatting_cowgirl_position | sweat | hairclip | black_pantyhose | open_clothes | coat | ribbed_sweater | turtleneck_sweater |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:----------------------|:-----------------|:-------|:-------------|:--------|:--------------------|:---------|:-----------------|:-----|:-----|:--------|:-----------------|:--------------|:--------------|:----------------|:--------------|:--------|:-------------|:------------|:----------------|:---------------|:--------------------|:--------------|:--------------------|:--------|:--------|:-------------|:-----------|:-------------------------|:---------------|:-------------------|:-----------|:----------------|:--------|:-----------|:----------|:-------|:--------|:-------------|:----------------------|:------------|:-----------|:-------|:-----------|:----------|:----------------|:-----------------|:---------------|:---------------------|:----------------|:-------------|:--------|:-----------|:---------|:--------------|:-------|:---------------|:--------|:---------|:------------|:------|:-------------|:-------|:--------------|:---------|:------|:-------------|:----------|:--------|:------------------|:-------------------|:--------------|:--------------------|:------|:-----------------------------|:--------|:-----------|:------------------|:---------------|:-------|:-----------------|:---------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 18 |  |  |  |  |  | X | | | 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 | 8 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | X | X | X | X | | X | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 9 | 7 |  |  |  |  |  | X | | | | X | | X | | | | | X | | | | | | | | | | | | | | X | X | X | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 10 | 9 |  |  |  |  |  | X | | | X | X | | X | | | | | X | | | | | X | X | | | | X | X | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
|
CyberHarem/honda_mio_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T18:23:39+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T11:35:29+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of honda\_mio/本田未央 (THE iDOLM@STER: Cinderella Girls)
=============================================================
This is the dataset of honda\_mio/本田未央 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'brown\_hair, short\_hair, brown\_eyes, breasts, 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"
] |
83460a4ac5567b780f5aa37980d9022eec4d4a1e
|
# DSTC11: Dialogue System Technology Challenge 11<br/><br/>[Track 4: Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems](https://github.com/Mario-RC/dstc11_track4_robust_multilingual_metrics)
# Directory Structure Scheme
Representation of the directory tree structure:
```
.
└── DSTC_11_Track_4 # DSTC11 data
├── task1 # Metrics for Multilingual Data data
│ ├── train # Train data (CHANEL/CDIAL datasets)
│ │ ├── en_es # English/Spanish data
│ │ ├── en_zh # English/Chinese data
│ │ └── zh_en # Chinese/English data
│ ├── dev # Dev data (DSTC10.T5/CDIAL datasets)
│ │ ├── en_es # English/Spanish data
│ │ └── en_zh # English/Chinese data
│ │ └── zh_en # Chinese/English data
│ ├── test # Test data (DSTC10.T5/CDIAL datasets)
│ │ ├── en_es # English/Spanish data
│ │ └── en_zh # English/Chinese data
│ │ └── zh_en # Chinese/English data
│ └── README.md # Task 1 data information
├── task2 # Robust Metrics data
│ ├── train # Train data (CHANEL datasets)
│ ├── dev # Development data (DSTC10.T5 datasets)
│ ├── test # Test data (DSTC10.T5 datasets)
│ └── README.md # Task 2 data information
├── metadata # Auxiliary datasets annotations
└── README.md # General data information
```
# Track Overview
This track consists of two tasks which are explained in more detail below:
Participants will develop effective automatic open-ended and multilingual dialogue evaluation metrics that perform similarly when evaluated over a new language.
Participants will develop effective automatic open-ended dialogue evaluation metrics that perform robustly when evaluated over back-translated/paraphrased sentences in English.
For both tasks, proposed metrics are expected to show the following two important properties as indicated in (Deriu et al., 2019):
Correlated to human judgments - the metrics should produce evaluation scores that well correlate to human judgments (scores) across multiple languages or alternative responses (i.e., back-translated or paraphrased).
Explainable - the metrics should provide constructive and explicit feedback to the generative models in terms of the quality of their generated responses. For instance, if a generative model is contradicting itself, the evaluation metrics should signal such behavior to the generative models.
Participants can propose their own metric or optionally improve two baseline evaluation metrics: MDD-Eval (Zhang et al, 2021) or Deep AM-FM (Zhang et al, 2020). A leaderboard in the ChatEval platform will be provided allowing participants to check their progress.
For each evaluation task, Spearman correlation will be computed to compare the proposed evaluation metrics against human judgments. A final average score will be calculated to rank the submitted evaluation metrics.
For more details:
* [Provided datasets](./dstc11/track4-provided-datasets.md)
* [Datasets format](./dstc11/track4-datasets-format.md)
* [Task 1: Multilingual Automatic Evaluation Metrics](./dstc11/track4-task1-multilingual-metrics.md)
* [Task 2: Robust Automatic Evaluation Metrics](./dstc11/track4-task2-robust-metrics.md)
* [Baseline model](./dstc11/track4-baseline-model.md)
* [FAQ](./dstc11/track4-faq.md)
For more information check the [ChatEval](https://chateval.org/dstc11) website.
# Provided Datasets
After the organizers' participation in the [CHANEL@JSALT2020](https://github.com/CHANEL-JSALT-2020/datasets) workshop (Rudnicky et al., 2020) at John Hopkins University, they have automatically translated back-and-forth (using the same MS Azure translation service) a total of 18 well-known human-human dialogue datasets. These data sets will be used as training data. The total amount of dialogues is 393k (approx. 3M turns).
* DBDC (Higashinaka et al., 2016)
* CMU_DoG (Zhou et al., 2018)
* Cornell Movie-Dialogs (Danescu-Niculescu-Mizil & Lee, 2011)
* DailyDialog (Li et al., 2017)
* DECODE (Nie et al., 2020)
* EmotionLines (Chen et al., 2018)
* EmpathicDialogues (Rashkin et al., 2018)
* Holl-E (Moghe et al., 2018)
* MEENA (Adiwardana et al., 2020)
* MELD (Poria et al., 2019)
* MetalWOz (Lee et al., 2019)
* Movie-DiC (Banchs, 2012)
* PersonaChat (Zhang et al., 2018)
* SentimentLIAR (Upadhayay & Behzadan, 2020)
* Switchboard Coherence (Cervone & Riccardi, 2020)
* Topical-Chat (Gopalakrishnan et al., 2019)
* Wizard of Wikipedia (Dinan et al., 2019)
* Wochat (D'Haro et al., 2016)
As development set, organizers will provide the following datasets identified during the [DSTC10 Track 5](https://chateval.org/dstc10) (Zhang et al, 2021), that sum up more than 35k turn-level human-annotations, which have been automatically translated to Spanish and Chinese, and back-translated both to English using [MS Azure](https://azure.microsoft.com/en-us/products/cognitive-services/translator/) services.
* CONVAI2-GRADE (CG)</b> (Huang et al., 2020)
* DAILYDIALOG-GRADE (DH)</b> (Huang et al., 2020)
* DAILYDIALOG-GUPTA (DG)</b> (Gupta et al., 2019)
* DAILYDIALOG-ZHAO (DZ)</b> (Zhao et al., 2020)
* DSTC7 (D7)</b> (Galley et al., 2019)
* EMPATHETIC-GRADE (EG)</b> (Huang et al., 2020)
* FED-DIAL (FD)</b> (Mehri & Eskenazi, 2020b)
* FED-TURN (FT)</b> (Mehri & Eskenazi, 2020b)
* HUMOD (HM)</b> (Merdivan et al., 2020)
* PERSONA-SEE (PS)</b> (See et al., 2019)
* PERSONA-USR (PU)</b> (Mehri & Eskenazi, 2020a)
* PERSONA-ZHAO (PZ)</b> (Zhao et al., 2020)
* TOPICAL-USR (TU)</b> (Mehri & Eskenazi, 2020a)
This development data can help participants to check the multilingualism or robustness capabilities of their trained models in terms of correlations with human-annotations. Additional databases, not mentioned here, will be added when available to increase the size of the benchmarking.
Moreover, the datasets provided by [THU-COAI](https://github.com/thu-coai) group (Conversational AI groups from Tsinghua University) will be used, naming this set of data CDial. They contain open domain human-human dialogs. They are originally in Chinese and contain of 3,470 dialogs (approx. 130k turns).
* ECM (Zhou et al., 2018)
* KdConv (Zhou et al., 2020)
* LCCC (Wang et al., 2020)
In addition, we will provide the same datasets translated (CHANEL@JSALT2020 and CDial) into Chinese using the SotA [Tencent MT](https://www.tencentcloud.com/products/tmt) system.
These datasets will be provided to participants, together with automatic meta-data information (machine translation Quality Estimation (QE), toxicity, and sentiment analysis) for filtering and dialogue curation purposes, so the participants have a better reference of the dataset quality, being of great help for them to decide whether or not to use these translations/paraphrases in the training of their evaluation models, and optionally fine-tune multilingual pre-trained models allowing better performance on the proposed dialogue-oriented tasks.
Since the quality of the back-translated sentences can play an important role in estimating the metric scores. QE metric scores will be given to the participants using our QE system and other existing models (e.g., [COMET](https://github.com/Unbabel/COMET) (Rei et al., 2020)). This information will be given to participants so they can optionally use it for discarding dialogues or turns that do not show high quality when training their metrics. Participants will be welcome to use the data and ideas from the MT field to propose QE metrics that can, optionally, be included to provide final scores. Finally, the organizers may provide new translated dialogue datasets to allow participants to create more robust and better-trained systems.
Regarding the paraphrases, all the original English sentences of each dataset will have multiple paraphrases, as well as annotations so that each participant can evaluate the quality of each paraphrase. The model used will be [PARROT](https://github.com/jsedoc/Parrot_Paraphraser) (Damodaran P., 2021).
Additionally, \~3k random H-H turns (\~1k dialogues) of CDial in Chinese were manually annotated by Tencent AI. Also, \~5k new H-C Chinese turns (\~500 dialogues) were generated with three different SotA chatbots (Tencent's model, Microsoft's Xiaoice (Zhou et al., 2020) and Baidu's Plato (Bao et al., 2019)). Both turn-level and dialog-level annotations were manually annotated by Tencent AI.
During the test phase, a new set of 2k turn-level (\~700 dialog-level) manually curated multilingual corpus (Spanish and Chinese) along with their turn-level and dialog-level human evaluation annotations will be provided to participants to test models for both tasks. This corpus will be manually checked to guarantee its quality and high correlation with the original dialogues.
Furthermore, in order to check the generalization capabilities of the proposed metrics from the participant, the test data will include a new dataset of human-chatbot interactions with \~2k turns (~60 dialogues).
## Datasets Summary
| Datasets<br/>Name | CHANEL | DSTC10 | CDIAL |
| --- | :---: | :----: | :---: |
| # Datsets | 18 | 7 | 3 |
| Language | English, Spanish/Chinese translations,<br/>and English back-translation | English, Spanish/Chinese translations,<br/>and English back-translation | Chinese and English translations |
| Dialogues Type | Human-Human Open-Domain | Human-Chatbot Open-Domain | Human-Human Open-Domain
| # Dialogues/<br/>Utterances | + 390.000 / + 3.000.000 | + 3.000 / + 60.000 | + 3.470 / +130.000
| Annotations | Sentiment analysis and Toxicity | Sentiment analysis and Toxicity<br/>Turn/dialgue level human scores | Turn/dialgue level human scores
| Task 1 Set | Train | Dev, Test | Train, Dev, Test
| Task 2 Set | Train | Dev, Test | —
## Datasets Statistics
| Name | #Turns | #Dialogues | Average Turn/Dial | Average Words/Turn | Annotation Granularity | Original Language | Translation |
| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **Train** | | | | | | | |
DBDC (Higashinaka et al., 2016) | 8,509 | 415 | 20.5 | 7.31 | Turn | En | Zh/Es |
CMU_DoG (Zhou et al., 2018c) | 95,305 | 4,221 | 22.58 | 17.93 | Turn | En | Zh/Es |
Cornell Movie-Dialogs (Danescu-Niculescu-Mizil and Lee, 2011) | 304,713 | 83,097 | 3.67 | 13.72 | Turn | En | Zh/Es |
DailyDialog (Li et al., 2017) | 102,960 | 13,116 | 7.85 | 13.96 | Turn | En | Zh/Es |
DECODE (Nie et al., 2020) | 296,105 | 35,426 | 8.36 | 15.05 | Turn | En | Zh/Es |
EmotionLines (Hsu et al., 2018) | 14,503 | 1,000 | 14.50 | 10.53 | Turn | En | Zh/Es |
EmpathicDialogues (Rashkin et al., 2019) | 107,220 | 24,850 | 4.31 | 15.88 | Turn | En | Zh/Es |
Holl-E (Moghe et al., 2018) | 91,452 | 9,071 | 10.08 | 17.74 | Turn | En | Zh/Es |
MEENA (Adiwardana et al., 2020) | 3,675 | 193 | 19.04 | 9.14 | Turn | En | Zh/Es |
MELD (Poria et al., 2019) | 23,197 | 1,592 | 14.57 | 10.98 | Turn | En | Zh/Es |
MetalWOz (Lee et al., 2019) | 432,036 | 37,884 | 11.40 | 8.47 | Turn | En | Zh/Es |
Movie-DiC (Banchs, 2012) | 512,582 | 65,215 | 7.86 | 13.82 | Turn | En | Zh/Es |
PersonaChat (Zhang et al., 2018a) | 162,064 | 10,907 | 14.86 | 11.72 | Turn | En | Zh/Es |
SentimentLIAR (Upadhayay and Behzadan, 2020) | 12,781 | 12,781 | 1.00 | 20.16 | Turn | En | Zh/Es |
Switchboard Coherence (Cervone and Riccardi, 2020) | 12,059 | 1,000 | 12.06 | 20.55 | Turn | En | Zh/Es |
Topical-Chat (Gopalakrishnan et al., 2019) | 235,281 | 10,784 | 21.82 | 23.23 | Turn | En | Zh/Es |
Wizard of Wikipedia (Dinan et al., 2019) | 201,999 | 22,311 | 9.05 | 18.83 | Turn | En | Zh/Es |
Wochat (Haro et al., 2016) | 19,881 | 607 | 32.75 | 6.75 | Turn | En | Zh/Es |
| --- | --- | --- | --- | --- | --- | --- | --- |
Total | 2,636,322 | 334,470 | 236.26 | 255.77 | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **Development** | | | | | | | |
ConvAI2-GRADE (Huang et al., 2020) | 1,800 | 600 | 3.0 | 12.07 | Turn | En | Zh/Es |
DailyDialog-GRADE (Huang et al., 2020) | 900 | 300 | 3.0 | 12.60 | Turn | En | Zh/Es |
DailyDialog-GUPTA (Gupta et al., 2019) | 2,460 | 500 | 4.92 | 12.37 | Turn | En | Zh/Es |
DailyDialog-ZHAO (Zhao et al., 2020) | 4,248 | 900 | 4.72 | 12.41 | Turn | En | Zh/Es |
DSTC7 (Galley et al., 2019) | 34,650 | 9,990 | 3.47 | 15.39 | Turn | En | Zh/Es |
Empathetic-GRADE (Huang et al., 2020) | 900 | 300 | 3.0 | 16.65 | Turn | En | Zh/Es |
FED-Dial (Mehri and Eskenazi, 2020a)) | 1,715 | 125 | 13.72 | 11.1 | Dial | En | Zh/Es |
FED-Turn (Mehri and Eskenazi, 2020a)) | 3,888 | 375 | 10.37 | 10.78 | Turn | En | Zh/Es |
HUMOD (Merdivan et al., 2020) | 37,468 | 9,499 | 3.94 | 7.97 | Turn | En | Zh/Es |
Persona-SEE (See et al., 2019) | 39,792 | 3,316 | 12.0 | 9.0 | Dial | En | Zh/Es |
PersonaChat-USR (Mehri and Eskenazi, 2020b) | 2,790 | 300 | 9.3 | 12.08 | Turn | En | Zh/Es |
PersonaChat-ZHAO (Zhao et al., 2020) | 4,614 | 900 | 5.13 | 12.06 | Turn | En | Zh/Es |
TOPICAL-USR (Mehri and Eskenazi, 2020b) | 4,032 | 360 | 11.2 | 23.16 | Turn | En | Zh/Es |
ECM-Eval (Zhou et al., 2018a) | 3,004 | 1,502 | 2.0 | 13.13 | Turn | Zh | En |
KdConv-Eval (Zhou et al., 2020a) | 3,499 | 354 | 9.88 | 21.11 | Turn | Zh | En |
LCCC-Eval (Wang et al., 2020a) | 3,009 | 589 | 5.11 | 11.72 | Turn | Zh | En |
| --- | --- | --- | --- | --- | --- | --- | --- |
Total | 148,769 | 29,910 | 104.76 | 212.64 | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **Test** | | | | | | | |
BlenderBot3 (Giorgi et al., 2023; Shuster et al., 2022) | 679 | 21 | 32.33 | 16.96 | Turn/Dial | En | Zh/Es |
ChatGPT (Giorgi et al., 2023; Radford et al., 2018) | 462 | 21 | 22 | 91.07 | Turn/Dial | En | Zh/Es |
GPT-3.5 (Giorgi et al., 2023; Brown et al., 2020) | 560 | 17 | 32.94 | 23.73 | Turn/Dial | En | Zh/Es |
HCChinese | 2,017 | 187 | 10.79 | 8.08 | Turn/Dial | Zh | En |
ChatEval (Sedoc et al., 2019) | 400 | 200 | 2 | 8.13 | Turn | En | Zh/Es |
DSTC10 (Zhang et al., 2022c) | 112 | 28 | 4 | 14 | Turn | En | Zh/Es |
JSALT (Rudnicky et al., 2020) | 46 | 13 | 3.54 | 17.26 | Turn | En | Zh/Es |
| --- | --- | --- | --- | --- | --- | --- | --- |
Total | 4,276 | 487 | 107.60 | 179.23 | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
## Datasets Information
CHANEL dataset. The source language is English.
| CHANEL | Spanish<br/>Translation | Chinese<br/>Translation | English<br/>Translation | English<br/>Back-translation | Paraphrases | Sentiment<br/>Analysis | Content<br/>Moderate | Human<br/>Annotations | Annotation<br/>Granularity |
| --- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| DBDC | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| CMU_DoG | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| Cornell Movie-Dialogs | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| DailyDialog | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| DECODE | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| EmotionLines | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| EmpathicDialogues | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| Holl-E | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| MEENA | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| MELD | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| MetalWOz | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| Movie-DiC | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| PersonaChat | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| SentimentLIAR | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| Switchboard Coherence | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| Topical-Chat | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| Wizard of Wikipedia | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
| WOCHAT | ✔ | | | ✔ | ✔ | ✔ | ✔ | | Turn-level |
DSTC10 dataset. The source language is English.
| DSTC10 | Spanish<br/>Translation | Chinese<br/>Translation | English<br/>Translation | English<br/>Back-translation | Paraphrases | Sentiment<br/>Analysis | Content<br/>Moderate | Human<br/>Annotations | Annotation<br/>Granularity |
| --- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| CONVAI2-GRADE (CG) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| DAILYDIALOG-GRADE (DH) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| DAILYDIALOG-GUPTA (DG) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| DAILYDIALOG-ZHAO (DZ) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| DSTC7 (D7) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| EMPATHETIC-GRADE (EG) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| FED-DIAL (FD) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Dialogue-level |
| FED-TURN (FT) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| HUMOD (HU) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| PERSONA-SEE (PS) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Dialogue-level |
| PERSONA-USR (PU) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| PERSONA-ZHAO (PZ) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
| TOPICAL-USR (TU) | ✔ | ✔ | | ✔ | ✔ | ✔ | ✔ | ✔ | Turn-level |
CDIAL dataset. The source language is Chinese.
| CDIAL | Spanish<br/>Translation | Chinese<br/>Translation | English<br/>Translation | English<br/>Back-translation | Paraphrases | Sentiment<br/>Analysis | Content<br/>Moderate | Human<br/>Annotations |
| --- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| ECM | | | ✔ | | | | | ✔ |
| KDCONV | | | ✔ | | | | | ✔ |
| LCCC | | | ✔ | | | | | ✔ |
## Data Format
All data given follows the [Data Formats](./dstc11/track4-datasets-format.md) which provides guidelines on how to store, maintain and handle dialogue corpora.
## Dimensions Evaluation
Considering the annotations available in the development data, the test data will have the following dimensions (annotations) to evaluate in both Task 1 (English, Chinese and Spanish) and Task 2:
* **Turn-level**: Appropriateness, Content Richness, Grammatical Correctness and Relevance
* **Dialogue-level**: Coherence, Engageness/Likeability, Informativeness and Overall.
The annotations will be evaluated and indicated individually (dimension by dimension), discriminating by dataset and language. In addition, a global score will be estimated by grouping all dimensions. This global value will be calculated separately at turn-level and dialogue-level for each task.
A brief description of each dimension (Mehri et al., 2022) is shown below.
Turn-level:
* **Appropriateness** - The response is appropriate given the preceding dialogue.
* **Content Richness** - The response is informative, with long sentences including multiple entities and conceptual or emotional words.
* **Grammatical Correctness** - Responses are free of grammatical and semantic errors.
* **Relevance** - Responses are on-topic with the immediate dialog history.
Dialogue-level:
* **Coherence** - Throughout the dialog, is the system maintaining a good conversation flow.
* **Engageness/Likeability** - Throughout the dialogue, the system displays a likeable personality.
* **Informativeness** - Throughout the dialog, the system provides unique and non-generic information.
* **Overall** - The overall quality of and satisfaction with the dialog.
# Organizers
* Mario Rodríguez-Cantelar (Universidad Politécnica de Madrid, Spain)
* Chen Zhang (National University of Singapore, Singapore)
* Chengguang Tang (Tencent AI Lab, China)
* Ke Shi (Tencent AI Lab, China)
* Sarik Ghazarian (University of Southern California, USA)
* João Sedoc (New York University, USA)
* Luis F. D'Haro (Universidad Politécnica de Madrid, Spain)
* Alexander Rudnicky (Carnegie Mellon University, USA)
# Citation
Please cite the paper, code or data from DSTC 11 Track 4:
```bibtex
@inproceedings{rodriguezcantelar2023dstc11t4,
author = "Mario Rodríguez-Cantelar and Chen Zhang and Chengguang Tang and Ke Shi and Sarik Ghazarian and João Sedoc and Luis Fernando D'Haro and Alexander Rudnicky",
title = "Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4",
booktitle = "DSTC11: The Eleventh Dialog System Technology Challenge",
series = "24th Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)",
year = 2023,
month = "September",
address = "Prague, Czechia"
}
```
# Acknowledgement
This research project is supported by the Comunidad de Madrid through the call Research Grants for Young Investigators from Universidad Politécnica de Madrid (GENIUS:APOYO-JOVENES-21-TAXTYC-32-K61X37).
This work is supported by project BEWORD (PID2021-126061OB-C43) funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union”, and by Programa Propio - Proyectos Semilla: Universidad Politécnica de Madrid (VSEMILLA22LFHE).
We gratefully acknowledge valuable efforts from Tencent AI Lab who supports Chinese translation and annotation of datasets by funding and infrastructure.
Thanks to THU-CoAI (Conversational AI groups from Tsinghua University) for providing their Chinese datasets as part of the challenge data.
Thanks to Unbabel for providing the COMET MTQE scores annotations as part of the challenge data. This contribution was supported by national funds through *Fundação para a Ciência e a Tecnologia* (FCT) with references PRT/BD/152198/2021 and UIDB/50021/2020, and by the P2020 program MAIA led by Unbabel (LISBOA-01-0247-FEDER-045909).
We also want to give thanks to MS Azure services (especially to Irving Kwong) for their sponsorship to continue processing new datasets that could be interesting for the dialogue community.
This research project is supported by the NYU ChatEval Team led by João Sedoc.
This research project is supported in part by a grant from Amazon to Alexander Rudnicky, Carnegie Mellon University.
Thanks to Karthik Ganesan, Sarik Ghazarian, James Hagerty, Zhang Chen and Alex Rudnicky for developing the baseline model as part of the challenge tasks.
This work is supported by the European Commission through Project ASTOUND (101071191 — HORIZON-EIC-2021-PATHFINDERCHALLENGES-01).
# References
Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2020). Survey on evaluation methods for dialogue systems. Artificial Intelligence Review, 1-56.
Zhang, C., D'Haro, L. F., Friedrichs, T., & Li, H. (2021). MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation. arXiv preprint arXiv:2112.07194.
Zhang, C., D'Haro, L. F., Banchs, R. E., Friedrichs, T., & Li, H. (2020). Deep AM-FM: Toolkit for Automatic Dialogue Evaluation. In Conversational Dialogue Systems for the Next Decade (pp. 53-69). Springer, Singapore.
Zhang, C., Sadoc, J., D'Haro, L. F., Banchs, R., & Rudnicky, A. (2021). Automatic Evaluation and Moderation of Open-domain Dialogue Systems. arXiv preprint arXiv:2111.02110.
Hori, C., & Hori, T. (2017). End-to-end conversation modeling track in DSTC6. arXiv preprint arXiv:1706.07440.
Galley, M., Brockett, C., Gao, X., Gao, J., & Dolan, B. (2019). Grounded response generation task at dstc7. In AAAI Dialog System Technology Challenges Workshop.
See, A., Roller, S., Kiela, D., & Weston, J. (2019). What makes a good conversation? how controllable attributes affect human judgments. arXiv preprint arXiv:1902.08654.
Sedoc, J., Ippolito, D., Kirubarajan, A., Thirani, J., Ungar, L., & Callison-Burch, C. (2019, June). Chateval: A tool for chatbot evaluation. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics (demonstrations) (pp. 60-65).
Vinyals, O., & Le, Q. (2015). A neural conversational model. arXiv preprint arXiv:1506.05869.
Lee, S., Lim, H., & Sedoc, J. (2020). An evaluation protocol for generative conversational systems. arXiv preprint arXiv:2010.12741.
Mehri, S., & Eskenazi, M. (2020). USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation. arXiv preprint arXiv:2005.00456.
Mehri, S., & Eskenazi, M. (2020, July). Unsupervised Evaluation of Interactive Dialog with DialoGPT. In Proc. of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 225-235).
Rudnicky, A., Banchs, R., D'Haro, L. F., Sedoc, J., Chen, Z., Rodríguez-Cantelar, M., Koh, A., & others. (2020). CHANEL-Metrics: Chat/Dialogue Modeling and Evaluation report. In 2020 Seventh Frederick Jelinek Memorial Summer Workshop.
Higashinaka, R., Funakoshi, K., Kobayashi, Y., & Inaba, M. (2016, May). The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 3146-3150).
Zhou, K., Prabhumoye, S., & Black, A. W. (2018). A dataset for document grounded conversations. arXiv preprint arXiv:1809.07358.
Danescu-Niculescu-Mizil, C., & Lee, L. (2011). Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. arXiv preprint arXiv:1106.3077.
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., & Niu, S. (2017). Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957.
Nie, Y., Williamson, M., Bansal, M., Kiela, D., & Weston, J. (2020). I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling. arXiv preprint arXiv:2012.13391.
Chen, S. Y., Hsu, C. C., Kuo, C. C., & Ku, L. W. (2018). Emotionlines: An emotion corpus of multi-party conversations. arXiv preprint arXiv:1802.08379.
Rashkin, H., Smith, E. M., Li, M., & Boureau, Y. L. (2018). Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207.
Moghe, N., Arora, S., Banerjee, S., & Khapra, M. M. (2018). Towards exploiting background knowledge for building conversation systems. arXiv preprint arXiv:1809.08205.
Adiwardana, D., Luong, M. T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., ... & Le, Q. V. (2020). Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977.
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2018). Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508.
Lee, S., Schulz, H., Atkinson, A., Gao, J., Suleman, K., El Asri, L., ... & Li, X. (2019). Multi-domain task-completion dialog challenge. Dialog system technology challenges, 8(9).
Banchs, R. E. (2012, July). Movie-DiC: a movie dialogue corpus for research and development. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 203-207).
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018). Personalizing dialogue agents: I have a dog, do you have pets too?. arXiv preprint arXiv:1801.07243.
Upadhayay, B., & Behzadan, V. (2020, November). Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification. In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 1-6). IEEE.
Cervone, A., & Riccardi, G. (2020). Is this dialogue coherent? learning from dialogue acts and entities. arXiv preprint arXiv:2006.10157.
Gopalakrishnan, K., Hedayatnia, B., Chen, Q., Gottardi, A., Kwatra, S., Venkatesh, A., ... & AI, A. A. (2019, January). Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations. In INTERSPEECH (pp. 1891-1895).
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., & Weston, J. (2018). Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241.
D'Haro, L. F., Shawar, B. A., & Yu, Z. (2016). REWOCHAT 2016–Shared task description report. In Proceedings of the workshop on collecting and generating resources for chatbots and conversational agents-development and evaluation (RE-WOCHAT) (p. 39).
Zhou, H., Huang, M., Zhang, T., Zhu, X., & Liu, B. (2018, April). Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
Zhou, H., Zheng, C., Huang, K., Huang, M., & Zhu, X. (2020). Kdconv: A chinese multi-domain dialogue dataset towards multi-turn knowledge-driven conversation. arXiv preprint arXiv:2004.04100.
Wang, Y., Ke, P., Zheng, Y., Huang, K., Jiang, Y., Zhu, X., & Huang, M. (2020, October). A large-scale chinese short-text conversation dataset. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 91-103). Springer, Cham.
Rei, R., Stewart, C., Farinha, A. C., & Lavie, A. (2020). COMET: A neural framework for MT evaluation. arXiv preprint arXiv:2009.09025.
Damodaran, P. (2021). Parrot: Paraphrase generation for NLU.
Zhou, L., Gao, J., Li, D., & Shum, H. Y. (2020). The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics, 46(1), 53-93.
Bao, S., He, H., Wang, F., Wu, H., & Wang, H. (2019). Plato: Pre-trained dialogue generation model with discrete latent variable. arXiv preprint arXiv:1910.07931.
Mehri, S., Choi, J., D'Haro, L. F., Deriu, J., Eskenazi, M., Gasic, M., ... & Zhang, C. (2022). Report from the nsf future directions workshop on automatic evaluation of dialog: Research directions and challenges. arXiv preprint arXiv:2203.10012.
|
mario-rc/dstc11.t4
|
[
"language:en",
"language:zh",
"language:es",
"license:apache-2.0",
"Robust",
"Multilingual",
"Open-Domain",
"region:us"
] |
2023-09-13T18:50:47+00:00
|
{"language": ["en", "zh", "es"], "license": "apache-2.0", "pretty_name": "DSTC11: Dialogue System Technology Challenge 11 Track 4: Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems", "tags": ["Robust", "Multilingual", "Open-Domain"]}
|
2023-09-15T17:32:14+00:00
|
[] |
[
"en",
"zh",
"es"
] |
TAGS
#language-English #language-Chinese #language-Spanish #license-apache-2.0 #Robust #Multilingual #Open-Domain #region-us
|
DSTC11: Dialogue System Technology Challenge 11Track 4: Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems
=============================================================================================================================================
Directory Structure Scheme
==========================
Representation of the directory tree structure:
Track Overview
==============
This track consists of two tasks which are explained in more detail below:
Participants will develop effective automatic open-ended and multilingual dialogue evaluation metrics that perform similarly when evaluated over a new language.
Participants will develop effective automatic open-ended dialogue evaluation metrics that perform robustly when evaluated over back-translated/paraphrased sentences in English.
For both tasks, proposed metrics are expected to show the following two important properties as indicated in (Deriu et al., 2019):
Correlated to human judgments - the metrics should produce evaluation scores that well correlate to human judgments (scores) across multiple languages or alternative responses (i.e., back-translated or paraphrased).
Explainable - the metrics should provide constructive and explicit feedback to the generative models in terms of the quality of their generated responses. For instance, if a generative model is contradicting itself, the evaluation metrics should signal such behavior to the generative models.
Participants can propose their own metric or optionally improve two baseline evaluation metrics: MDD-Eval (Zhang et al, 2021) or Deep AM-FM (Zhang et al, 2020). A leaderboard in the ChatEval platform will be provided allowing participants to check their progress.
For each evaluation task, Spearman correlation will be computed to compare the proposed evaluation metrics against human judgments. A final average score will be calculated to rank the submitted evaluation metrics.
For more details:
* Provided datasets
* Datasets format
* Task 1: Multilingual Automatic Evaluation Metrics
* Task 2: Robust Automatic Evaluation Metrics
* Baseline model
* FAQ
For more information check the ChatEval website.
Provided Datasets
=================
After the organizers' participation in the CHANEL@JSALT2020 workshop (Rudnicky et al., 2020) at John Hopkins University, they have automatically translated back-and-forth (using the same MS Azure translation service) a total of 18 well-known human-human dialogue datasets. These data sets will be used as training data. The total amount of dialogues is 393k (approx. 3M turns).
* DBDC (Higashinaka et al., 2016)
* CMU\_DoG (Zhou et al., 2018)
* Cornell Movie-Dialogs (Danescu-Niculescu-Mizil & Lee, 2011)
* DailyDialog (Li et al., 2017)
* DECODE (Nie et al., 2020)
* EmotionLines (Chen et al., 2018)
* EmpathicDialogues (Rashkin et al., 2018)
* Holl-E (Moghe et al., 2018)
* MEENA (Adiwardana et al., 2020)
* MELD (Poria et al., 2019)
* MetalWOz (Lee et al., 2019)
* Movie-DiC (Banchs, 2012)
* PersonaChat (Zhang et al., 2018)
* SentimentLIAR (Upadhayay & Behzadan, 2020)
* Switchboard Coherence (Cervone & Riccardi, 2020)
* Topical-Chat (Gopalakrishnan et al., 2019)
* Wizard of Wikipedia (Dinan et al., 2019)
* Wochat (D'Haro et al., 2016)
As development set, organizers will provide the following datasets identified during the DSTC10 Track 5 (Zhang et al, 2021), that sum up more than 35k turn-level human-annotations, which have been automatically translated to Spanish and Chinese, and back-translated both to English using MS Azure services.
* CONVAI2-GRADE (CG) (Huang et al., 2020)
* DAILYDIALOG-GRADE (DH) (Huang et al., 2020)
* DAILYDIALOG-GUPTA (DG) (Gupta et al., 2019)
* DAILYDIALOG-ZHAO (DZ) (Zhao et al., 2020)
* DSTC7 (D7) (Galley et al., 2019)
* EMPATHETIC-GRADE (EG) (Huang et al., 2020)
* FED-DIAL (FD) (Mehri & Eskenazi, 2020b)
* FED-TURN (FT) (Mehri & Eskenazi, 2020b)
* HUMOD (HM) (Merdivan et al., 2020)
* PERSONA-SEE (PS) (See et al., 2019)
* PERSONA-USR (PU) (Mehri & Eskenazi, 2020a)
* PERSONA-ZHAO (PZ) (Zhao et al., 2020)
* TOPICAL-USR (TU) (Mehri & Eskenazi, 2020a)
This development data can help participants to check the multilingualism or robustness capabilities of their trained models in terms of correlations with human-annotations. Additional databases, not mentioned here, will be added when available to increase the size of the benchmarking.
Moreover, the datasets provided by THU-COAI group (Conversational AI groups from Tsinghua University) will be used, naming this set of data CDial. They contain open domain human-human dialogs. They are originally in Chinese and contain of 3,470 dialogs (approx. 130k turns).
* ECM (Zhou et al., 2018)
* KdConv (Zhou et al., 2020)
* LCCC (Wang et al., 2020)
In addition, we will provide the same datasets translated (CHANEL@JSALT2020 and CDial) into Chinese using the SotA Tencent MT system.
These datasets will be provided to participants, together with automatic meta-data information (machine translation Quality Estimation (QE), toxicity, and sentiment analysis) for filtering and dialogue curation purposes, so the participants have a better reference of the dataset quality, being of great help for them to decide whether or not to use these translations/paraphrases in the training of their evaluation models, and optionally fine-tune multilingual pre-trained models allowing better performance on the proposed dialogue-oriented tasks.
Since the quality of the back-translated sentences can play an important role in estimating the metric scores. QE metric scores will be given to the participants using our QE system and other existing models (e.g., COMET (Rei et al., 2020)). This information will be given to participants so they can optionally use it for discarding dialogues or turns that do not show high quality when training their metrics. Participants will be welcome to use the data and ideas from the MT field to propose QE metrics that can, optionally, be included to provide final scores. Finally, the organizers may provide new translated dialogue datasets to allow participants to create more robust and better-trained systems.
Regarding the paraphrases, all the original English sentences of each dataset will have multiple paraphrases, as well as annotations so that each participant can evaluate the quality of each paraphrase. The model used will be PARROT (Damodaran P., 2021).
Additionally, ~3k random H-H turns (~1k dialogues) of CDial in Chinese were manually annotated by Tencent AI. Also, ~5k new H-C Chinese turns (~500 dialogues) were generated with three different SotA chatbots (Tencent's model, Microsoft's Xiaoice (Zhou et al., 2020) and Baidu's Plato (Bao et al., 2019)). Both turn-level and dialog-level annotations were manually annotated by Tencent AI.
During the test phase, a new set of 2k turn-level (~700 dialog-level) manually curated multilingual corpus (Spanish and Chinese) along with their turn-level and dialog-level human evaluation annotations will be provided to participants to test models for both tasks. This corpus will be manually checked to guarantee its quality and high correlation with the original dialogues.
Furthermore, in order to check the generalization capabilities of the proposed metrics from the participant, the test data will include a new dataset of human-chatbot interactions with ~2k turns (~60 dialogues).
Datasets Summary
----------------
Datasets Statistics
-------------------
Datasets Information
--------------------
CHANEL dataset. The source language is English.
DSTC10 dataset. The source language is English.
CDIAL dataset. The source language is Chinese.
Data Format
-----------
All data given follows the Data Formats which provides guidelines on how to store, maintain and handle dialogue corpora.
Dimensions Evaluation
---------------------
Considering the annotations available in the development data, the test data will have the following dimensions (annotations) to evaluate in both Task 1 (English, Chinese and Spanish) and Task 2:
* Turn-level: Appropriateness, Content Richness, Grammatical Correctness and Relevance
* Dialogue-level: Coherence, Engageness/Likeability, Informativeness and Overall.
The annotations will be evaluated and indicated individually (dimension by dimension), discriminating by dataset and language. In addition, a global score will be estimated by grouping all dimensions. This global value will be calculated separately at turn-level and dialogue-level for each task.
A brief description of each dimension (Mehri et al., 2022) is shown below.
Turn-level:
* Appropriateness - The response is appropriate given the preceding dialogue.
* Content Richness - The response is informative, with long sentences including multiple entities and conceptual or emotional words.
* Grammatical Correctness - Responses are free of grammatical and semantic errors.
* Relevance - Responses are on-topic with the immediate dialog history.
Dialogue-level:
* Coherence - Throughout the dialog, is the system maintaining a good conversation flow.
* Engageness/Likeability - Throughout the dialogue, the system displays a likeable personality.
* Informativeness - Throughout the dialog, the system provides unique and non-generic information.
* Overall - The overall quality of and satisfaction with the dialog.
Organizers
==========
* Mario Rodríguez-Cantelar (Universidad Politécnica de Madrid, Spain)
* Chen Zhang (National University of Singapore, Singapore)
* Chengguang Tang (Tencent AI Lab, China)
* Ke Shi (Tencent AI Lab, China)
* Sarik Ghazarian (University of Southern California, USA)
* João Sedoc (New York University, USA)
* Luis F. D'Haro (Universidad Politécnica de Madrid, Spain)
* Alexander Rudnicky (Carnegie Mellon University, USA)
Please cite the paper, code or data from DSTC 11 Track 4:
Acknowledgement
===============
This research project is supported by the Comunidad de Madrid through the call Research Grants for Young Investigators from Universidad Politécnica de Madrid (GENIUS:APOYO-JOVENES-21-TAXTYC-32-K61X37).
This work is supported by project BEWORD (PID2021-126061OB-C43) funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union”, and by Programa Propio - Proyectos Semilla: Universidad Politécnica de Madrid (VSEMILLA22LFHE).
We gratefully acknowledge valuable efforts from Tencent AI Lab who supports Chinese translation and annotation of datasets by funding and infrastructure.
Thanks to THU-CoAI (Conversational AI groups from Tsinghua University) for providing their Chinese datasets as part of the challenge data.
Thanks to Unbabel for providing the COMET MTQE scores annotations as part of the challenge data. This contribution was supported by national funds through *Fundação para a Ciência e a Tecnologia* (FCT) with references PRT/BD/152198/2021 and UIDB/50021/2020, and by the P2020 program MAIA led by Unbabel (LISBOA-01-0247-FEDER-045909).
We also want to give thanks to MS Azure services (especially to Irving Kwong) for their sponsorship to continue processing new datasets that could be interesting for the dialogue community.
This research project is supported by the NYU ChatEval Team led by João Sedoc.
This research project is supported in part by a grant from Amazon to Alexander Rudnicky, Carnegie Mellon University.
Thanks to Karthik Ganesan, Sarik Ghazarian, James Hagerty, Zhang Chen and Alex Rudnicky for developing the baseline model as part of the challenge tasks.
This work is supported by the European Commission through Project ASTOUND (101071191 — HORIZON-EIC-2021-PATHFINDERCHALLENGES-01).
!alt text
References
==========
Deriu, J., Rodrigo, A., Otegi, A., Echegoyen, G., Rosset, S., Agirre, E., & Cieliebak, M. (2020). Survey on evaluation methods for dialogue systems. Artificial Intelligence Review, 1-56.
Zhang, C., D'Haro, L. F., Friedrichs, T., & Li, H. (2021). MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation. arXiv preprint arXiv:2112.07194.
Zhang, C., D'Haro, L. F., Banchs, R. E., Friedrichs, T., & Li, H. (2020). Deep AM-FM: Toolkit for Automatic Dialogue Evaluation. In Conversational Dialogue Systems for the Next Decade (pp. 53-69). Springer, Singapore.
Zhang, C., Sadoc, J., D'Haro, L. F., Banchs, R., & Rudnicky, A. (2021). Automatic Evaluation and Moderation of Open-domain Dialogue Systems. arXiv preprint arXiv:2111.02110.
Hori, C., & Hori, T. (2017). End-to-end conversation modeling track in DSTC6. arXiv preprint arXiv:1706.07440.
Galley, M., Brockett, C., Gao, X., Gao, J., & Dolan, B. (2019). Grounded response generation task at dstc7. In AAAI Dialog System Technology Challenges Workshop.
See, A., Roller, S., Kiela, D., & Weston, J. (2019). What makes a good conversation? how controllable attributes affect human judgments. arXiv preprint arXiv:1902.08654.
Sedoc, J., Ippolito, D., Kirubarajan, A., Thirani, J., Ungar, L., & Callison-Burch, C. (2019, June). Chateval: A tool for chatbot evaluation. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics (demonstrations) (pp. 60-65).
Vinyals, O., & Le, Q. (2015). A neural conversational model. arXiv preprint arXiv:1506.05869.
Lee, S., Lim, H., & Sedoc, J. (2020). An evaluation protocol for generative conversational systems. arXiv preprint arXiv:2010.12741.
Mehri, S., & Eskenazi, M. (2020). USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation. arXiv preprint arXiv:2005.00456.
Mehri, S., & Eskenazi, M. (2020, July). Unsupervised Evaluation of Interactive Dialog with DialoGPT. In Proc. of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue (pp. 225-235).
Rudnicky, A., Banchs, R., D'Haro, L. F., Sedoc, J., Chen, Z., Rodríguez-Cantelar, M., Koh, A., & others. (2020). CHANEL-Metrics: Chat/Dialogue Modeling and Evaluation report. In 2020 Seventh Frederick Jelinek Memorial Summer Workshop.
Higashinaka, R., Funakoshi, K., Kobayashi, Y., & Inaba, M. (2016, May). The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16) (pp. 3146-3150).
Zhou, K., Prabhumoye, S., & Black, A. W. (2018). A dataset for document grounded conversations. arXiv preprint arXiv:1809.07358.
Danescu-Niculescu-Mizil, C., & Lee, L. (2011). Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. arXiv preprint arXiv:1106.3077.
Li, Y., Su, H., Shen, X., Li, W., Cao, Z., & Niu, S. (2017). Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957.
Nie, Y., Williamson, M., Bansal, M., Kiela, D., & Weston, J. (2020). I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling. arXiv preprint arXiv:2012.13391.
Chen, S. Y., Hsu, C. C., Kuo, C. C., & Ku, L. W. (2018). Emotionlines: An emotion corpus of multi-party conversations. arXiv preprint arXiv:1802.08379.
Rashkin, H., Smith, E. M., Li, M., & Boureau, Y. L. (2018). Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207.
Moghe, N., Arora, S., Banerjee, S., & Khapra, M. M. (2018). Towards exploiting background knowledge for building conversation systems. arXiv preprint arXiv:1809.08205.
Adiwardana, D., Luong, M. T., So, D. R., Hall, J., Fiedel, N., Thoppilan, R., ... & Le, Q. V. (2020). Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977.
Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., & Mihalcea, R. (2018). Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508.
Lee, S., Schulz, H., Atkinson, A., Gao, J., Suleman, K., El Asri, L., ... & Li, X. (2019). Multi-domain task-completion dialog challenge. Dialog system technology challenges, 8(9).
Banchs, R. E. (2012, July). Movie-DiC: a movie dialogue corpus for research and development. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 203-207).
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018). Personalizing dialogue agents: I have a dog, do you have pets too?. arXiv preprint arXiv:1801.07243.
Upadhayay, B., & Behzadan, V. (2020, November). Sentimental LIAR: Extended Corpus and Deep Learning Models for Fake Claim Classification. In 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 1-6). IEEE.
Cervone, A., & Riccardi, G. (2020). Is this dialogue coherent? learning from dialogue acts and entities. arXiv preprint arXiv:2006.10157.
Gopalakrishnan, K., Hedayatnia, B., Chen, Q., Gottardi, A., Kwatra, S., Venkatesh, A., ... & AI, A. A. (2019, January). Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations. In INTERSPEECH (pp. 1891-1895).
Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., & Weston, J. (2018). Wizard of wikipedia: Knowledge-powered conversational agents. arXiv preprint arXiv:1811.01241.
D'Haro, L. F., Shawar, B. A., & Yu, Z. (2016). REWOCHAT 2016–Shared task description report. In Proceedings of the workshop on collecting and generating resources for chatbots and conversational agents-development and evaluation (RE-WOCHAT) (p. 39).
Zhou, H., Huang, M., Zhang, T., Zhu, X., & Liu, B. (2018, April). Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
Zhou, H., Zheng, C., Huang, K., Huang, M., & Zhu, X. (2020). Kdconv: A chinese multi-domain dialogue dataset towards multi-turn knowledge-driven conversation. arXiv preprint arXiv:2004.04100.
Wang, Y., Ke, P., Zheng, Y., Huang, K., Jiang, Y., Zhu, X., & Huang, M. (2020, October). A large-scale chinese short-text conversation dataset. In CCF International Conference on Natural Language Processing and Chinese Computing (pp. 91-103). Springer, Cham.
Rei, R., Stewart, C., Farinha, A. C., & Lavie, A. (2020). COMET: A neural framework for MT evaluation. arXiv preprint arXiv:2009.09025.
Damodaran, P. (2021). Parrot: Paraphrase generation for NLU.
Zhou, L., Gao, J., Li, D., & Shum, H. Y. (2020). The design and implementation of xiaoice, an empathetic social chatbot. Computational Linguistics, 46(1), 53-93.
Bao, S., He, H., Wang, F., Wu, H., & Wang, H. (2019). Plato: Pre-trained dialogue generation model with discrete latent variable. arXiv preprint arXiv:1910.07931.
Mehri, S., Choi, J., D'Haro, L. F., Deriu, J., Eskenazi, M., Gasic, M., ... & Zhang, C. (2022). Report from the nsf future directions workshop on automatic evaluation of dialog: Research directions and challenges. arXiv preprint arXiv:2203.10012.
|
[] |
[
"TAGS\n#language-English #language-Chinese #language-Spanish #license-apache-2.0 #Robust #Multilingual #Open-Domain #region-us \n"
] |
[
42
] |
[
"passage: TAGS\n#language-English #language-Chinese #language-Spanish #license-apache-2.0 #Robust #Multilingual #Open-Domain #region-us \n"
] |
0b4123c9f90e8873d62b7b798e958bd22f76c6cf
|
# Dataset Card for "textbook_synth_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vikp/textbook_synth_sample
|
[
"region:us"
] |
2023-09-13T18:54:13+00:00
|
{"dataset_info": {"features": [{"name": "markdown", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5522373, "num_examples": 368}], "download_size": 0, "dataset_size": 5522373}}
|
2023-09-13T18:55:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "textbook_synth_sample"
More Information needed
|
[
"# Dataset Card for \"textbook_synth_sample\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"textbook_synth_sample\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"textbook_synth_sample\"\n\nMore Information needed"
] |
7e4a7d955ad48ff7fa39bf23f8611196bea7f4b3
|
# Dataset of tachibana_alice/橘ありす (THE iDOLM@STER: Cinderella Girls)
This is the dataset of tachibana_alice/橘ありす (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, brown_eyes, bow, hair_bow, bangs, blue_bow`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 623.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tachibana_alice_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 360.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tachibana_alice_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1188 | 770.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tachibana_alice_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 552.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/tachibana_alice_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1188 | 1.07 GiB | [Download](https://huggingface.co/datasets/CyberHarem/tachibana_alice_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/tachibana_alice_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, blue_dress, looking_at_viewer, solo, white_background, blush, simple_background, belt, puffy_short_sleeves, closed_mouth |
| 1 | 7 |  |  |  |  |  | 1girl, blue_dress, looking_at_viewer, solo, white_gloves, blush, smile, frilled_dress, open_mouth, simple_background, white_background, hairband, heart, one_eye_closed, sleeveless_dress, sparkle, tiara |
| 2 | 15 |  |  |  |  |  | 1girl, blush, solo, looking_at_viewer, open_mouth, dress, :d, black_hair |
| 3 | 8 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, ribbon, enmaided, maid_headdress, strawberry, frills, maid_apron, puffy_short_sleeves, simple_background, white_background, bowtie, white_apron |
| 4 | 12 |  |  |  |  |  | 1girl, solo, blush, looking_at_viewer, plaid_scarf, skirt, jacket, school_uniform |
| 5 | 9 |  |  |  |  |  | 1girl, blush, gym_shirt, gym_shorts, gym_uniform, short_sleeves, white_shirt, name_tag, red_shorts, white_background, looking_at_viewer, simple_background, closed_mouth, solo, open_mouth, sweat |
| 6 | 34 |  |  |  |  |  | 1girl, blush, open_mouth, loli, small_breasts, nipples, hetero, navel, 1boy, nude, penis, spread_legs, half_updo, cum_in_pussy, solo_focus, looking_at_viewer, sex, sidelocks, bar_censor, collarbone, vaginal, lying, parted_bangs |
| 7 | 10 |  |  |  |  |  | 1girl, blush, collarbone, solo, blue_one-piece_swimsuit, looking_at_viewer, small_breasts, covered_navel, name_tag, simple_background, :o, half_updo, old_school_swimsuit, open_mouth |
| 8 | 6 |  |  |  |  |  | 1girl, blush, obi, print_kimono, floral_print, holding, solo, blue_kimono, looking_at_viewer, :o, hair_flower, long_sleeves, open_mouth, outdoors, upper_body, wide_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_dress | looking_at_viewer | solo | white_background | blush | simple_background | belt | puffy_short_sleeves | closed_mouth | white_gloves | smile | frilled_dress | open_mouth | hairband | heart | one_eye_closed | sleeveless_dress | sparkle | tiara | dress | :d | black_hair | ribbon | enmaided | maid_headdress | strawberry | frills | maid_apron | bowtie | white_apron | plaid_scarf | skirt | jacket | school_uniform | gym_shirt | gym_shorts | gym_uniform | short_sleeves | white_shirt | name_tag | red_shorts | sweat | loli | small_breasts | nipples | hetero | navel | 1boy | nude | penis | spread_legs | half_updo | cum_in_pussy | solo_focus | sex | sidelocks | bar_censor | collarbone | vaginal | lying | parted_bangs | blue_one-piece_swimsuit | covered_navel | :o | old_school_swimsuit | obi | print_kimono | floral_print | holding | blue_kimono | hair_flower | long_sleeves | outdoors | upper_body | wide_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:--------------------|:-------|:-------------------|:--------|:--------------------|:-------|:----------------------|:---------------|:---------------|:--------|:----------------|:-------------|:-----------|:--------|:-----------------|:-------------------|:----------|:--------|:--------|:-----|:-------------|:---------|:-----------|:-----------------|:-------------|:---------|:-------------|:---------|:--------------|:--------------|:--------|:---------|:-----------------|:------------|:-------------|:--------------|:----------------|:--------------|:-----------|:-------------|:--------|:-------|:----------------|:----------|:---------|:--------|:-------|:-------|:--------|:--------------|:------------|:---------------|:-------------|:------|:------------|:-------------|:-------------|:----------|:--------|:---------------|:--------------------------|:----------------|:-----|:----------------------|:------|:---------------|:---------------|:----------|:--------------|:--------------|:---------------|:-----------|:-------------|:---------------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 15 |  |  |  |  |  | X | | X | X | | X | | | | | | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 8 |  |  |  |  |  | X | | X | X | X | X | X | | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | 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 | 34 |  |  |  |  |  | X | | X | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | X | | X | X | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | | | | | | | | X | | | | | | X | | | | X | X | X | X | | | | | | | | | | |
| 8 | 6 |  |  |  |  |  | X | | X | X | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/tachibana_alice_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T18:58:34+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T10:07:57+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of tachibana\_alice/橘ありす (THE iDOLM@STER: Cinderella Girls)
===================================================================
This is the dataset of tachibana\_alice/橘ありす (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, bow, hair\_bow, bangs, blue\_bow', which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
List of Packages
----------------
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code
List of Clusters
----------------
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
### Table Version
|
[
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n",
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version"
] |
[
44,
61,
5,
4
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.### Raw Text Version### Table Version"
] |
926ef25704670dd40a7b614822e7b173535853a0
|
# Dataset Card for Evaluation run of wenge-research/yayi-70b-llama2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/wenge-research/yayi-70b-llama2
- **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 [wenge-research/yayi-70b-llama2](https://huggingface.co/wenge-research/yayi-70b-llama2) 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_wenge-research__yayi-70b-llama2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-13T20:08:14.965059](https://huggingface.co/datasets/open-llm-leaderboard/details_wenge-research__yayi-70b-llama2/blob/main/results_2023-09-13T20-08-14.965059.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": 0.6427362614871128,
"acc_stderr": 0.03251742836753478,
"acc_norm": 0.6468766983428953,
"acc_norm_stderr": 0.032494548846313066,
"mc1": 0.30599755201958384,
"mc1_stderr": 0.016132229728155045,
"mc2": 0.4762734947955207,
"mc2_stderr": 0.01439837288557781
},
"harness|arc:challenge|25": {
"acc": 0.5614334470989761,
"acc_stderr": 0.014500682618212862,
"acc_norm": 0.606655290102389,
"acc_norm_stderr": 0.014275101465693026
},
"harness|hellaswag|10": {
"acc": 0.640211113324039,
"acc_stderr": 0.0047895751634186535,
"acc_norm": 0.8392750448117905,
"acc_norm_stderr": 0.00366526456385775
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322666,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322666
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7013888888888888,
"acc_stderr": 0.03827052357950756,
"acc_norm": 0.7013888888888888,
"acc_norm_stderr": 0.03827052357950756
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5606936416184971,
"acc_stderr": 0.037842719328874674,
"acc_norm": 0.5606936416184971,
"acc_norm_stderr": 0.037842719328874674
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6170212765957447,
"acc_stderr": 0.03177821250236922,
"acc_norm": 0.6170212765957447,
"acc_norm_stderr": 0.03177821250236922
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.04644602091222318,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.04644602091222318
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6068965517241379,
"acc_stderr": 0.0407032901370707,
"acc_norm": 0.6068965517241379,
"acc_norm_stderr": 0.0407032901370707
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.025467149045469536,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.025467149045469536
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.36507936507936506,
"acc_stderr": 0.04306241259127153,
"acc_norm": 0.36507936507936506,
"acc_norm_stderr": 0.04306241259127153
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7870967741935484,
"acc_stderr": 0.02328766512726854,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.02328766512726854
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5369458128078818,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.5369458128078818,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.793939393939394,
"acc_stderr": 0.0315841532404771,
"acc_norm": 0.793939393939394,
"acc_norm_stderr": 0.0315841532404771
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.020986854593289708,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.020986854593289708
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6487179487179487,
"acc_stderr": 0.024203665177902803,
"acc_norm": 0.6487179487179487,
"acc_norm_stderr": 0.024203665177902803
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.028972648884844267,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.028972648884844267
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6386554621848739,
"acc_stderr": 0.03120469122515002,
"acc_norm": 0.6386554621848739,
"acc_norm_stderr": 0.03120469122515002
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.41721854304635764,
"acc_stderr": 0.04026141497634612,
"acc_norm": 0.41721854304635764,
"acc_norm_stderr": 0.04026141497634612
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8128440366972477,
"acc_stderr": 0.016722684526200154,
"acc_norm": 0.8128440366972477,
"acc_norm_stderr": 0.016722684526200154
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"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_aging|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-jurisprudence|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.0246596851859673
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5177304964539007,
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5772058823529411,
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
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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": {
"acc": 0.93,
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"harness|hendrycksTest-virology|5": {
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"acc_stderr": 0.03882310850890594,
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"acc_norm_stderr": 0.03882310850890594
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"harness|hendrycksTest-world_religions|5": {
"acc": 0.8538011695906432,
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"harness|truthfulqa:mc|0": {
"mc1": 0.30599755201958384,
"mc1_stderr": 0.016132229728155045,
"mc2": 0.4762734947955207,
"mc2_stderr": 0.01439837288557781
}
}
```
### 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_wenge-research__yayi-70b-llama2
|
[
"region:us"
] |
2023-09-13T19:08:28+00:00
|
{"pretty_name": "Evaluation run of wenge-research/yayi-70b-llama2", "dataset_summary": "Dataset automatically created during the evaluation run of model [wenge-research/yayi-70b-llama2](https://huggingface.co/wenge-research/yayi-70b-llama2) 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_wenge-research__yayi-70b-llama2\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-13T20:08:14.965059](https://huggingface.co/datasets/open-llm-leaderboard/details_wenge-research__yayi-70b-llama2/blob/main/results_2023-09-13T20-08-14.965059.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\": 0.6427362614871128,\n \"acc_stderr\": 0.03251742836753478,\n \"acc_norm\": 0.6468766983428953,\n \"acc_norm_stderr\": 0.032494548846313066,\n \"mc1\": 0.30599755201958384,\n \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.4762734947955207,\n \"mc2_stderr\": 0.01439837288557781\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5614334470989761,\n \"acc_stderr\": 0.014500682618212862,\n \"acc_norm\": 0.606655290102389,\n \"acc_norm_stderr\": 0.014275101465693026\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.640211113324039,\n \"acc_stderr\": 0.0047895751634186535,\n \"acc_norm\": 0.8392750448117905,\n \"acc_norm_stderr\": 0.00366526456385775\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5606936416184971,\n \"acc_stderr\": 0.037842719328874674,\n \"acc_norm\": 0.5606936416184971,\n \"acc_norm_stderr\": 0.037842719328874674\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.6170212765957447,\n \"acc_stderr\": 0.03177821250236922,\n \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.03177821250236922\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.04644602091222318,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.04644602091222318\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.6068965517241379,\n \"acc_stderr\": 0.0407032901370707,\n \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.0407032901370707\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42592592592592593,\n \"acc_stderr\": 0.025467149045469536,\n \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.025467149045469536\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7870967741935484,\n \"acc_stderr\": 0.02328766512726854,\n \"acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.02328766512726854\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5369458128078818,\n \"acc_stderr\": 0.035083705204426656,\n \"acc_norm\": 0.5369458128078818,\n \"acc_norm_stderr\": 0.035083705204426656\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.0315841532404771,\n \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.0315841532404771\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289708,\n \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289708\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6487179487179487,\n \"acc_stderr\": 0.024203665177902803,\n \"acc_norm\": 0.6487179487179487,\n \"acc_norm_stderr\": 0.024203665177902803\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6386554621848739,\n \"acc_stderr\": 0.03120469122515002,\n \"acc_norm\": 0.6386554621848739,\n \"acc_norm_stderr\": 0.03120469122515002\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.41721854304635764,\n \"acc_stderr\": 0.04026141497634612,\n \"acc_norm\": 0.41721854304635764,\n \"acc_norm_stderr\": 0.04026141497634612\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200154,\n \"acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200154\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02552472232455334,\n \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02552472232455334\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944853,\n \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944853\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7354260089686099,\n \"acc_stderr\": 0.02960510321703832,\n \"acc_norm\": 0.7354260089686099,\n \"acc_norm_stderr\": 0.02960510321703832\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.035477710041594654,\n \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.035477710041594654\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8677685950413223,\n \"acc_stderr\": 0.0309227883204458,\n \"acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.0309227883204458\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n \"acc_stderr\": 0.038935425188248475,\n \"acc_norm\": 0.7962962962962963,\n \"acc_norm_stderr\": 0.038935425188248475\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.0398913985953177,\n \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.0398913985953177\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n \"acc_stderr\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 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TAGS
#region-us
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# Dataset Card for Evaluation run of wenge-research/yayi-70b-llama2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model wenge-research/yayi-70b-llama2 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 2023-09-13T20:08:14.965059(note that their might be results for other tasks in 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 wenge-research/yayi-70b-llama2",
"## 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 wenge-research/yayi-70b-llama2 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 2023-09-13T20:08:14.965059(note that their might be results for other tasks in 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 wenge-research/yayi-70b-llama2",
"## 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 wenge-research/yayi-70b-llama2 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 2023-09-13T20:08:14.965059(note that their might be results for other tasks in 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,
23,
31,
171,
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 wenge-research/yayi-70b-llama2## 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 wenge-research/yayi-70b-llama2 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 2023-09-13T20:08:14.965059(note that their might be results for other tasks in 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"
] |
ae03323721327f9f893a651ade4b422801e32d3d
|
# French Transcribed Podcast
### Dataset Summary
Dataset of 280,000 mp3 links to French podcasts. Transcription using [whisper](https://github.com/openai/whisper) is underway. However, due to the large number of podcasts, it will not be possible to transcribe all of them. We are therefore counting on the help of the community to help us finish this colossal task.
The total duration of the podcasts is estimated at approximately 2958 days (4259523 minutes). However, this value is only an indication, as some links no longer seem to work and not all podcasts have the indicated duration.
N.B. The podcast links are available on the French government's data gouv [website](https://www.data.gouv.fr/fr/datasets/podcasts-francais-archives-a-lina/).
|
Nicolas-BZRD/French_Transcribed_Podcast
|
[
"task_categories:automatic-speech-recognition",
"size_categories:100K<n<1M",
"language:fr",
"license:unknown",
"Podcast",
"Audio",
"Transcribed",
"region:us"
] |
2023-09-13T19:12:37+00:00
|
{"language": ["fr"], "license": "unknown", "size_categories": ["100K<n<1M"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "Transcribed French Podcast", "tags": ["Podcast", "Audio", "Transcribed"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "programme_id", "dtype": "string"}, {"name": "programme_entry_date", "dtype": "string"}, {"name": "programme_rss_link", "dtype": "string"}, {"name": "podcast_title", "dtype": "string"}, {"name": "podcast_date", "dtype": "string"}, {"name": "podcast_duration", "dtype": "string"}, {"name": "audio_podcast_link", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 96627005, "num_examples": 281759}], "download_size": 28777088, "dataset_size": 96627005}}
|
2023-09-22T09:03:18+00:00
|
[] |
[
"fr"
] |
TAGS
#task_categories-automatic-speech-recognition #size_categories-100K<n<1M #language-French #license-unknown #Podcast #Audio #Transcribed #region-us
|
# French Transcribed Podcast
### Dataset Summary
Dataset of 280,000 mp3 links to French podcasts. Transcription using whisper is underway. However, due to the large number of podcasts, it will not be possible to transcribe all of them. We are therefore counting on the help of the community to help us finish this colossal task.
The total duration of the podcasts is estimated at approximately 2958 days (4259523 minutes). However, this value is only an indication, as some links no longer seem to work and not all podcasts have the indicated duration.
N.B. The podcast links are available on the French government's data gouv website.
|
[
"# French Transcribed Podcast",
"### Dataset Summary\n\nDataset of 280,000 mp3 links to French podcasts. Transcription using whisper is underway. However, due to the large number of podcasts, it will not be possible to transcribe all of them. We are therefore counting on the help of the community to help us finish this colossal task.\n\nThe total duration of the podcasts is estimated at approximately 2958 days (4259523 minutes). However, this value is only an indication, as some links no longer seem to work and not all podcasts have the indicated duration.\n\nN.B. The podcast links are available on the French government's data gouv website."
] |
[
"TAGS\n#task_categories-automatic-speech-recognition #size_categories-100K<n<1M #language-French #license-unknown #Podcast #Audio #Transcribed #region-us \n",
"# French Transcribed Podcast",
"### Dataset Summary\n\nDataset of 280,000 mp3 links to French podcasts. Transcription using whisper is underway. However, due to the large number of podcasts, it will not be possible to transcribe all of them. We are therefore counting on the help of the community to help us finish this colossal task.\n\nThe total duration of the podcasts is estimated at approximately 2958 days (4259523 minutes). However, this value is only an indication, as some links no longer seem to work and not all podcasts have the indicated duration.\n\nN.B. The podcast links are available on the French government's data gouv website."
] |
[
57,
6,
145
] |
[
"passage: TAGS\n#task_categories-automatic-speech-recognition #size_categories-100K<n<1M #language-French #license-unknown #Podcast #Audio #Transcribed #region-us \n# French Transcribed Podcast### Dataset Summary\n\nDataset of 280,000 mp3 links to French podcasts. Transcription using whisper is underway. However, due to the large number of podcasts, it will not be possible to transcribe all of them. We are therefore counting on the help of the community to help us finish this colossal task.\n\nThe total duration of the podcasts is estimated at approximately 2958 days (4259523 minutes). However, this value is only an indication, as some links no longer seem to work and not all podcasts have the indicated duration.\n\nN.B. The podcast links are available on the French government's data gouv website."
] |
61266682d88db98d55bb2dbf73af7328a3c0c7d8
|
# Dataset Card for "maltaomics_dataset_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
roa7n/maltaomics_dataset_embeddings
|
[
"region:us"
] |
2023-09-13T19:19:34+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "seq", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "features", "dtype": "string"}, {"name": "0", "dtype": "float64"}, {"name": "1", "dtype": "float64"}, {"name": "2", "dtype": "float64"}, {"name": "3", "dtype": "float64"}, {"name": "4", "dtype": "float64"}, {"name": "5", "dtype": "float64"}, {"name": "6", "dtype": "float64"}, {"name": "7", "dtype": "float64"}, {"name": "8", "dtype": "float64"}, {"name": "9", "dtype": "float64"}, {"name": "10", "dtype": "float64"}, {"name": "11", "dtype": "float64"}, {"name": "12", "dtype": "float64"}, {"name": "13", "dtype": "float64"}, {"name": "14", "dtype": "float64"}, {"name": "15", "dtype": "float64"}, {"name": "16", "dtype": "float64"}, {"name": "17", "dtype": "float64"}, {"name": "18", "dtype": "float64"}, {"name": "19", "dtype": "float64"}, {"name": "20", "dtype": "float64"}, {"name": "21", "dtype": "float64"}, {"name": "22", "dtype": "float64"}, {"name": "23", "dtype": "float64"}, {"name": "24", "dtype": "float64"}, {"name": "25", "dtype": "float64"}, {"name": "26", "dtype": "float64"}, {"name": "27", "dtype": "float64"}, {"name": "28", "dtype": "float64"}, {"name": "29", "dtype": "float64"}, {"name": "30", "dtype": "float64"}, {"name": "31", "dtype": "float64"}, {"name": "32", "dtype": "float64"}, {"name": "33", "dtype": "float64"}, {"name": "34", "dtype": "float64"}, {"name": "35", "dtype": "float64"}, {"name": "36", "dtype": "float64"}, {"name": "37", "dtype": "float64"}, {"name": "38", "dtype": "float64"}, {"name": "39", "dtype": "float64"}, {"name": "40", "dtype": "float64"}, {"name": "41", "dtype": "float64"}, {"name": "42", "dtype": "float64"}, {"name": "43", "dtype": "float64"}, {"name": "44", "dtype": "float64"}, {"name": "45", "dtype": "float64"}, {"name": "46", "dtype": "float64"}, {"name": "47", "dtype": 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{"name": "184", "dtype": "float64"}, {"name": "185", "dtype": "float64"}, {"name": "186", "dtype": "float64"}, {"name": "187", "dtype": "float64"}, {"name": "188", "dtype": "float64"}, {"name": "189", "dtype": "float64"}, {"name": "190", "dtype": "float64"}, {"name": "191", "dtype": "float64"}, {"name": "192", "dtype": "float64"}, {"name": "193", "dtype": "float64"}, {"name": "194", "dtype": "float64"}, {"name": "195", "dtype": "float64"}, {"name": "196", "dtype": "float64"}, {"name": "197", "dtype": "float64"}, {"name": "198", "dtype": "float64"}, {"name": "199", "dtype": "float64"}, {"name": "200", "dtype": "float64"}, {"name": "201", "dtype": "float64"}, {"name": "202", "dtype": "float64"}, {"name": "203", "dtype": "float64"}, {"name": "204", "dtype": "float64"}, {"name": "205", "dtype": "float64"}, {"name": "206", "dtype": "float64"}, {"name": "207", "dtype": "float64"}, {"name": "208", "dtype": "float64"}, {"name": "209", "dtype": "float64"}, {"name": "210", "dtype": "float64"}, 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{"name": "994", "dtype": "float64"}, {"name": "995", "dtype": "float64"}, {"name": "996", "dtype": "float64"}, {"name": "997", "dtype": "float64"}, {"name": "998", "dtype": "float64"}, {"name": "999", "dtype": "float64"}, {"name": "1000", "dtype": "float64"}, {"name": "1001", "dtype": "float64"}, {"name": "1002", "dtype": "float64"}, {"name": "1003", "dtype": "float64"}, {"name": "1004", "dtype": "float64"}, {"name": "1005", "dtype": "float64"}, {"name": "1006", "dtype": "float64"}, {"name": "1007", "dtype": "float64"}, {"name": "1008", "dtype": "float64"}, {"name": "1009", "dtype": "float64"}, {"name": "1010", "dtype": "float64"}, {"name": "1011", "dtype": "float64"}, {"name": "1012", "dtype": "float64"}, {"name": "1013", "dtype": "float64"}, {"name": "1014", "dtype": "float64"}, {"name": "1015", "dtype": "float64"}, {"name": "1016", "dtype": "float64"}, {"name": "1017", "dtype": "float64"}, {"name": "1018", "dtype": "float64"}, {"name": "1019", "dtype": "float64"}, {"name": "1020", "dtype": "float64"}, {"name": "1021", "dtype": "float64"}, {"name": "1022", "dtype": "float64"}, {"name": "1023", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 49274939, "num_examples": 1600}, {"name": "test", "num_bytes": 12315986, "num_examples": 400}], "download_size": 0, "dataset_size": 61590925}}
|
2023-09-13T19:46:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "maltaomics_dataset_embeddings"
More Information needed
|
[
"# Dataset Card for \"maltaomics_dataset_embeddings\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"maltaomics_dataset_embeddings\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"maltaomics_dataset_embeddings\"\n\nMore Information needed"
] |
67046728b3eba4c9907ac1cd9fa3c998baabb37a
|
# Dataset Card for "summ_screen_fd_memsum_bp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
learn3r/summ_screen_fd_memsum_bp
|
[
"region:us"
] |
2023-09-13T19:22:51+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7002624, "num_examples": 3673}, {"name": "validation", "num_bytes": 676928, "num_examples": 338}, {"name": "test", "num_bytes": 717198, "num_examples": 337}], "download_size": 410312, "dataset_size": 8396750}}
|
2023-09-26T09:36:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "summ_screen_fd_memsum_bp"
More Information needed
|
[
"# Dataset Card for \"summ_screen_fd_memsum_bp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"summ_screen_fd_memsum_bp\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"summ_screen_fd_memsum_bp\"\n\nMore Information needed"
] |
2011b78e4fc281c2cb47fa1f71aaddb789d6cd83
|
# Dataset Card for IncelSet
### Dataset Summary
This dataset is based off the incels.is forum and is ⚠️HIGHLY OFFENSIVE⚠️
A compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective.
Co-Authored by inmate & curly for Universiteit van Amsterdam
[Politics, Psychology, Law and Economics (PPLE)](https://pple.uva.nl)
### Languages
English with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended.
## Dataset Structure
The dataset consists of 2 colums, "title" - representing the thread title & "text" - representing the user replies (posts) under the thread title
### Source Data
Incels.is Forum.
#### Initial Data Collection and Normalization
1. We first built a script in GoLang that scrapes all the content of the incel.is Forum.
We downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores.
2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards.
3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data.
4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions.
5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet.
6. Final results yield approx 1bil characters on ~144k rows.
#### Who are the source language producers?
Self-described incels / members of the incels.is website (not to be taken in the mot-a-mot sense of the word)
### Personal and Sensitive Information
Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects.
## Considerations for Using the Data
Go wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community.
We have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project.
We will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so.
### Social Impact of Dataset
Public Awareness and Education:
Pro: Publishing a dataset might bring greater public awareness to the issue and could be used for educational purposes, enlightening people about the intricacies of this community. Greater understanding might foster empathy and encourage supportive interventions.
Con: It might also inadvertently glamorize or sensationalize the community, leading to an increased interest in and potential growth of such ideologies.
Source: Marwick, A., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543-559.
Potential Stigmatization and Alienation:
Pro: Identifying problematic behaviors and attitudes can help professionals develop targeted interventions.
Con: Generalizing or pathologizing the behaviors of this community might further stigmatize and alienate its members. Labeling can reinforce undesirable behavior if individuals internalize these negative identities.
Source: Dovidio, J. F., Major, B., & Crocker, J. (2000). Stigma: Introduction and overview. In T. F. Heatherton, R. E. Kleck, M. R. Hebl, & J. G. Hull (Eds.), The social psychology of stigma (p. 1–28).
Misuse of Data:
Pro: When used responsibly, such a dataset can be a treasure trove for academic research.
Con: However, there's always a risk of data being misused, misinterpreted, or cherry-picked to support harmful narratives or agendas.
Source: boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.
Ethical Concerns:
Pro: Revealing problematic beliefs might serve a greater good.
Con: There are ethical concerns, especially if data was collected without consent. Respect for individuals' autonomy and privacy is paramount in research ethics. (Data is collected under anonymity from a free-to-view, no-signup required, non-scrape blocking Forum - as per their ToS)
Source: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research.
Psychological Impact on Incels:
Pro: Confronting one's views might lead to self-reflection and change.
Con: Conversely, it might entrench their beliefs further if they feel attacked or misunderstood, a phenomenon supported by the backfire effect.
Source: Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330.
### Discussion of Biases
The authors compiled only the first 150.000 of the 270.000 threads in the "Inceldom discussion" part of the forum. As a consequence, older posts have been left out and the dataset may not thoroughly represent the full extent of incel discourse. The authors declare no further biases or conflicts of interest - the data was scraped and processed as it appears on the forum.
|
187ro/incelset
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T19:45:08+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "fill-mask"], "pretty_name": "Incel Dataset \ud83c\udfad", "tags": ["not-for-all-audiences"]}
|
2023-10-30T12:51:07+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-English #license-apache-2.0 #not-for-all-audiences #region-us
|
# Dataset Card for IncelSet
### Dataset Summary
This dataset is based off the URL forum and is ️HIGHLY OFFENSIVE️
A compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective.
Co-Authored by inmate & curly for Universiteit van Amsterdam
Politics, Psychology, Law and Economics (PPLE)
### Languages
English with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended.
## Dataset Structure
The dataset consists of 2 colums, "title" - representing the thread title & "text" - representing the user replies (posts) under the thread title
### Source Data
URL Forum.
#### Initial Data Collection and Normalization
1. We first built a script in GoLang that scrapes all the content of the URL Forum.
We downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores.
2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards.
3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data.
4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions.
5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet.
6. Final results yield approx 1bil characters on ~144k rows.
#### Who are the source language producers?
Self-described incels / members of the URL website (not to be taken in the mot-a-mot sense of the word)
### Personal and Sensitive Information
Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects.
## Considerations for Using the Data
Go wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community.
We have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project.
We will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so.
### Social Impact of Dataset
Public Awareness and Education:
Pro: Publishing a dataset might bring greater public awareness to the issue and could be used for educational purposes, enlightening people about the intricacies of this community. Greater understanding might foster empathy and encourage supportive interventions.
Con: It might also inadvertently glamorize or sensationalize the community, leading to an increased interest in and potential growth of such ideologies.
Source: Marwick, A., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543-559.
Potential Stigmatization and Alienation:
Pro: Identifying problematic behaviors and attitudes can help professionals develop targeted interventions.
Con: Generalizing or pathologizing the behaviors of this community might further stigmatize and alienate its members. Labeling can reinforce undesirable behavior if individuals internalize these negative identities.
Source: Dovidio, J. F., Major, B., & Crocker, J. (2000). Stigma: Introduction and overview. In T. F. Heatherton, R. E. Kleck, M. R. Hebl, & J. G. Hull (Eds.), The social psychology of stigma (p. 1–28).
Misuse of Data:
Pro: When used responsibly, such a dataset can be a treasure trove for academic research.
Con: However, there's always a risk of data being misused, misinterpreted, or cherry-picked to support harmful narratives or agendas.
Source: boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.
Ethical Concerns:
Pro: Revealing problematic beliefs might serve a greater good.
Con: There are ethical concerns, especially if data was collected without consent. Respect for individuals' autonomy and privacy is paramount in research ethics. (Data is collected under anonymity from a free-to-view, no-signup required, non-scrape blocking Forum - as per their ToS)
Source: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research.
Psychological Impact on Incels:
Pro: Confronting one's views might lead to self-reflection and change.
Con: Conversely, it might entrench their beliefs further if they feel attacked or misunderstood, a phenomenon supported by the backfire effect.
Source: Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330.
### Discussion of Biases
The authors compiled only the first 150.000 of the 270.000 threads in the "Inceldom discussion" part of the forum. As a consequence, older posts have been left out and the dataset may not thoroughly represent the full extent of incel discourse. The authors declare no further biases or conflicts of interest - the data was scraped and processed as it appears on the forum.
|
[
"# Dataset Card for IncelSet",
"### Dataset Summary\n\nThis dataset is based off the URL forum and is ️HIGHLY OFFENSIVE️\nA compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective.\n\nCo-Authored by inmate & curly for Universiteit van Amsterdam \nPolitics, Psychology, Law and Economics (PPLE)",
"### Languages\n\nEnglish with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended.",
"## Dataset Structure\n\nThe dataset consists of 2 colums, \"title\" - representing the thread title & \"text\" - representing the user replies (posts) under the thread title",
"### Source Data\n\nURL Forum.",
"#### Initial Data Collection and Normalization\n\n1. We first built a script in GoLang that scrapes all the content of the URL Forum.\nWe downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores.\n2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards. \n3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data.\n4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions.\n5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet.\n6. Final results yield approx 1bil characters on ~144k rows.",
"#### Who are the source language producers?\n\nSelf-described incels / members of the URL website (not to be taken in the mot-a-mot sense of the word)",
"### Personal and Sensitive Information\n\n Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects.",
"## Considerations for Using the Data\n\nGo wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community.\nWe have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project.\nWe will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so.",
"### Social Impact of Dataset\n\nPublic Awareness and Education:\n\nPro: Publishing a dataset might bring greater public awareness to the issue and could be used for educational purposes, enlightening people about the intricacies of this community. Greater understanding might foster empathy and encourage supportive interventions.\nCon: It might also inadvertently glamorize or sensationalize the community, leading to an increased interest in and potential growth of such ideologies.\nSource: Marwick, A., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543-559.\n\nPotential Stigmatization and Alienation:\n\nPro: Identifying problematic behaviors and attitudes can help professionals develop targeted interventions.\nCon: Generalizing or pathologizing the behaviors of this community might further stigmatize and alienate its members. Labeling can reinforce undesirable behavior if individuals internalize these negative identities.\nSource: Dovidio, J. F., Major, B., & Crocker, J. (2000). Stigma: Introduction and overview. In T. F. Heatherton, R. E. Kleck, M. R. Hebl, & J. G. Hull (Eds.), The social psychology of stigma (p. 1–28).\nMisuse of Data:\n\nPro: When used responsibly, such a dataset can be a treasure trove for academic research.\nCon: However, there's always a risk of data being misused, misinterpreted, or cherry-picked to support harmful narratives or agendas.\nSource: boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.\n\nEthical Concerns:\n\nPro: Revealing problematic beliefs might serve a greater good.\nCon: There are ethical concerns, especially if data was collected without consent. Respect for individuals' autonomy and privacy is paramount in research ethics. (Data is collected under anonymity from a free-to-view, no-signup required, non-scrape blocking Forum - as per their ToS)\nSource: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research.\n\nPsychological Impact on Incels:\n\nPro: Confronting one's views might lead to self-reflection and change.\nCon: Conversely, it might entrench their beliefs further if they feel attacked or misunderstood, a phenomenon supported by the backfire effect.\nSource: Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330.",
"### Discussion of Biases\n\nThe authors compiled only the first 150.000 of the 270.000 threads in the \"Inceldom discussion\" part of the forum. As a consequence, older posts have been left out and the dataset may not thoroughly represent the full extent of incel discourse. The authors declare no further biases or conflicts of interest - the data was scraped and processed as it appears on the forum."
] |
[
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-English #license-apache-2.0 #not-for-all-audiences #region-us \n",
"# Dataset Card for IncelSet",
"### Dataset Summary\n\nThis dataset is based off the URL forum and is ️HIGHLY OFFENSIVE️\nA compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective.\n\nCo-Authored by inmate & curly for Universiteit van Amsterdam \nPolitics, Psychology, Law and Economics (PPLE)",
"### Languages\n\nEnglish with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended.",
"## Dataset Structure\n\nThe dataset consists of 2 colums, \"title\" - representing the thread title & \"text\" - representing the user replies (posts) under the thread title",
"### Source Data\n\nURL Forum.",
"#### Initial Data Collection and Normalization\n\n1. We first built a script in GoLang that scrapes all the content of the URL Forum.\nWe downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores.\n2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards. \n3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data.\n4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions.\n5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet.\n6. Final results yield approx 1bil characters on ~144k rows.",
"#### Who are the source language producers?\n\nSelf-described incels / members of the URL website (not to be taken in the mot-a-mot sense of the word)",
"### Personal and Sensitive Information\n\n Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects.",
"## Considerations for Using the Data\n\nGo wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community.\nWe have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project.\nWe will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so.",
"### Social Impact of Dataset\n\nPublic Awareness and Education:\n\nPro: Publishing a dataset might bring greater public awareness to the issue and could be used for educational purposes, enlightening people about the intricacies of this community. Greater understanding might foster empathy and encourage supportive interventions.\nCon: It might also inadvertently glamorize or sensationalize the community, leading to an increased interest in and potential growth of such ideologies.\nSource: Marwick, A., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543-559.\n\nPotential Stigmatization and Alienation:\n\nPro: Identifying problematic behaviors and attitudes can help professionals develop targeted interventions.\nCon: Generalizing or pathologizing the behaviors of this community might further stigmatize and alienate its members. Labeling can reinforce undesirable behavior if individuals internalize these negative identities.\nSource: Dovidio, J. F., Major, B., & Crocker, J. (2000). Stigma: Introduction and overview. In T. F. Heatherton, R. E. Kleck, M. R. Hebl, & J. G. Hull (Eds.), The social psychology of stigma (p. 1–28).\nMisuse of Data:\n\nPro: When used responsibly, such a dataset can be a treasure trove for academic research.\nCon: However, there's always a risk of data being misused, misinterpreted, or cherry-picked to support harmful narratives or agendas.\nSource: boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662-679.\n\nEthical Concerns:\n\nPro: Revealing problematic beliefs might serve a greater good.\nCon: There are ethical concerns, especially if data was collected without consent. Respect for individuals' autonomy and privacy is paramount in research ethics. (Data is collected under anonymity from a free-to-view, no-signup required, non-scrape blocking Forum - as per their ToS)\nSource: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research.\n\nPsychological Impact on Incels:\n\nPro: Confronting one's views might lead to self-reflection and change.\nCon: Conversely, it might entrench their beliefs further if they feel attacked or misunderstood, a phenomenon supported by the backfire effect.\nSource: Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303-330.",
"### Discussion of Biases\n\nThe authors compiled only the first 150.000 of the 270.000 threads in the \"Inceldom discussion\" part of the forum. As a consequence, older posts have been left out and the dataset may not thoroughly represent the full extent of incel discourse. The authors declare no further biases or conflicts of interest - the data was scraped and processed as it appears on the forum."
] |
[
61,
9,
126,
40,
44,
7,
278,
41,
48,
99,
648,
96
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-100K<n<1M #language-English #license-apache-2.0 #not-for-all-audiences #region-us \n# Dataset Card for IncelSet### Dataset Summary\n\nThis dataset is based off the URL forum and is ️HIGHLY OFFENSIVE️\nA compilation of almost 3 years worth of posts, highlighting topics such as (self-described) celibatism, self-views, life-improvement (attempts or advice), suicide, perceived failure, views on women, views on society, views on politcs - from the members' perspective.\n\nCo-Authored by inmate & curly for Universiteit van Amsterdam \nPolitics, Psychology, Law and Economics (PPLE)### Languages\n\nEnglish with a lot of racial slurs, misoginy, mentions of sexual assault and general hatred - do not view or use if easily offended.## Dataset Structure\n\nThe dataset consists of 2 colums, \"title\" - representing the thread title & \"text\" - representing the user replies (posts) under the thread title### Source Data\n\nURL Forum.",
"passage: #### Initial Data Collection and Normalization\n\n1. We first built a script in GoLang that scrapes all the content of the URL Forum.\nWe downloaded roughly 150.000 threads - containing almost 2.1 Million posts - in approximately 9 hours from start to finish - using a dedicated server with 72 cores.\n2. We then took the scraped data and started processing it, firstly building a script in Python that processed the data & formatted it into the JSON data format according to (RFC 8259) standards. \n3. We then started the removal process of PII (Personal Identifiable Information) - thus anonymizing user posts in the dataset. This wasn't hard to do as users already set up monikers for themselves & never gave out personal information such as full names, addresses or social security numbers, nevertheless we still validated the removal of such data.\n4. We then proceeded to remove leftover non-human readable text such as HTML tags or base64 encodings, along URLs users may have posted in their discussions.\n5. We now begin the dataset formatting process of compiling all 143.501 files left (threads) & ~2.1M posts in Parquet.\n6. Final results yield approx 1bil characters on ~144k rows.#### Who are the source language producers?\n\nSelf-described incels / members of the URL website (not to be taken in the mot-a-mot sense of the word)### Personal and Sensitive Information\n\n Includes details of the users' (tragic & tragically self-perceived) lifes. No personal information contained in itself but touches on many sensitive subjects.## Considerations for Using the Data\n\nGo wild with it. Keep in mind that we are not trying to expose, radicalize or even remotely harm this community.\nWe have compiled almost 3 years worth of posts on this forum so we could better study this phenomena for a University project.\nWe will be taking into consideration the actual publishing of the model trained on this data, but we do not see a potential scientific gain that would convince us to do so."
] |
881cde0b28c38945e619202b017be3c86c648a91
|
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Weyaxi/Luban-Marcoroni-13B-v2](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-28T11:01:27.302979](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v2/blob/main/results_2023-10-28T11-01-27.302979.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": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10253880033557114,
"f1_stderr": 0.001982157556823196,
"acc": 0.4344259989839472,
"acc_stderr": 0.010037121788760327
},
"harness|drop|3": {
"em": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10253880033557114,
"f1_stderr": 0.001982157556823196
},
"harness|gsm8k|5": {
"acc": 0.09931766489764973,
"acc_stderr": 0.008238371412683973
},
"harness|winogrande|5": {
"acc": 0.7695343330702447,
"acc_stderr": 0.011835872164836682
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v2
|
[
"region:us"
] |
2023-09-13T19:55:01+00:00
|
{"pretty_name": "Evaluation run of Weyaxi/Luban-Marcoroni-13B-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/Luban-Marcoroni-13B-v2](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-28T11:01:27.302979](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v2/blob/main/results_2023-10-28T11-01-27.302979.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\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10253880033557114,\n \"f1_stderr\": 0.001982157556823196,\n \"acc\": 0.4344259989839472,\n \"acc_stderr\": 0.010037121788760327\n },\n \"harness|drop|3\": {\n \"em\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10253880033557114,\n \"f1_stderr\": 0.001982157556823196\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \"acc_stderr\": 0.008238371412683973\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836682\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|arc:challenge|25_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_28T11_01_27.302979", "path": ["**/details_harness|drop|3_2023-10-28T11-01-27.302979.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-28T11-01-27.302979.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_28T11_01_27.302979", "path": ["**/details_harness|gsm8k|5_2023-10-28T11-01-27.302979.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-28T11-01-27.302979.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hellaswag|10_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-09-13T20-54-44.969205.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-13T20-54-44.969205.parquet", 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"path": ["**/details_harness|hendrycksTest-marketing|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": 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"latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": 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["**/details_harness|truthfulqa:mc|0_2023-09-13T20-54-44.969205.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-09-13T20-54-44.969205.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_28T11_01_27.302979", "path": ["**/details_harness|winogrande|5_2023-10-28T11-01-27.302979.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-28T11-01-27.302979.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_13T20_54_44.969205", "path": ["results_2023-09-13T20-54-44.969205.parquet"]}, {"split": "2023_10_28T11_01_27.302979", "path": ["results_2023-10-28T11-01-27.302979.parquet"]}, {"split": "latest", "path": ["results_2023-10-28T11-01-27.302979.parquet"]}]}]}
|
2023-10-28T10:01:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-28T11:01:27.302979(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T11:01:27.302979(note that their might be results for other tasks in 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",
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"### 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 Weyaxi/Luban-Marcoroni-13B-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-28T11:01:27.302979(note that their might be results for other tasks in 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",
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"### 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 Weyaxi/Luban-Marcoroni-13B-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-28T11:01:27.302979(note that their might be results for other tasks in 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"
] |
129053b4ce01c1cd970771178eca42efa77fdeac
|
# Dataset of igarashi_kyouko/五十嵐響子 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of igarashi_kyouko/五十嵐響子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `brown_hair, long_hair, brown_eyes, side_ponytail, bangs, bow, breasts, 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 | 500 | 580.99 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igarashi_kyouko_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 374.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igarashi_kyouko_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1194 | 776.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igarashi_kyouko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 534.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/igarashi_kyouko_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1194 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/igarashi_kyouko_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/igarashi_kyouko_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | blue_sky, blush, cleavage, cloud, collarbone, day, halterneck, navel, outdoors, looking_at_viewer, open_mouth, solo_focus, white_bikini, 2girls, 3girls, :d, bare_shoulders, sitting, braid, floral_print, ocean, short_hair, side-tie_bikini_bottom, stomach, thigh_gap, underboob |
| 1 | 24 |  |  |  |  |  | cleavage, navel, outdoors, smile, 1girl, looking_at_viewer, blush, solo, cloud, day, open_mouth, collarbone, blue_sky, ocean, hair_flower, front-tie_top, cowboy_shot, yellow_bikini, beach, one_eye_closed, water |
| 2 | 10 |  |  |  |  |  | 1girl, solo, blush, dress, looking_at_viewer, necklace, smile, simple_background, open_mouth, white_background |
| 3 | 10 |  |  |  |  |  | 1girl, bare_shoulders, blush, looking_at_viewer, necklace, solo, collarbone, pink_dress, :d, frilled_dress, open_mouth, strapless_dress, earrings, hair_flower, pink_bow, white_gloves, pink_flower, simple_background, white_background, high_heels, pink_footwear, pink_gloves, rose |
| 4 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, open_mouth, skirt, thighhighs, :d |
| 5 | 16 |  |  |  |  |  | looking_at_viewer, school_uniform, plaid_skirt, smile, 1girl, blazer, blush, love_letter, kneehighs, solo_focus, multiple_girls, open_mouth, bowtie, one_side_up |
| 6 | 7 |  |  |  |  |  | 1girl, blush, long_sleeves, open_jacket, plaid_skirt, school_uniform, solo, white_shirt, blazer, blue_skirt, looking_at_viewer, center_frills, dress_shirt, holding_letter, love_letter, pleated_skirt, smile, red_bowtie, blunt_bangs, brown_jacket, envelope, heart, indoors, plaid_bow |
| 7 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, collarbone, open_mouth, hair_scrunchie, cleavage, holding, :d, naked_apron, pink_scrunchie, ladle, large_breasts, white_background |
| 8 | 11 |  |  |  |  |  | 1girl, blush, floral_print, looking_at_viewer, open_mouth, :d, print_kimono, hair_flower, pink_kimono, upper_body, blunt_bangs, collarbone, obi, outdoors, solo_focus, sparkle, wide_sleeves, yukata |
| 9 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, looking_at_viewer, penis, solo_focus, sweat, handjob, mosaic_censoring, nipples, nude, collarbone, heart, pov, smile |
| 10 | 5 |  |  |  |  |  | 1girl, bed_sheet, blush, looking_at_viewer, on_back, solo, dakimakura_(medium), open_mouth, pink_bow, pink_panties, plaid, smile, armpits, arms_up, ass_visible_through_thighs, navel, pink_bra, skirt, ;d, bow_panties, bra_removed, cleavage, collarbone, full_body, groin, heart_hands, nipples, one_eye_closed, open_shirt, panty_pull, sleeveless, topless |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blue_sky | blush | cleavage | cloud | collarbone | day | halterneck | navel | outdoors | looking_at_viewer | open_mouth | solo_focus | white_bikini | 2girls | 3girls | :d | bare_shoulders | sitting | braid | floral_print | ocean | short_hair | side-tie_bikini_bottom | stomach | thigh_gap | underboob | smile | 1girl | solo | hair_flower | front-tie_top | cowboy_shot | yellow_bikini | beach | one_eye_closed | water | dress | necklace | simple_background | white_background | pink_dress | frilled_dress | strapless_dress | earrings | pink_bow | white_gloves | pink_flower | high_heels | pink_footwear | pink_gloves | rose | skirt | thighhighs | school_uniform | plaid_skirt | blazer | love_letter | kneehighs | multiple_girls | bowtie | one_side_up | long_sleeves | open_jacket | white_shirt | blue_skirt | center_frills | dress_shirt | holding_letter | pleated_skirt | red_bowtie | blunt_bangs | brown_jacket | envelope | heart | indoors | plaid_bow | hair_scrunchie | holding | naked_apron | pink_scrunchie | ladle | large_breasts | print_kimono | pink_kimono | upper_body | obi | sparkle | wide_sleeves | yukata | 1boy | hetero | penis | sweat | handjob | mosaic_censoring | nipples | nude | pov | bed_sheet | on_back | dakimakura_(medium) | pink_panties | plaid | armpits | arms_up | ass_visible_through_thighs | pink_bra | ;d | bow_panties | bra_removed | full_body | groin | heart_hands | open_shirt | panty_pull | sleeveless | topless |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------|:--------|:-----------|:--------|:-------------|:------|:-------------|:--------|:-----------|:--------------------|:-------------|:-------------|:---------------|:---------|:---------|:-----|:-----------------|:----------|:--------|:---------------|:--------|:-------------|:-------------------------|:----------|:------------|:------------|:--------|:--------|:-------|:--------------|:----------------|:--------------|:----------------|:--------|:-----------------|:--------|:--------|:-----------|:--------------------|:-------------------|:-------------|:----------------|:------------------|:-----------|:-----------|:---------------|:--------------|:-------------|:----------------|:--------------|:-------|:--------|:-------------|:-----------------|:--------------|:---------|:--------------|:------------|:-----------------|:---------|:--------------|:---------------|:--------------|:--------------|:-------------|:----------------|:--------------|:-----------------|:----------------|:-------------|:--------------|:---------------|:-----------|:--------|:----------|:------------|:-----------------|:----------|:--------------|:-----------------|:--------|:----------------|:---------------|:--------------|:-------------|:------|:----------|:---------------|:---------|:-------|:---------|:--------|:--------|:----------|:-------------------|:----------|:-------|:------|:------------|:----------|:----------------------|:---------------|:--------|:----------|:----------|:-----------------------------|:-----------|:-----|:--------------|:--------------|:------------|:--------|:--------------|:-------------|:-------------|:-------------|:----------|
| 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 24 |  |  |  |  |  | X | X | X | X | X | X | | X | X | X | X | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | | X | | | | | | | | X | X | | | | | | | | | | | | | | | | X | X | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 10 |  |  |  |  |  | | X | | | X | | | | | X | X | | | | | X | X | | | | | | | | | | | X | X | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | | X | | | | | | | | X | X | | | | | X | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 16 |  |  |  |  |  | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 13 |  |  |  |  |  | | X | X | | X | | | | | X | X | | | | | X | | | | | | | | | | | | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | | X | | | X | | | | X | X | X | X | | | | X | | | | X | | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 6 |  |  |  |  |  | | X | | | X | | | | | X | | X | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | | X | X | | X | | | X | | X | X | | | | | | | | | | | | | | | | X | X | X | | | | | | X | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/igarashi_kyouko_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T20:25:23+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T16:23:08+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of igarashi\_kyouko/五十嵐響子 (THE iDOLM@STER: Cinderella Girls)
====================================================================
This is the dataset of igarashi\_kyouko/五十嵐響子 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'brown\_hair, long\_hair, brown\_eyes, side\_ponytail, bangs, bow, breasts, 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"
] |
f7731a266020fb7e107cc46b71e8bdad3c8a89c1
|
# Dataset of akagi_miria/赤城みりあ (THE iDOLM@STER: Cinderella Girls)
This is the dataset of akagi_miria/赤城みりあ (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `two_side_up, brown_eyes, short_hair, black_hair, bangs, brown_hair, 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 | 500 | 571.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_miria_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 345.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_miria_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1144 | 726.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_miria_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 511.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_miria_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1144 | 1010.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akagi_miria_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/akagi_miria_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, open_mouth, solo, blush, :d, looking_at_viewer, skirt |
| 1 | 10 |  |  |  |  |  | hair_bobbles, midriff, navel, skirt, 1girl, detached_collar, mismatched_legwear, open_mouth, solo, thighhighs, bare_shoulders, blush, looking_at_viewer, :d, star_(symbol), wrist_cuffs |
| 2 | 10 |  |  |  |  |  | 1girl, blue_dress, maid_apron, maid_headdress, blush, puffy_short_sleeves, red_bow, solo, white_apron, looking_at_viewer, open_mouth, wrist_cuffs, frilled_apron, ribbon, white_background, :d, bowtie, hair_between_eyes, mary_janes, simple_background, white_thighhighs, black_footwear |
| 3 | 18 |  |  |  |  |  | 1girl, blush, double_bun, hair_bow, hairclip, long_sleeves, looking_at_viewer, solo, open_mouth, hood_down, necklace, star_hair_ornament, animal_bag, drawstring, hooded_jacket, x_hair_ornament, belt_buckle, hair_between_eyes, sneakers, blue_shorts, heart_hair_ornament, multicolored_clothes, open_jacket, short_shorts, shoulder_bag, beads, collarbone, pantyhose, plaid, yellow_shirt, :d, loose_socks, simple_background, white_background, fur-trimmed_shorts, one_eye_closed, pink_bow, sleeves_past_wrists, star_print, striped |
| 4 | 7 |  |  |  |  |  | 1girl, flat_chest, micro_bikini, navel, looking_at_viewer, open_mouth, solo, :d, loli, side-tie_bikini_bottom, blush, simple_background, dated, standing, white_background, white_bikini |
| 5 | 6 |  |  |  |  |  | 1girl, black_gloves, blue_dress, earrings, hair_bow, solo, looking_at_viewer, smile, blush, bracelet, choker, hairclip, bare_shoulders, blue_bow, collarbone, flower, simple_background |
| 6 | 5 |  |  |  |  |  | 1girl, blush, hetero, huge_breasts, oppai_loli, 1boy, lactation, nipples, open_mouth, alternate_breast_size, breast_grab, grabbing, navel, serafuku, shirt_lift, skirt, faceless_male, multiple_boys, smile, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | open_mouth | solo | blush | :d | looking_at_viewer | skirt | hair_bobbles | midriff | navel | detached_collar | mismatched_legwear | thighhighs | bare_shoulders | star_(symbol) | wrist_cuffs | blue_dress | maid_apron | maid_headdress | puffy_short_sleeves | red_bow | white_apron | frilled_apron | ribbon | white_background | bowtie | hair_between_eyes | mary_janes | simple_background | white_thighhighs | black_footwear | double_bun | hair_bow | hairclip | long_sleeves | hood_down | necklace | star_hair_ornament | animal_bag | drawstring | hooded_jacket | x_hair_ornament | belt_buckle | sneakers | blue_shorts | heart_hair_ornament | multicolored_clothes | open_jacket | short_shorts | shoulder_bag | beads | collarbone | pantyhose | plaid | yellow_shirt | loose_socks | fur-trimmed_shorts | one_eye_closed | pink_bow | sleeves_past_wrists | star_print | striped | flat_chest | micro_bikini | loli | side-tie_bikini_bottom | dated | standing | white_bikini | black_gloves | earrings | smile | bracelet | choker | blue_bow | flower | hetero | huge_breasts | oppai_loli | 1boy | lactation | nipples | alternate_breast_size | breast_grab | grabbing | serafuku | shirt_lift | faceless_male | multiple_boys | solo_focus |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------|:--------|:-----|:--------------------|:--------|:---------------|:----------|:--------|:------------------|:---------------------|:-------------|:-----------------|:----------------|:--------------|:-------------|:-------------|:-----------------|:----------------------|:----------|:--------------|:----------------|:---------|:-------------------|:---------|:--------------------|:-------------|:--------------------|:-------------------|:-----------------|:-------------|:-----------|:-----------|:---------------|:------------|:-----------|:---------------------|:-------------|:-------------|:----------------|:------------------|:--------------|:-----------|:--------------|:----------------------|:-----------------------|:--------------|:---------------|:---------------|:--------|:-------------|:------------|:--------|:---------------|:--------------|:---------------------|:-----------------|:-----------|:----------------------|:-------------|:----------|:-------------|:---------------|:-------|:-------------------------|:--------|:-----------|:---------------|:---------------|:-----------|:--------|:-----------|:---------|:-----------|:---------|:---------|:---------------|:-------------|:-------|:------------|:----------|:------------------------|:--------------|:-----------|:-----------|:-------------|:----------------|:----------------|:-------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 18 |  |  |  |  |  | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | X | | X | | X | | | X | X | X | X | X | X | X | 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 | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | | X | X | | X | | | | | | | | X | | | X | | | | | | | | | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/akagi_miria_idolmastercinderellagirls
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-13T20:27:26+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2024-01-16T13:53:19+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of akagi\_miria/赤城みりあ (THE iDOLM@STER: Cinderella Girls)
================================================================
This is the dataset of akagi\_miria/赤城みりあ (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are 'two\_side\_up, brown\_eyes, short\_hair, black\_hair, bangs, brown\_hair, 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"
] |
b8b7667d452b5f3ac16fd77b84823d00599e2656
|
PLSC - Polish Library of Science Corpus
|
rafalposwiata/plsc
|
[
"task_categories:text-classification",
"task_ids:topic-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:pl",
"license:cc0-1.0",
"region:us"
] |
2023-09-13T20:29:25+00:00
|
{"language": ["pl"], "license": "cc0-1.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "task_ids": ["topic-classification", "multi-class-classification", "multi-label-classification"]}
|
2023-12-04T15:02:08+00:00
|
[] |
[
"pl"
] |
TAGS
#task_categories-text-classification #task_ids-topic-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #multilinguality-monolingual #size_categories-100K<n<1M #language-Polish #license-cc0-1.0 #region-us
|
PLSC - Polish Library of Science Corpus
|
[] |
[
"TAGS\n#task_categories-text-classification #task_ids-topic-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #multilinguality-monolingual #size_categories-100K<n<1M #language-Polish #license-cc0-1.0 #region-us \n"
] |
[
84
] |
[
"passage: TAGS\n#task_categories-text-classification #task_ids-topic-classification #task_ids-multi-class-classification #task_ids-multi-label-classification #multilinguality-monolingual #size_categories-100K<n<1M #language-Polish #license-cc0-1.0 #region-us \n"
] |
6c4e7f87882980346f2b8c86c9c618939c442a8e
|
Dataset sintético creado tomando cada artículo de los códigos y constituciones argentinas, formulando preguntas con marianbasti/Llama-2-13b-alpaca-spanish
Las categorias presentes son:
Aduanero
Aeronautico
Civil-y-Comercial
de-Mineria
Electoral
Penal
Procesal-Civil-y-Comercial
Procesal-Penal
Procesal-Penal-Federal
Buenos-Aires
Catamarca
Chaco
Chubut
Ciudad-Autonoma-de-Buenos-Aires
Cordoba
Corrientes
Entre-Rios
Formosa
Jujuy
La-Pampa
La-Rioja
Mendoza
Misiones
Nacional
Neuquen
Rio-Negro
Salta
San-Juan
San-Luis
Santa-Cruz
Santa-Fe
Santiago-del-Estero
Tierra-del-Fuego-Antartida-Islas-Atlantico-Sur
Tucuman
|
marianbasti/argentina_codes-constitutions_questions_labels
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:es",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-09-13T20:40:19+00:00
|
{"language": ["es"], "license": "cc-by-nc-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]}
|
2023-09-14T01:56:16+00:00
|
[] |
[
"es"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-Spanish #license-cc-by-nc-4.0 #region-us
|
Dataset sintético creado tomando cada artículo de los códigos y constituciones argentinas, formulando preguntas con marianbasti/Llama-2-13b-alpaca-spanish
Las categorias presentes son:
Aduanero
Aeronautico
Civil-y-Comercial
de-Mineria
Electoral
Penal
Procesal-Civil-y-Comercial
Procesal-Penal
Procesal-Penal-Federal
Buenos-Aires
Catamarca
Chaco
Chubut
Ciudad-Autonoma-de-Buenos-Aires
Cordoba
Corrientes
Entre-Rios
Formosa
Jujuy
La-Pampa
La-Rioja
Mendoza
Misiones
Nacional
Neuquen
Rio-Negro
Salta
San-Juan
San-Luis
Santa-Cruz
Santa-Fe
Santiago-del-Estero
Tierra-del-Fuego-Antartida-Islas-Atlantico-Sur
Tucuman
|
[] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Spanish #license-cc-by-nc-4.0 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Spanish #license-cc-by-nc-4.0 #region-us \n"
] |
e01045bba821b3db5856aca94fcb0cb0ee454780
|
# Dataset Card for AttributionBench
- Repository: https://huggingface.co/datasets/osunlp/AttributionBench
- Paper: Link is coming soon
- Point of Contact: <a href="mailto:[email protected]">Yifei Li</a>
# Dataset Overview
We constructed this dataset from multiple existing data sources in a unified format, in order to create a unified and diverse testbed for evaluating advanced attribution evaluation systems. The dataset contains both in-domain training set and id-domain and out-of-domain test set.
## Usage
```python
import datasets
features = datasets.Features({
'question': datasets.Value('string'),
'claim': datasets.Value('string'),
'claim_raw_string': datasets.Value('string'),
'response': datasets.Value('string'),
'references': datasets.Sequence(datasets.Value("string")),
'citation_links': datasets.Sequence(datasets.Value("string")),
'webpage_references': datasets.Sequence(datasets.Value("string")),
'attribution_label': datasets.Value('string'),
'src_dataset': datasets.Value('string'),
'id': datasets.Value('string'),
})
# in-domain train (subset-balanced)
# possible values for 'name' field: ["subset_balanced", "overall_balanced", "not_balanced", "full_data"]
dataset = datasets.load_dataset("osunlp/AttributionBench", name="subset_balanced", split="train", features=features)
# in-domain eval/test (subset-balanced)
# dataset = datasets.load_dataset("osunlp/AttributionBench", name="subset_balanced", split="test", features=features)
dataset = datasets.load_dataset("osunlp/AttributionBench", name="subset_balanced", split="test", features=features)
# out-of-domain test (subset-balanced)
dataset = datasets.load_dataset("osunlp/AttributionBench", name="subset_balanced", split="test_ood", features=features)
```
## Dataset Structure
### Data Instances
```json
{
"question":"Is the number of horses living on Easter Island twice the number of people?",
"claim":"According to James Grant-Peterkin in his book \u201cA Companion to Easter Island\u201d, there are almost 3,000 horses on Easter Island. However, locals often claim that they have more horses than people. The population of Easter Island is about 6,000 inhabitants. So it seems that the number of horses living on Easter Island is not twice the number of people.",
"claim_raw_string":"According to James Grant-Peterkin in his book \u201cA Companion to Easter Island\u201d, there are almost 3,000 horses on Easter Island. However, locals often claim that they have more horses than people. The population of Easter Island is about 6,000 inhabitants. So it seems that the number of horses living on Easter Island is not twice the number of people.",
"response":"According to James Grant-Peterkin in his book \u201cA Companion to Easter Island\u201d, there are almost 3,000 horses on Easter Island. However, locals often claim that they have more horses than people. The population of Easter Island is about 6,000 inhabitants. So it seems that the number of horses living on Easter Island is not twice the number of people.",
"references":[
"It is worth mentioning the huge population of horses (about 6,000) that already outnumber people and roam free on the island."
],
"citation_links":[],
"webpage_references":[],
"attribution_label":"not attributable",
"src_dataset":"AttrScore-GenSearch",
"id":"AttrScore-GenSearch_7234d6e9-1f51-4203-9587-f539e34d34f4"
}
```
### Data Fields
- ```question```: ```str``` The question proposed by the user.
- ```claim```: ```str``` Part of the response to the question. Could be one single sentence or multiple sentences.
- ```claim_raw_string```: ```str``` The raw string of the claim from the original datasets before being processed.
- ```response```: ```str``` The response to the question generated by LMs or generative search engines.
- ```references```: ```List[str]``` A list of documents or paragraphs which could support the claim.
- ```citation_links```: ```Optional[List[str]]``` Reserved field for citation links.
- ```webpage_references```: ```Optional[List[str]]``` Reserved field for the webpage contents of the reference links.
- ```attribution_label```: ```str``` "attributable" or "not attributable".
- ```src_dataset```: ```str``` The source dataset of the data item.
- ```id```: ```str``` The unique id for the data item in AttributionBench.
|
osunlp/AttributionBench
|
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-09-13T20:51:47+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "AttributionBench", "configs": [{"config_name": "subset_balanced", "description": "The subset balanced version of the dataset. Each sub-dataset contains the same number of attributable labels and not attributable labels.", "data_files": [{"split": "train", "path": "train_all_subset_balanced.jsonl"}, {"split": "dev", "path": "dev_all_subset_balanced.jsonl"}, {"split": "test", "path": "test_all_subset_balanced.jsonl"}, {"split": "test_ood", "path": "test_ood_all_subset_balanced.jsonl"}]}, {"config_name": "overall_balanced", "description": "The overall balanced version of the dataset. The whole set contains the same number of attributable labels and not attributable labels, but each sub-dataset does not.", "data_files": [{"split": "train", "path": "train_overall_balanced.jsonl"}, {"split": "dev", "path": "dev_all_subset_balanced.jsonl"}, {"split": "test", "path": "test_all_subset_balanced.jsonl"}, {"split": "test_ood", "path": "test_ood_all_subset_balanced.jsonl"}]}, {"config_name": "not_balanced", "description": "The not balanced version of the dataset. The label distribution is the same as full data which is not balanced, but the data scale is sampled as comparable with the two label balanced version.", "data_files": [{"split": "train", "path": "merged_train_sampled.jsonl"}, {"split": "dev", "path": "dev_all_subset_balanced.jsonl"}, {"split": "test", "path": "test_all_subset_balanced.jsonl"}, {"split": "test_ood", "path": "test_ood_all_subset_balanced.jsonl"}]}, {"config_name": "full_data", "description": "Full training data. The label distribution is not balanced.", "data_files": [{"split": "train", "path": "merged_train.jsonl"}, {"split": "dev", "path": "dev_all_subset_balanced.jsonl"}, {"split": "test", "path": "test_all_subset_balanced.jsonl"}, {"split": "test_ood", "path": "test_ood_all_subset_balanced.jsonl"}]}]}
|
2024-02-08T21:01:45+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us
|
# Dataset Card for AttributionBench
- Repository: URL
- Paper: Link is coming soon
- Point of Contact: <a href="mailto:li.14042@URL">Yifei Li</a>
# Dataset Overview
We constructed this dataset from multiple existing data sources in a unified format, in order to create a unified and diverse testbed for evaluating advanced attribution evaluation systems. The dataset contains both in-domain training set and id-domain and out-of-domain test set.
## Usage
## Dataset Structure
### Data Instances
### Data Fields
- : The question proposed by the user.
- : Part of the response to the question. Could be one single sentence or multiple sentences.
- : The raw string of the claim from the original datasets before being processed.
- : The response to the question generated by LMs or generative search engines.
- : A list of documents or paragraphs which could support the claim.
- : Reserved field for citation links.
- : Reserved field for the webpage contents of the reference links.
- : "attributable" or "not attributable".
- : The source dataset of the data item.
- : The unique id for the data item in AttributionBench.
|
[
"# Dataset Card for AttributionBench\n\n- Repository: URL\n- Paper: Link is coming soon\n- Point of Contact: <a href=\"mailto:li.14042@URL\">Yifei Li</a>",
"# Dataset Overview\nWe constructed this dataset from multiple existing data sources in a unified format, in order to create a unified and diverse testbed for evaluating advanced attribution evaluation systems. The dataset contains both in-domain training set and id-domain and out-of-domain test set.",
"## Usage",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n- : The question proposed by the user.\n- : Part of the response to the question. Could be one single sentence or multiple sentences.\n- : The raw string of the claim from the original datasets before being processed.\n- : The response to the question generated by LMs or generative search engines.\n- : A list of documents or paragraphs which could support the claim.\n- : Reserved field for citation links.\n- : Reserved field for the webpage contents of the reference links.\n- : \"attributable\" or \"not attributable\".\n- : The source dataset of the data item.\n- : The unique id for the data item in AttributionBench."
] |
[
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for AttributionBench\n\n- Repository: URL\n- Paper: Link is coming soon\n- Point of Contact: <a href=\"mailto:li.14042@URL\">Yifei Li</a>",
"# Dataset Overview\nWe constructed this dataset from multiple existing data sources in a unified format, in order to create a unified and diverse testbed for evaluating advanced attribution evaluation systems. The dataset contains both in-domain training set and id-domain and out-of-domain test set.",
"## Usage",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n- : The question proposed by the user.\n- : Part of the response to the question. Could be one single sentence or multiple sentences.\n- : The raw string of the claim from the original datasets before being processed.\n- : The response to the question generated by LMs or generative search engines.\n- : A list of documents or paragraphs which could support the claim.\n- : Reserved field for citation links.\n- : Reserved field for the webpage contents of the reference links.\n- : \"attributable\" or \"not attributable\".\n- : The source dataset of the data item.\n- : The unique id for the data item in AttributionBench."
] |
[
41,
48,
69,
3,
6,
6,
151
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# Dataset Card for AttributionBench\n\n- Repository: URL\n- Paper: Link is coming soon\n- Point of Contact: <a href=\"mailto:li.14042@URL\">Yifei Li</a># Dataset Overview\nWe constructed this dataset from multiple existing data sources in a unified format, in order to create a unified and diverse testbed for evaluating advanced attribution evaluation systems. The dataset contains both in-domain training set and id-domain and out-of-domain test set.## Usage## Dataset Structure### Data Instances### Data Fields\n- : The question proposed by the user.\n- : Part of the response to the question. Could be one single sentence or multiple sentences.\n- : The raw string of the claim from the original datasets before being processed.\n- : The response to the question generated by LMs or generative search engines.\n- : A list of documents or paragraphs which could support the claim.\n- : Reserved field for citation links.\n- : Reserved field for the webpage contents of the reference links.\n- : \"attributable\" or \"not attributable\".\n- : The source dataset of the data item.\n- : The unique id for the data item in AttributionBench."
] |
291388bac293511f45728281cc02765f34465455
|
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10252936241610805,
"f1_stderr": 0.0019829740048614144,
"acc": 0.4340313659926291,
"acc_stderr": 0.010044205768767243
},
"harness|drop|3": {
"em": 0.00776006711409396,
"em_stderr": 0.0008986296432392762,
"f1": 0.10252936241610805,
"f1_stderr": 0.0019829740048614144
},
"harness|gsm8k|5": {
"acc": 0.09931766489764973,
"acc_stderr": 0.008238371412683977
},
"harness|winogrande|5": {
"acc": 0.7687450670876085,
"acc_stderr": 0.01185004012485051
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3
|
[
"region:us"
] |
2023-09-13T21:12:42+00:00
|
{"pretty_name": "Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/Luban-Marcoroni-13B-v3](https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-29T14:08:44.787529](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Luban-Marcoroni-13B-v3/blob/main/results_2023-10-29T14-08-44.787529.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\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10252936241610805,\n \"f1_stderr\": 0.0019829740048614144,\n \"acc\": 0.4340313659926291,\n \"acc_stderr\": 0.010044205768767243\n },\n \"harness|drop|3\": {\n \"em\": 0.00776006711409396,\n \"em_stderr\": 0.0008986296432392762,\n \"f1\": 0.10252936241610805,\n \"f1_stderr\": 0.0019829740048614144\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \"acc_stderr\": 0.008238371412683977\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7687450670876085,\n \"acc_stderr\": 0.01185004012485051\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/Luban-Marcoroni-13B-v3", "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_13T22_12_25.570871", "path": ["**/details_harness|arc:challenge|25_2023-09-13T22-12-25.570871.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T22-12-25.570871.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_29T14_08_44.787529", "path": ["**/details_harness|drop|3_2023-10-29T14-08-44.787529.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-29T14-08-44.787529.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_29T14_08_44.787529", "path": ["**/details_harness|gsm8k|5_2023-10-29T14-08-44.787529.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-29T14-08-44.787529.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_09_13T22_12_25.570871", "path": ["**/details_harness|hellaswag|10_2023-09-13T22-12-25.570871.parquet"]}, {"split": "latest", "path": 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|
2023-10-29T14:08:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v3 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 2023-10-29T14:08:44.787529(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v3 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 2023-10-29T14:08:44.787529(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v3 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 2023-10-29T14:08:44.787529(note that their might be results for other tasks in 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,
24,
31,
172,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
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5,
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5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/Luban-Marcoroni-13B-v3## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Luban-Marcoroni-13B-v3 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 2023-10-29T14:08:44.787529(note that their might be results for other tasks in 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"
] |
aa7bb6c451baa2da5d282e224d9ee4f5e95631f5
|
SARFish is a [Synthetic Aperture Radar (SAR)](https://sentinel.esa.int/web/sentinel/missions/sentinel-1/instrument-payload) imagery dataset for the purpose of training, validating and testing supervised machine learning models on the tasks of ship detection, classification, and length regression. The SARFish dataset builds on the excellent work of the [xView3-SAR dataset](https://iuu.xview.us/dataset) (2021) and consists of two parts:
1. Data - Extends the xView3-SAR dataset to include [Single Look Complex (SLC)](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1-algorithms/single-look-complex) as well as [Ground Range Detected (GRD)](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar/products-algorithms/level-1-algorithms/ground-range-detected) imagery data taken directly from the European Space Agency (ESA) Copernicus Programme [Open Access Hub Website](https://scihub.copernicus.eu/).
2. Labels - Derives labels from the xView3-SAR dataset providing maritime object location, vessel classification and vessel length information.
### Quick Links
The following are links to the Kaggle competitions for each of the tracks of the SARFish challenge along with the SARFish dataset and GitHub repo:
- Data:
- [SARFish](https://huggingface.co/datasets/ConnorLuckettDSTG/SARFish)
- [SARFishSample](https://huggingface.co/datasets/ConnorLuckettDSTG/SARFishSample)
- [Labels](https://iuu.xview.us/download-links)
- Challenge:
- [Maritime Object Detection Track](https://www.kaggle.com/competitions/sarfish-maritime-object-detection)
- [Maritime Object Classification Track](https://www.kaggle.com/competitions/sarfish-maritime-object-classification)
- [Vessel Length Regression Track](https://www.kaggle.com/competitions/sarfish-vessel-length-regression)
- [GitHub repo](https://github.com/RitwikGupta/SARFish)
- [Mailbox]([email protected])
- [DAIRNet](https://www.dairnet.com.au/events/workshop-on-complex-valued-deep-learning-and-sarfish-challenge/)
The [GitHub repo](https://github.com/RitwikGupta/SARFish) describes how to:
- Download the dataset.
- Run the SARFish_demo jupyter notebook.
- Load imagery products and groundtruth labels,
- Train and evaluate a reference/baseline model using the dataset.
### Dataset summary - What does the SARFish dataset consist of?
The following table summarises the sizes of the full size and sample SARFish dataset.
| dataset | coincident GRD, SLC products | compressed (GB) | uncompressed (GB) |
| --- | --- | --- | --- |
| SARFishSample | 1 | 4.3 | 8.2 |
| SARFish | 753 | 3293 | 6468 |
The following table summarises the partitions of the dataset:
| Partition | Coincident products | Labels Provided | Unique maritime object labels | |
| --- | --- | --- | --- | --- |
| | | | SLC | GRD |
| train | 553 | True | 63071 | 64054 |
| validation | 50 | True | 18906 | 19222 |
| public | 150 | False | 58744 | 60008 |
| | | Total | 140721 | 143284 |
### How to access the SARFish dataset
The SARFish dataset is available for download at:
- [full SARFish dataset](https://huggingface.co/datasets/ConnorLuckettDSTG/SARFish)
- [sample SARFish dataset](https://huggingface.co/datasets/ConnorLuckettDSTG/SARFishSample)
#### Full SARFish dataset
Make sure you have at least enough storage space for the uncompressed dataset.
```bash
cd /path/to/large/storage/location
```
[Create|login] to a [huggingface](https://huggingface.co) account.
Login to the huggingface command line interface.
```bash
huggingface-cli login
```
Copy the access token in settings/Access Tokens from your huggingface account. Clone the dataset
```bash
git lfs install
git clone https://huggingface.co/datasets/ConnorLuckettDSTG/SARFish
```
#### SARFish sample dataset
Substitute the final command for the full dataset with the following:
```bash
git clone https://huggingface.co/datasets/ConnorLuckettDSTG/SARFishSample
```
Follow the instructions of the github repo README to check the md5sums of the data and unzip them.
#### Labels
The SARFish dataset labels are derived from the labels supplied with the [xView-3 SAR dataset](https://iuu.xview.us/dataset). The SARFish dataset labels are available for download from the [DIU website](https://iuu.xview.us/download-links). Be sure to take into account country restrictions.
### Data
SARFish extends the xView3-SAR dataset by providing products from the [Sentinel-1 C-band SAR satellite constellation](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) operated by the European Space Agency’s (ESA) Copernicus Programme available on their [Open Access Hub Website](https://scihub.copernicus.eu/) in both real-valued GRD and complex-valued SLC product types.
The above image shows a condensed summary of the image formation pipeline of the Sentinel-1 products provided by the Sentinel-1 Mission Performance Center. Note that the SLC and GRD products both share a common ancestor.
The above image shows the relationship between the xView3-SAR and SARFish datasets.
#### Summary table
The following table compares the GRD and SLC products of the SARFish dataset [3][4]
| | | |
| --- | --- | --- |
| Platform | Sentinel-1 (A, B) | |
| Operator | European Space Agency (ESA) Sentinel-1 Mission Performance Center | |
| Sensor | CBand SAR | |
| Mode | Interferometric Wide Swath (IW) | |
| Polarisations | VV, VH | |
| Ground range coverage (km) | 251.8 | |
| Product type | SLC | GRD |
| Pixel value | Complex | Magnitude Detected |
| Data type | Complex Int16 | Unsigned Int16 |
| Azimuth pixel spacing (m) | 2.3 | 10 |
| Range pixel spacing (m) | 14.1 | 10 |
#### Ground Range Detected (GRD) Products
GRD products consist of two 'detected' imagery products in VH, VV polarisations. The imagery data is stored in GeoTiff format. Also included in the dataset are no_data masks and shoreline files which are used to evaluate 'close-to-shore' maritime object detection tasks.
#### Single Look Complex (SLC) Products
The figures above show the 'swaths' comprising a SARFish SLC product in VH polarisation with groundtruth maritime object. labels The complex data has been 'detected' [3] by projecting the complex-valued data onto the real numbers for visualisation and displayed on decibel scale where the dynamic range is between 15 and 60 dB. Note that the SLC products have non-square (x, y): 2.3 × 14.1 m pixel spacing. The native format of the data is Complex Int16.
The figure above shows the footprint of the first swath of the example SLC product in context. The footprint was plotted using Clyde D'Cruz' ["openstreetmap WKT playground"](https://clydedacruz.github.io/openstreetmap-wkt-playground/).
The above images show detail of a labelled vessel in a SLC product in both VH (above) and VV (below) polarisations. Note the differences in the speckle and side-lobing artefacts on the vessel between polarisations and the non-square pixel spacing.
### Labels
#### Location labels
The labels denote the image pixel and geographic coordinate location of the maritime object.
| field | data_type | description |
| --------- | ----------- | --------- |
| detect\_lat | float | latitude of detection in World Geodetic System (WGS) 84 coordinates |
| detect\_lon | float | longitude of detection in WGS84 coordinates |
| detect\_scene\_row | int | pixel row of scene containing detection |
| detect\_scene\_column | int | pixel column of scene containing detection |
#### Classification Labels
The labels for the maritime object classification are organised in the same hierarchical structure as the xView3-SAR challenge labels:
```bash
label_heirarchy:
└── maritime_objects
└── vessels
└── fishing_vessels
```
They are denoted by the following columns in the labels:
| field | data_type | description |
| --------- | ----------- | --------- |
| is\_vessel | bool | True if detection is a vessel, False otherwise |
| is\_fishing | bool | True if detection is a fishing vessel, False otherwise |
The maritime object categories are labelled using boolean values to the following questions:
- is the maritime object a vessel?
- is the vessel a fishing vessel?
The following table shows the combinations of hierarchical classification labels present in the SARFish dataset:
| is\_vessel | is\_fishing |
|------------:|-------------:|
| False | nan |
| True | nan |
| | False |
| | True |
| nan | nan |
#### Vessel Length Labels
The vessel lengths are denoted in the following column in the labels:
| field | data_type | description |
| --------- | ----------- | --------- |
| vessel\_length\_m | float | length of vessel in meters; only provided where available from AIS |
#### Detailed labels summary
| field | data_type | description |
| --------- | ----------- | --------- |
| partition | str: \{"train", "validation"\} | split of the dataset |
| product\_type | str: \{"GRD", "SLC"\} | product type of the data |
| scene\_id | str | unique xView3 scene ID for challenge purposes |
| detect\_id | str | unique detection ID in the format: {scene\_id}\_{detect\_lat}\_{detect\_lon} |
| \{product\_type\}\_product\_identifier | str | The Copernicus Sentinel-1 product identifier for the designated product type |
| detect\_lat | float | latitude of detection in World Geodetic System (WGS) 84 coordinates |
| detect\_lon | float | longitude of detection in WGS84 coordinates |
| detect\_scene\_row | int | pixel row of scene containing detection |
| detect\_scene\_column | int | pixel column of scene containing detection |
| top | float | pixel row of the top left corner of the bounding box, where available |
| left | float | pixel column of the top left corner of the bounding box, where available |
| bottom | float | pixel row of the bottom right corner of the bounding box, where available |
| right | float | pixel column of the bottom right corner of the bounding box, where available |
| vessel\_length\_m | float | length of vessel in meters; only provided where available from AIS |
| source | str: \{AIS, AIS/Manual, Manual\} | source of detection (AIS, manual label, or both) |
| is\_vessel | bool | True if detection is a vessel, False otherwise |
| is\_fishing | bool | True if detection is a fishing vessel, False otherwise |
| global\_shoreline\_vector\_distance\_from\_shore\_km | float | distance from shore of detection in kilometers as determined using the global shoreline vectors projected into the pixel space of the SARFish products |
| xView3\_shoreline\_vector\_distance\_from\_shore\_km | float | distance from shore of detection in kilometers as determined using the xView3-SAR shoreline vectors projected into the pixel space of the SARFish products |
| confidence | str: \{HIGH, MEDIUM, LOW\} | level of confidence for is\_vessel and is\_fishing labels |
### Source
The Sentinel-1 GRD and SLC products were downloaded the University of Alaska's Alaska Satellite Facillity (ASF) which operates NASA's Distributed Active Archive Center (DAAC).
- [website](https://asf.alaska.edu/)
- [registration](https://urs.earthdata.nasa.gov/users/new)
- [download](https://datapool.asf.alaska.edu/)
- API docs
- [basics](https://docs.asf.alaska.edu/api/basics/)
- [keywords](https://docs.asf.alaska.edu/api/keywords/)
- [tools](https://docs.asf.alaska.edu/api/tools/)
[1]. Tri-Tan Cao, Connor Luckett, Jerome Williams, Tristrom Cooke, Ben Yip, Arvind Rajagopalan, and Sebastien Wong. Sarfish: Space-based maritime surveillance using complex synthetic aperture radar imagery. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2022.
[2] xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery. arXiv:2206.00897v4 [cs.CV], Nov 2022.
[3] M. Bourbigot, H. Johnsen, R. Piantanida, and G. Hajduch, Sentinel-1 Product Definition. Sentinel-1 Mission Performance Centre, 2016. [Online]. Available: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/document-library/-/asset_publisher/1dO7RF5fJMbd/content/sentinel-1-product-definition
[4] S. N. R. Chandra, J. Christopherson, and K. A. Casey, 2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium. US Geological Survey, 2020.
|
ConnorLuckettDSTG/SARFish
|
[
"task_categories:object-detection",
"task_categories:image-classification",
"size_categories:n<1K",
"license:apache-2.0",
"SARFish",
"Illegal Fishing",
"Computer Vision",
"Complex-Valued",
"Synthetic Aperture Radar",
"region:us"
] |
2023-09-13T21:41:57+00:00
|
{"license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["object-detection", "image-classification"], "pretty_name": "SARFish Dataset", "tags": ["SARFish", "Illegal Fishing", "Computer Vision", "Complex-Valued", "Synthetic Aperture Radar"]}
|
2024-01-07T01:16:18+00:00
|
[] |
[] |
TAGS
#task_categories-object-detection #task_categories-image-classification #size_categories-n<1K #license-apache-2.0 #SARFish #Illegal Fishing #Computer Vision #Complex-Valued #Synthetic Aperture Radar #region-us
|
SARFish is a Synthetic Aperture Radar (SAR) imagery dataset for the purpose of training, validating and testing supervised machine learning models on the tasks of ship detection, classification, and length regression. The SARFish dataset builds on the excellent work of the xView3-SAR dataset (2021) and consists of two parts:
1. Data - Extends the xView3-SAR dataset to include Single Look Complex (SLC) as well as Ground Range Detected (GRD) imagery data taken directly from the European Space Agency (ESA) Copernicus Programme Open Access Hub Website.
2. Labels - Derives labels from the xView3-SAR dataset providing maritime object location, vessel classification and vessel length information.
### Quick Links
The following are links to the Kaggle competitions for each of the tracks of the SARFish challenge along with the SARFish dataset and GitHub repo:
* Data:
+ SARFish
+ SARFishSample
* Labels
* Challenge:
+ Maritime Object Detection Track
+ Maritime Object Classification Track
+ Vessel Length Regression Track
* GitHub repo
* Mailbox
* DAIRNet
The GitHub repo describes how to:
* Download the dataset.
* Run the SARFish\_demo jupyter notebook.
* Load imagery products and groundtruth labels,
* Train and evaluate a reference/baseline model using the dataset.
### Dataset summary - What does the SARFish dataset consist of?
The following table summarises the sizes of the full size and sample SARFish dataset.
The following table summarises the partitions of the dataset:
### How to access the SARFish dataset
The SARFish dataset is available for download at:
* full SARFish dataset
* sample SARFish dataset
#### Full SARFish dataset
Make sure you have at least enough storage space for the uncompressed dataset.
[Create|login] to a huggingface account.
Login to the huggingface command line interface.
Copy the access token in settings/Access Tokens from your huggingface account. Clone the dataset
#### SARFish sample dataset
Substitute the final command for the full dataset with the following:
Follow the instructions of the github repo README to check the md5sums of the data and unzip them.
#### Labels
The SARFish dataset labels are derived from the labels supplied with the xView-3 SAR dataset. The SARFish dataset labels are available for download from the DIU website. Be sure to take into account country restrictions.
### Data
SARFish extends the xView3-SAR dataset by providing products from the Sentinel-1 C-band SAR satellite constellation operated by the European Space Agency’s (ESA) Copernicus Programme available on their Open Access Hub Website in both real-valued GRD and complex-valued SLC product types.
 Products
GRD products consist of two 'detected' imagery products in VH, VV polarisations. The imagery data is stored in GeoTiff format. Also included in the dataset are no\_data masks and shoreline files which are used to evaluate 'close-to-shore' maritime object detection tasks.
#### Single Look Complex (SLC) Products
!SARFish Single Look Complex (SLC) example swath 1
!SARFish Single Look Complex (SLC) example swath 2
!SARFish Single Look Complex (SLC) example swath 3
The figures above show the 'swaths' comprising a SARFish SLC product in VH polarisation with groundtruth maritime object. labels The complex data has been 'detected' [3] by projecting the complex-valued data onto the real numbers for visualisation and displayed on decibel scale where the dynamic range is between 15 and 60 dB. Note that the SLC products have non-square (x, y): 2.3 × 14.1 m pixel spacing. The native format of the data is Complex Int16.
!SARFish SLC footprint
The figure above shows the footprint of the first swath of the example SLC product in context. The footprint was plotted using Clyde D'Cruz' "openstreetmap WKT playground".
!SARFish SLC VH polarisation ship example
!SARFish SLC VV polarisation ship example
The above images show detail of a labelled vessel in a SLC product in both VH (above) and VV (below) polarisations. Note the differences in the speckle and side-lobing artefacts on the vessel between polarisations and the non-square pixel spacing.
### Labels
#### Location labels
The labels denote the image pixel and geographic coordinate location of the maritime object.
field: detect\_lat, data\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates
field: detect\_lon, data\_type: float, description: longitude of detection in WGS84 coordinates
field: detect\_scene\_row, data\_type: int, description: pixel row of scene containing detection
field: detect\_scene\_column, data\_type: int, description: pixel column of scene containing detection
#### Classification Labels
The labels for the maritime object classification are organised in the same hierarchical structure as the xView3-SAR challenge labels:
They are denoted by the following columns in the labels:
field: is\_vessel, data\_type: bool, description: True if detection is a vessel, False otherwise
field: is\_fishing, data\_type: bool, description: True if detection is a fishing vessel, False otherwise
The maritime object categories are labelled using boolean values to the following questions:
* is the maritime object a vessel?
* is the vessel a fishing vessel?
The following table shows the combinations of hierarchical classification labels present in the SARFish dataset:
#### Vessel Length Labels
The vessel lengths are denoted in the following column in the labels:
field: vessel\_length\_m, data\_type: float, description: length of vessel in meters; only provided where available from AIS
#### Detailed labels summary
field: partition, data\_type: str: {"train", "validation"}, description: split of the dataset
field: product\_type, data\_type: str: {"GRD", "SLC"}, description: product type of the data
field: scene\_id, data\_type: str, description: unique xView3 scene ID for challenge purposes
field: detect\_id, data\_type: str, description: unique detection ID in the format: {scene\_id}\_{detect\_lat}\_{detect\_lon}
field: {product\_type}\_product\_identifier, data\_type: str, description: The Copernicus Sentinel-1 product identifier for the designated product type
field: detect\_lat, data\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates
field: detect\_lon, data\_type: float, description: longitude of detection in WGS84 coordinates
field: detect\_scene\_row, data\_type: int, description: pixel row of scene containing detection
field: detect\_scene\_column, data\_type: int, description: pixel column of scene containing detection
field: top, data\_type: float, description: pixel row of the top left corner of the bounding box, where available
field: left, data\_type: float, description: pixel column of the top left corner of the bounding box, where available
field: bottom, data\_type: float, description: pixel row of the bottom right corner of the bounding box, where available
field: right, data\_type: float, description: pixel column of the bottom right corner of the bounding box, where available
field: vessel\_length\_m, data\_type: float, description: length of vessel in meters; only provided where available from AIS
field: source, data\_type: str: {AIS, AIS/Manual, Manual}, description: source of detection (AIS, manual label, or both)
field: is\_vessel, data\_type: bool, description: True if detection is a vessel, False otherwise
field: is\_fishing, data\_type: bool, description: True if detection is a fishing vessel, False otherwise
field: global\_shoreline\_vector\_distance\_from\_shore\_km, data\_type: float, description: distance from shore of detection in kilometers as determined using the global shoreline vectors projected into the pixel space of the SARFish products
field: xView3\_shoreline\_vector\_distance\_from\_shore\_km, data\_type: float, description: distance from shore of detection in kilometers as determined using the xView3-SAR shoreline vectors projected into the pixel space of the SARFish products
field: confidence, data\_type: str: {HIGH, MEDIUM, LOW}, description: level of confidence for is\_vessel and is\_fishing labels
### Source
The Sentinel-1 GRD and SLC products were downloaded the University of Alaska's Alaska Satellite Facillity (ASF) which operates NASA's Distributed Active Archive Center (DAAC).
* website
* registration
* download
* API docs
+ basics
+ keywords
+ tools
[1]. Tri-Tan Cao, Connor Luckett, Jerome Williams, Tristrom Cooke, Ben Yip, Arvind Rajagopalan, and Sebastien Wong. Sarfish: Space-based maritime surveillance using complex synthetic aperture radar imagery. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2022.
[2] xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery. arXiv:2206.00897v4 [cs.CV], Nov 2022.
[3] M. Bourbigot, H. Johnsen, R. Piantanida, and G. Hajduch, Sentinel-1 Product Definition. Sentinel-1 Mission Performance Centre, 2016. [Online]. Available: URL
[4] S. N. R. Chandra, J. Christopherson, and K. A. Casey, 2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium. US Geological Survey, 2020.
|
[
"### Quick Links\n\n\nThe following are links to the Kaggle competitions for each of the tracks of the SARFish challenge along with the SARFish dataset and GitHub repo:\n\n\n* Data:\n\t+ SARFish\n\t+ SARFishSample\n* Labels\n* Challenge:\n\t+ Maritime Object Detection Track\n\t+ Maritime Object Classification Track\n\t+ Vessel Length Regression Track\n* GitHub repo\n* Mailbox\n* DAIRNet\n\n\nThe GitHub repo describes how to:\n\n\n* Download the dataset.\n* Run the SARFish\\_demo jupyter notebook.\n* Load imagery products and groundtruth labels,\n* Train and evaluate a reference/baseline model using the dataset.",
"### Dataset summary - What does the SARFish dataset consist of?\n\n\nThe following table summarises the sizes of the full size and sample SARFish dataset.\n\n\n\nThe following table summarises the partitions of the dataset:",
"### How to access the SARFish dataset\n\n\nThe SARFish dataset is available for download at:\n\n\n* full SARFish dataset\n* sample SARFish dataset",
"#### Full SARFish dataset\n\n\nMake sure you have at least enough storage space for the uncompressed dataset.\n\n\n[Create|login] to a huggingface account.\n\n\nLogin to the huggingface command line interface.\n\n\nCopy the access token in settings/Access Tokens from your huggingface account. Clone the dataset",
"#### SARFish sample dataset\n\n\nSubstitute the final command for the full dataset with the following:\n\n\nFollow the instructions of the github repo README to check the md5sums of the data and unzip them.",
"#### Labels\n\n\nThe SARFish dataset labels are derived from the labels supplied with the xView-3 SAR dataset. The SARFish dataset labels are available for download from the DIU website. Be sure to take into account country restrictions.",
"### Data\n\n\nSARFish extends the xView3-SAR dataset by providing products from the Sentinel-1 C-band SAR satellite constellation operated by the European Space Agency’s (ESA) Copernicus Programme available on their Open Access Hub Website in both real-valued GRD and complex-valued SLC product types.\n\n\n![](URL\n\n\nThe above image shows a condensed summary of the image formation pipeline of the Sentinel-1 products provided by the Sentinel-1 Mission Performance Center. Note that the SLC and GRD products both share a common ancestor.\n\n\n![](URL\n\n\nThe above image shows the relationship between the xView3-SAR and SARFish datasets.",
"#### Summary table\n\n\nThe following table compares the GRD and SLC products of the SARFish dataset [3][4]",
"#### Ground Range Detected (GRD) Products\n\n\nGRD products consist of two 'detected' imagery products in VH, VV polarisations. The imagery data is stored in GeoTiff format. Also included in the dataset are no\\_data masks and shoreline files which are used to evaluate 'close-to-shore' maritime object detection tasks.",
"#### Single Look Complex (SLC) Products\n\n\n!SARFish Single Look Complex (SLC) example swath 1\n\n\n!SARFish Single Look Complex (SLC) example swath 2\n\n\n!SARFish Single Look Complex (SLC) example swath 3\n\n\nThe figures above show the 'swaths' comprising a SARFish SLC product in VH polarisation with groundtruth maritime object. labels The complex data has been 'detected' [3] by projecting the complex-valued data onto the real numbers for visualisation and displayed on decibel scale where the dynamic range is between 15 and 60 dB. Note that the SLC products have non-square (x, y): 2.3 × 14.1 m pixel spacing. The native format of the data is Complex Int16.\n\n\n!SARFish SLC footprint\n\n\nThe figure above shows the footprint of the first swath of the example SLC product in context. The footprint was plotted using Clyde D'Cruz' \"openstreetmap WKT playground\".\n\n\n!SARFish SLC VH polarisation ship example\n\n\n!SARFish SLC VV polarisation ship example\n\n\nThe above images show detail of a labelled vessel in a SLC product in both VH (above) and VV (below) polarisations. Note the differences in the speckle and side-lobing artefacts on the vessel between polarisations and the non-square pixel spacing.",
"### Labels",
"#### Location labels\n\n\nThe labels denote the image pixel and geographic coordinate location of the maritime object.\n\n\nfield: detect\\_lat, data\\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates\nfield: detect\\_lon, data\\_type: float, description: longitude of detection in WGS84 coordinates\nfield: detect\\_scene\\_row, data\\_type: int, description: pixel row of scene containing detection\nfield: detect\\_scene\\_column, data\\_type: int, description: pixel column of scene containing detection",
"#### Classification Labels\n\n\nThe labels for the maritime object classification are organised in the same hierarchical structure as the xView3-SAR challenge labels:\n\n\nThey are denoted by the following columns in the labels:\n\n\nfield: is\\_vessel, data\\_type: bool, description: True if detection is a vessel, False otherwise\nfield: is\\_fishing, data\\_type: bool, description: True if detection is a fishing vessel, False otherwise\n\n\nThe maritime object categories are labelled using boolean values to the following questions:\n\n\n* is the maritime object a vessel?\n* is the vessel a fishing vessel?\n\n\nThe following table shows the combinations of hierarchical classification labels present in the SARFish dataset:",
"#### Vessel Length Labels\n\n\nThe vessel lengths are denoted in the following column in the labels:\n\n\nfield: vessel\\_length\\_m, data\\_type: float, description: length of vessel in meters; only provided where available from AIS",
"#### Detailed labels summary\n\n\nfield: partition, data\\_type: str: {\"train\", \"validation\"}, description: split of the dataset\nfield: product\\_type, data\\_type: str: {\"GRD\", \"SLC\"}, description: product type of the data\nfield: scene\\_id, data\\_type: str, description: unique xView3 scene ID for challenge purposes\nfield: detect\\_id, data\\_type: str, description: unique detection ID in the format: {scene\\_id}\\_{detect\\_lat}\\_{detect\\_lon}\nfield: {product\\_type}\\_product\\_identifier, data\\_type: str, description: The Copernicus Sentinel-1 product identifier for the designated product type\nfield: detect\\_lat, data\\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates\nfield: detect\\_lon, data\\_type: float, description: longitude of detection in WGS84 coordinates\nfield: detect\\_scene\\_row, data\\_type: int, description: pixel row of scene containing detection\nfield: detect\\_scene\\_column, data\\_type: int, description: pixel column of scene containing detection\nfield: top, data\\_type: float, description: pixel row of the top left corner of the bounding box, where available\nfield: left, data\\_type: float, description: pixel column of the top left corner of the bounding box, where available\nfield: bottom, data\\_type: float, description: pixel row of the bottom right corner of the bounding box, where available\nfield: right, data\\_type: float, description: pixel column of the bottom right corner of the bounding box, where available\nfield: vessel\\_length\\_m, data\\_type: float, description: length of vessel in meters; only provided where available from AIS\nfield: source, data\\_type: str: {AIS, AIS/Manual, Manual}, description: source of detection (AIS, manual label, or both)\nfield: is\\_vessel, data\\_type: bool, description: True if detection is a vessel, False otherwise\nfield: is\\_fishing, data\\_type: bool, description: True if detection is a fishing vessel, False otherwise\nfield: global\\_shoreline\\_vector\\_distance\\_from\\_shore\\_km, data\\_type: float, description: distance from shore of detection in kilometers as determined using the global shoreline vectors projected into the pixel space of the SARFish products\nfield: xView3\\_shoreline\\_vector\\_distance\\_from\\_shore\\_km, data\\_type: float, description: distance from shore of detection in kilometers as determined using the xView3-SAR shoreline vectors projected into the pixel space of the SARFish products\nfield: confidence, data\\_type: str: {HIGH, MEDIUM, LOW}, description: level of confidence for is\\_vessel and is\\_fishing labels",
"### Source\n\n\nThe Sentinel-1 GRD and SLC products were downloaded the University of Alaska's Alaska Satellite Facillity (ASF) which operates NASA's Distributed Active Archive Center (DAAC).\n\n\n* website\n* registration\n* download\n* API docs\n\t+ basics\n\t+ keywords\n\t+ tools\n\n\n[1]. Tri-Tan Cao, Connor Luckett, Jerome Williams, Tristrom Cooke, Ben Yip, Arvind Rajagopalan, and Sebastien Wong. Sarfish: Space-based maritime surveillance using complex synthetic aperture radar imagery. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2022.\n\n\n[2] xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery. arXiv:2206.00897v4 [cs.CV], Nov 2022.\n\n\n[3] M. Bourbigot, H. Johnsen, R. Piantanida, and G. Hajduch, Sentinel-1 Product Definition. Sentinel-1 Mission Performance Centre, 2016. [Online]. Available: URL\n\n\n[4] S. N. R. Chandra, J. Christopherson, and K. A. Casey, 2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium. US Geological Survey, 2020."
] |
[
"TAGS\n#task_categories-object-detection #task_categories-image-classification #size_categories-n<1K #license-apache-2.0 #SARFish #Illegal Fishing #Computer Vision #Complex-Valued #Synthetic Aperture Radar #region-us \n",
"### Quick Links\n\n\nThe following are links to the Kaggle competitions for each of the tracks of the SARFish challenge along with the SARFish dataset and GitHub repo:\n\n\n* Data:\n\t+ SARFish\n\t+ SARFishSample\n* Labels\n* Challenge:\n\t+ Maritime Object Detection Track\n\t+ Maritime Object Classification Track\n\t+ Vessel Length Regression Track\n* GitHub repo\n* Mailbox\n* DAIRNet\n\n\nThe GitHub repo describes how to:\n\n\n* Download the dataset.\n* Run the SARFish\\_demo jupyter notebook.\n* Load imagery products and groundtruth labels,\n* Train and evaluate a reference/baseline model using the dataset.",
"### Dataset summary - What does the SARFish dataset consist of?\n\n\nThe following table summarises the sizes of the full size and sample SARFish dataset.\n\n\n\nThe following table summarises the partitions of the dataset:",
"### How to access the SARFish dataset\n\n\nThe SARFish dataset is available for download at:\n\n\n* full SARFish dataset\n* sample SARFish dataset",
"#### Full SARFish dataset\n\n\nMake sure you have at least enough storage space for the uncompressed dataset.\n\n\n[Create|login] to a huggingface account.\n\n\nLogin to the huggingface command line interface.\n\n\nCopy the access token in settings/Access Tokens from your huggingface account. Clone the dataset",
"#### SARFish sample dataset\n\n\nSubstitute the final command for the full dataset with the following:\n\n\nFollow the instructions of the github repo README to check the md5sums of the data and unzip them.",
"#### Labels\n\n\nThe SARFish dataset labels are derived from the labels supplied with the xView-3 SAR dataset. The SARFish dataset labels are available for download from the DIU website. Be sure to take into account country restrictions.",
"### Data\n\n\nSARFish extends the xView3-SAR dataset by providing products from the Sentinel-1 C-band SAR satellite constellation operated by the European Space Agency’s (ESA) Copernicus Programme available on their Open Access Hub Website in both real-valued GRD and complex-valued SLC product types.\n\n\n![](URL\n\n\nThe above image shows a condensed summary of the image formation pipeline of the Sentinel-1 products provided by the Sentinel-1 Mission Performance Center. Note that the SLC and GRD products both share a common ancestor.\n\n\n![](URL\n\n\nThe above image shows the relationship between the xView3-SAR and SARFish datasets.",
"#### Summary table\n\n\nThe following table compares the GRD and SLC products of the SARFish dataset [3][4]",
"#### Ground Range Detected (GRD) Products\n\n\nGRD products consist of two 'detected' imagery products in VH, VV polarisations. The imagery data is stored in GeoTiff format. Also included in the dataset are no\\_data masks and shoreline files which are used to evaluate 'close-to-shore' maritime object detection tasks.",
"#### Single Look Complex (SLC) Products\n\n\n!SARFish Single Look Complex (SLC) example swath 1\n\n\n!SARFish Single Look Complex (SLC) example swath 2\n\n\n!SARFish Single Look Complex (SLC) example swath 3\n\n\nThe figures above show the 'swaths' comprising a SARFish SLC product in VH polarisation with groundtruth maritime object. labels The complex data has been 'detected' [3] by projecting the complex-valued data onto the real numbers for visualisation and displayed on decibel scale where the dynamic range is between 15 and 60 dB. Note that the SLC products have non-square (x, y): 2.3 × 14.1 m pixel spacing. The native format of the data is Complex Int16.\n\n\n!SARFish SLC footprint\n\n\nThe figure above shows the footprint of the first swath of the example SLC product in context. The footprint was plotted using Clyde D'Cruz' \"openstreetmap WKT playground\".\n\n\n!SARFish SLC VH polarisation ship example\n\n\n!SARFish SLC VV polarisation ship example\n\n\nThe above images show detail of a labelled vessel in a SLC product in both VH (above) and VV (below) polarisations. Note the differences in the speckle and side-lobing artefacts on the vessel between polarisations and the non-square pixel spacing.",
"### Labels",
"#### Location labels\n\n\nThe labels denote the image pixel and geographic coordinate location of the maritime object.\n\n\nfield: detect\\_lat, data\\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates\nfield: detect\\_lon, data\\_type: float, description: longitude of detection in WGS84 coordinates\nfield: detect\\_scene\\_row, data\\_type: int, description: pixel row of scene containing detection\nfield: detect\\_scene\\_column, data\\_type: int, description: pixel column of scene containing detection",
"#### Classification Labels\n\n\nThe labels for the maritime object classification are organised in the same hierarchical structure as the xView3-SAR challenge labels:\n\n\nThey are denoted by the following columns in the labels:\n\n\nfield: is\\_vessel, data\\_type: bool, description: True if detection is a vessel, False otherwise\nfield: is\\_fishing, data\\_type: bool, description: True if detection is a fishing vessel, False otherwise\n\n\nThe maritime object categories are labelled using boolean values to the following questions:\n\n\n* is the maritime object a vessel?\n* is the vessel a fishing vessel?\n\n\nThe following table shows the combinations of hierarchical classification labels present in the SARFish dataset:",
"#### Vessel Length Labels\n\n\nThe vessel lengths are denoted in the following column in the labels:\n\n\nfield: vessel\\_length\\_m, data\\_type: float, description: length of vessel in meters; only provided where available from AIS",
"#### Detailed labels summary\n\n\nfield: partition, data\\_type: str: {\"train\", \"validation\"}, description: split of the dataset\nfield: product\\_type, data\\_type: str: {\"GRD\", \"SLC\"}, description: product type of the data\nfield: scene\\_id, data\\_type: str, description: unique xView3 scene ID for challenge purposes\nfield: detect\\_id, data\\_type: str, description: unique detection ID in the format: {scene\\_id}\\_{detect\\_lat}\\_{detect\\_lon}\nfield: {product\\_type}\\_product\\_identifier, data\\_type: str, description: The Copernicus Sentinel-1 product identifier for the designated product type\nfield: detect\\_lat, data\\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates\nfield: detect\\_lon, data\\_type: float, description: longitude of detection in WGS84 coordinates\nfield: detect\\_scene\\_row, data\\_type: int, description: pixel row of scene containing detection\nfield: detect\\_scene\\_column, data\\_type: int, description: pixel column of scene containing detection\nfield: top, data\\_type: float, description: pixel row of the top left corner of the bounding box, where available\nfield: left, data\\_type: float, description: pixel column of the top left corner of the bounding box, where available\nfield: bottom, data\\_type: float, description: pixel row of the bottom right corner of the bounding box, where available\nfield: right, data\\_type: float, description: pixel column of the bottom right corner of the bounding box, where available\nfield: vessel\\_length\\_m, data\\_type: float, description: length of vessel in meters; only provided where available from AIS\nfield: source, data\\_type: str: {AIS, AIS/Manual, Manual}, description: source of detection (AIS, manual label, or both)\nfield: is\\_vessel, data\\_type: bool, description: True if detection is a vessel, False otherwise\nfield: is\\_fishing, data\\_type: bool, description: True if detection is a fishing vessel, False otherwise\nfield: global\\_shoreline\\_vector\\_distance\\_from\\_shore\\_km, data\\_type: float, description: distance from shore of detection in kilometers as determined using the global shoreline vectors projected into the pixel space of the SARFish products\nfield: xView3\\_shoreline\\_vector\\_distance\\_from\\_shore\\_km, data\\_type: float, description: distance from shore of detection in kilometers as determined using the xView3-SAR shoreline vectors projected into the pixel space of the SARFish products\nfield: confidence, data\\_type: str: {HIGH, MEDIUM, LOW}, description: level of confidence for is\\_vessel and is\\_fishing labels",
"### Source\n\n\nThe Sentinel-1 GRD and SLC products were downloaded the University of Alaska's Alaska Satellite Facillity (ASF) which operates NASA's Distributed Active Archive Center (DAAC).\n\n\n* website\n* registration\n* download\n* API docs\n\t+ basics\n\t+ keywords\n\t+ tools\n\n\n[1]. Tri-Tan Cao, Connor Luckett, Jerome Williams, Tristrom Cooke, Ben Yip, Arvind Rajagopalan, and Sebastien Wong. Sarfish: Space-based maritime surveillance using complex synthetic aperture radar imagery. In 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pages 1–8. IEEE, 2022.\n\n\n[2] xview3-sar: Detecting dark fishing activity using synthetic aperture radar imagery. arXiv:2206.00897v4 [cs.CV], Nov 2022.\n\n\n[3] M. Bourbigot, H. Johnsen, R. Piantanida, and G. Hajduch, Sentinel-1 Product Definition. Sentinel-1 Mission Performance Centre, 2016. [Online]. Available: URL\n\n\n[4] S. N. R. Chandra, J. Christopherson, and K. A. Casey, 2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium. US Geological Survey, 2020."
] |
[
76,
151,
52,
37,
75,
49,
57,
152,
26,
89,
326,
3,
151,
177,
64,
755,
295
] |
[
"passage: TAGS\n#task_categories-object-detection #task_categories-image-classification #size_categories-n<1K #license-apache-2.0 #SARFish #Illegal Fishing #Computer Vision #Complex-Valued #Synthetic Aperture Radar #region-us \n### Quick Links\n\n\nThe following are links to the Kaggle competitions for each of the tracks of the SARFish challenge along with the SARFish dataset and GitHub repo:\n\n\n* Data:\n\t+ SARFish\n\t+ SARFishSample\n* Labels\n* Challenge:\n\t+ Maritime Object Detection Track\n\t+ Maritime Object Classification Track\n\t+ Vessel Length Regression Track\n* GitHub repo\n* Mailbox\n* DAIRNet\n\n\nThe GitHub repo describes how to:\n\n\n* Download the dataset.\n* Run the SARFish\\_demo jupyter notebook.\n* Load imagery products and groundtruth labels,\n* Train and evaluate a reference/baseline model using the dataset.### Dataset summary - What does the SARFish dataset consist of?\n\n\nThe following table summarises the sizes of the full size and sample SARFish dataset.\n\n\n\nThe following table summarises the partitions of the dataset:### How to access the SARFish dataset\n\n\nThe SARFish dataset is available for download at:\n\n\n* full SARFish dataset\n* sample SARFish dataset#### Full SARFish dataset\n\n\nMake sure you have at least enough storage space for the uncompressed dataset.\n\n\n[Create|login] to a huggingface account.\n\n\nLogin to the huggingface command line interface.\n\n\nCopy the access token in settings/Access Tokens from your huggingface account. Clone the dataset#### SARFish sample dataset\n\n\nSubstitute the final command for the full dataset with the following:\n\n\nFollow the instructions of the github repo README to check the md5sums of the data and unzip them.#### Labels\n\n\nThe SARFish dataset labels are derived from the labels supplied with the xView-3 SAR dataset. The SARFish dataset labels are available for download from the DIU website. Be sure to take into account country restrictions.",
"passage: ### Data\n\n\nSARFish extends the xView3-SAR dataset by providing products from the Sentinel-1 C-band SAR satellite constellation operated by the European Space Agency’s (ESA) Copernicus Programme available on their Open Access Hub Website in both real-valued GRD and complex-valued SLC product types.\n\n\n Products\n\n\nGRD products consist of two 'detected' imagery products in VH, VV polarisations. The imagery data is stored in GeoTiff format. Also included in the dataset are no\\_data masks and shoreline files which are used to evaluate 'close-to-shore' maritime object detection tasks.#### Single Look Complex (SLC) Products\n\n\n!SARFish Single Look Complex (SLC) example swath 1\n\n\n!SARFish Single Look Complex (SLC) example swath 2\n\n\n!SARFish Single Look Complex (SLC) example swath 3\n\n\nThe figures above show the 'swaths' comprising a SARFish SLC product in VH polarisation with groundtruth maritime object. labels The complex data has been 'detected' [3] by projecting the complex-valued data onto the real numbers for visualisation and displayed on decibel scale where the dynamic range is between 15 and 60 dB. Note that the SLC products have non-square (x, y): 2.3 × 14.1 m pixel spacing. The native format of the data is Complex Int16.\n\n\n!SARFish SLC footprint\n\n\nThe figure above shows the footprint of the first swath of the example SLC product in context. The footprint was plotted using Clyde D'Cruz' \"openstreetmap WKT playground\".\n\n\n!SARFish SLC VH polarisation ship example\n\n\n!SARFish SLC VV polarisation ship example\n\n\nThe above images show detail of a labelled vessel in a SLC product in both VH (above) and VV (below) polarisations. Note the differences in the speckle and side-lobing artefacts on the vessel between polarisations and the non-square pixel spacing.### Labels",
"passage: #### Location labels\n\n\nThe labels denote the image pixel and geographic coordinate location of the maritime object.\n\n\nfield: detect\\_lat, data\\_type: float, description: latitude of detection in World Geodetic System (WGS) 84 coordinates\nfield: detect\\_lon, data\\_type: float, description: longitude of detection in WGS84 coordinates\nfield: detect\\_scene\\_row, data\\_type: int, description: pixel row of scene containing detection\nfield: detect\\_scene\\_column, data\\_type: int, description: pixel column of scene containing detection#### Classification Labels\n\n\nThe labels for the maritime object classification are organised in the same hierarchical structure as the xView3-SAR challenge labels:\n\n\nThey are denoted by the following columns in the labels:\n\n\nfield: is\\_vessel, data\\_type: bool, description: True if detection is a vessel, False otherwise\nfield: is\\_fishing, data\\_type: bool, description: True if detection is a fishing vessel, False otherwise\n\n\nThe maritime object categories are labelled using boolean values to the following questions:\n\n\n* is the maritime object a vessel?\n* is the vessel a fishing vessel?\n\n\nThe following table shows the combinations of hierarchical classification labels present in the SARFish dataset:#### Vessel Length Labels\n\n\nThe vessel lengths are denoted in the following column in the labels:\n\n\nfield: vessel\\_length\\_m, data\\_type: float, description: length of vessel in meters; only provided where available from AIS"
] |
3fe0ff8aaf5acf46967cff7af83244113cfd6710
|
# Dataset Card for "filtered-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/filtered-1
|
[
"region:us"
] |
2023-09-13T21:45:00+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "conversation_id", "dtype": "int64"}, {"name": "dataset_id", "dtype": "string"}, {"name": "unique_conversation_id", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13560566066, "num_examples": 2297193}], "download_size": 13048058105, "dataset_size": 13560566066}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-13T22:38:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "filtered-2"
More Information needed
|
[
"# Dataset Card for \"filtered-2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"filtered-2\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"filtered-2\"\n\nMore Information needed"
] |
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